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Entropy analysis as a tool for optimal sustainable use of biorefineries
Kasra Samiei
This thesis comprises 30 ECTS credits and is a compulsory part in the Master of Science with a Major in Chemical Engineering – waste management and resource recovery technology
No. 2/2007
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Entropy analysis as a tool for optimal sustainable use of biorefineries Kasra Samiei Master's thesis Subject Category: Technology Series number: 2/2007 University College of Borås School of Engineering SE-501 90 BORÅS Telephone +46 (0)33 435 4640 Examiner: Mohammad Taherzadeh Supervisor: Morgan Fröling, Department of Chemical and Biological Engineering, Chalmers University of Technology Client: Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg Date: 16 November 2007 Keywords: biomass conversion, biorefinery, entropy, life cycle assessment, second law analysis, thermodynamic optimization
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To my parents for their love and support
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Abstract The biorefinery concept is attractive. Increasing international concerns over issues such as climate change have led to political as well as social pressures for a shift from fossil fuels to renewable resources and biomass is one abundant renewable resource. Biomass has the potential of supplying many of the fuels and chemicals which are currently dependent on petroleum. Much development is still needed in the field of biorefineries and a systematic approach to evaluate and compare process technologies and to suggest optimizations seems necessary. The objective of this thesis is to develop entropy analysis as a possible evaluation tool for optimization of biorefinery processes. This is a new application of entropy analysis which is rarely discussed in the literature. The scientific basis of the entropy analysis is described and the proposed methodology is explained. The position of entropy analysis among other system analysis tools such as exergy analysis and life cycle assessment is discussed along with entropy analysis earlier applications. A case study is introduced which is the IBUS (Integrated Biomass Utilization System) project in Denmark. The idea in IBUS is to integrate the biomass plant with a power plant to utilize the surplus steam from the power plant for the internal use of the biorefinery. The suggested method of entropy analysis is applied to this case study to compare different processes for production of ethanol along with solid biofuel and animal feed from Danish wheat straw. The evaluation is a gate to gate analysis in which production of energy carriers are also included in addition to biorefining of wheat straw. A parallel life cycle assessment study with equivalent system boundaries is also carried out to compare the results with a conventional environmental systems analysis method. The results from the entropy analysis of the IBUS case study show that fermentation of C5 and C6 sugars by yeast is the most efficient process thermodynamically while fermentation of only C6 sugars by yeast is the least efficient among the three cases studied. Integration of the biorefinery with a coal fired CHP plant is identified as a wise choice by the results of the entropy analysis method. For the IBUS process alternatives investigated in this study, the entropy results and the LCA results (aggregated environmental load) are in correlation; entropy results are consistent with weighting results based on two different weighting methods namely Eco indicator 99 and EPS 2000. Entropy generation is also in correlation with production cost for the processes analyzed in this evaluation. Another observation is that cooling in the biorefining process contributes highly in the generation of entropy. This potential improvement option is not surfaced by the LCA conducted. The potential for further investigation and development of the tool is recognized reflecting on some interesting observations in the results. Improvement of the tool is highly possible for example by supplementing other implications of entropy in process design such as "waste potential entropy" concept which is developed as an eco-toxicity measure.
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Contents
Abstract..……………………………………..……………………………..………….3
Contents……………………………………………………………...…....…………..4
Chapter 1. Biorefinery……………………………….…………………..…………...5 1.1 Introduction……………….………………………...………………………………….….5 1.2 A hot topic………………………………………………………………………..………...7 1.3 Biomass…………………………………... …………………….....…………...…………...8 1.4 Economy…………………………………...…………………………………..…………10 1.5 Existing biorefineries……………………..………..………………………….…………12 1.6 Biorefinery of tomorrow…………….………………………………….………….......14 1.7 Sustainability………………………….…………………………….……………….....15 1.8 Conclusions……………………………………………………………….………….…….………………....16
Chapter 2. Entropy analysis among other system analysis tools….............................................…........……….17 2.1 Introduction…………………………………………………………………………...……….……………….17 2.2 Flow based measures for eco-sustainability………………………………………….……………………..17 2.3 Thermodynamic background………………………………………………………….21 2.4 Second law analysis…………………………………………………………………….25 2.5 Entropy and sustainability………………………………………….……………..……27 2.6 Entropy analysis………………………………………………………………………..28 2.7 Summary of the methodology……………………………………………………………35 2.8 Conclusions……………………………………………………….……………………..36
Chapter 3. Case study…………..……………………………………………….…..37 3.1 Introduction…………………………………………………………………………….37 3.2 IBUS………………………………………………………..…………………………….37 3.3 Goal and scope definition……………………………………………………………...39 3.4 Data……………………………………………………………………………………..41 3.5 Method………………………………………………………………………………….42 3.6 Entropy flows…………………………………………………………………………..42 3.7 Entropy change in the system for the ethanol process…………………………………………..………44 3.8 Entropy change in the environment………………………………………………………………..…..45 3.9 Entropy change due to energy production……………………………………………….………………...45 3.10 LCA on IBUS……………………………………………………….........…………….…50 3.11 Results………………………………………………………………………………………………….…...54 3.12 Discussion…………………………………….......………………………........................61 3.13 Conclusions…………………… ……………………………………………………………62
Chapter 4. Overall conclusions and recommendations………........………………….…63 4.1 Why entropy as sustainability indicator for biorefineries........ ....………………………63 4.2 Case study: the IBUS process………………..……………………... ....…………..…....64 4.3 Entropy analysis compared to conventional life cycle assessment .…………….........…..64 4.4 Recommendations……………………… ....…………………………………………...65
Acknowledgement....………………………….…… ….……………………..............66
References……………………… …….………………………………………………...67
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Chapter 1 Biorefinery 1.1 Introduction "Man cannot create material things. His efforts and sacrifices result in changing the form or arrangement of matter to adopt it better for the satisfaction of his wants". Alfred Marshall [1] The term biorefinery is analogous to petroleum refinery. Similar to a petroleum refinery, a biorefinery converts a variety of biomass feedstock to produce a spectrum of biobased products, including electricity, fuels, chemical intermediates and consumer products [2]. Biorefinery can mean many different things depending on perspective [3] because it is still a concept under development, in fact. Some definitions of biorefinery are presented in Box 1.1. In a sense crude oil itself is originated from living organisms but this origin goes way back to millions of years earlier. So crude oil is not considered biomaterial but fossil material. A scheme of a refinery versus biorefinery concept is presented in Figure 1.1.
Figure 1.1 Scheme of a refinery versus biorefinery concept [4]
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Box 1.1 Some definitions of biorefinery
Kamm et al., 2006 [12] have classified biorefinery development into three phases and accordingly discusses three generations of biorefineries. An example of the type "generation-I biorefinery" is a dry milling ethanol plant. It uses grain as a feedstock, has a fixed processing capability, and produces a fixed amount of ethanol, feed co-products and carbon dioxide. It has almost no flexibility in processing. An example of a type "generation-II biorefinery" is a current wet milling technology. This technology uses grain feedstock, yet has the capability to produce a variety of end products depending on product demand. Third generation (generation-III) and more advanced biorefineries have not yet been built but will use agricultural or forest biomass to produce multiple product streams, for example ethanol for fuel, chemicals and plastics [12].
• A biorefinery is a facility that integrates biomass conversion processes and
equipment to produce fuels, power and value-added products from biomass. http://en.wikipedia.org/wiki/Biorefinery [5]
• In a biorefinery, biomass is refined in a chemical or biochemical process with the purpose of producing a high quality chemical or material.
Bioraffinaderier – vägen mot hållbar utveckling [6]
• A facility able to produce a variety of outputs such as electricity, chemicals, plastics, food and fibre in addition to biofuels. Fulton et al., 2004 [7]
• A biorefinery is an integrated facility that uses biomass as a feedstock for conversion into a range of differentiated products such as transport fuels, bulk and fine chemicals and using waste biomass for heat or power. The approach is similar to that of petroleum refineries involving a wider range of processes and techniques. http://www.biorenew.iastate.edu/resources/glossary-of-biorenewables-terms.html [8]
• Biorefinery is a refinery which replaces petroleum as the feedstock with renewable biomass. T. E. Amidon, 2006, The Biorefinery in New York: Woody Biomass into Commercial Ethanol [9]
• Biorefinery is an industrial facility able to convert biomass, including lignocellulosic material such as wood chips, into a range of material, chemical, and energy products, analogous to a petroleum refinery. Chum, H.L. and Overend, R.P., 2001. Biomass and reneawable fuels [10]
• An industrial complex that will convert renewable, lignocellulosic biomass
into fibre, chemicals, fuels, power and/or products with minimal or no negative environmental impacts. Ed Lehrburger, 2005 [11]
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1.2 A hot topic "…forests can be made to produce fifty times their present volume of end products and still remain a permanently self-renewing source for raw materials…. Only forests-no other raw material resource can yield such returns. The forest can, and so must, end the chronic scarcities of material goods that have harassed man's experience since the beginning of history." Egon Glesinger, The Coming Age of Wood, 1949 [13] As also seen in the quotation above, the idea of utilizing biomass as a source of products is not a new idea. Many others have shared the same vision and made attempts to bring this idea into practice. Among them were Henry Ford who was convinced that he could develop industrial organic chemicals from the oil of soybeans. He supported research into crop-based polymers that he hoped could be used for constructing the auto body, and in 1941 a "farm-grown automobile" was displayed at the Ford factory in Dearborn, Michigan [14]. Such early attempts have not been very successful in large industrial scale, however, mainly because of rapid development and dominance of petrochemical industry. But things are different now. The descent of petrochemical industry has arrived and this gives us courage to pursue what earlier innovators also believed in. In 2005 the Swedish government announced their intention to become the first country to break their dependence on oil and other fossil raw materials by 2020 [15]. Biorefineries is one possible approach to reach this goal considering Sweden's large potential of renewable resources, especially forests. Not only in Sweden and Europe but also worldwide, biorefinery concept is gaining more and more attention and is the focus of research and development programs. Table 1.1 shows the year 2002 levels and future targets of the United States of America for a national bio-based industry [16]. The global increasing trend towards a bio-based economy comes as the result of several reasons including:
• Fossil starting materials are non-renewable in contrast with biomass which is renewable. This is becoming more and more important as fewer and fewer reserves are being explored. They will end one day, not very far into the future.
• As the price of crude oil increases, countries seek alternative sources of energy
and material to become independent of the insecure and unstable oil supply in the global market.
• Growing environmental concerns throughout the world is another factor for
the shift towards bio-economy. The main concern is climate change. The Kyoto protocol has required the signatories to reduce their greenhouse gas emissions and fossil based industry is a main emitter.
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Target 2030 Target 2020 Target 2010 2002 Year 5%
5%
4%
2.8%
Biopower(Bioenergy) Biomass share of electricity and heat demand in utilities and industry
20% 10% 4% 0.5% Biofuels Biomass share of demand for transportation fuels
25% 18% 12% 5.0% Bioproducts Share of target chemicals that are biobased Table 1.1 The USA national vision goals for Biomass technologies by the Biomass Technical Advisory Committee [16].
• Social factors also play a role for example development of rural areas and diversifying the agricultural products are among the reasons to promote biorefineries.
• Biomass is our sole source of renewable liquid fuels and chemicals.
1.3 Biomass "Everything that exists is the seed of that which shall come out of it". -Marcus Aurelius (121-180 A.D.) [17] Biomass, in technical context, refers to living and recently dead biological material which can be used as fuel or for industrial production [18]. Technically available biomass includes a wide range of products and by-products from forestry and agriculture as well as municipal and industrial waste streams. It thus includes trees, arable crops, algae and other plants, agricultural and forest residues, effluents, sewage sludge, manure, industrial by-products and the organic fraction of municipal solid waste [19]. Biomass is a renewable resource and is carbon neutral. That means its combustion does not contribute to net increase of carbon dioxide concentration in the atmosphere. Even so biomass could still contribute to global warming indirectly, if utilized unsustainably. This happens when the natural carbon equilibrium is disturbed; for example by deforestation or urbanization of green sites. Besides, it takes some energy
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(and therefore a discharge of CO2) to grow, harvest and transport the biomass for use as a fuel source. However the use of biomass fuels can result in displacement of the emissions of carbon dioxide ordinarily released using fossil fuels. This displacement will depend entirely on the efficiency with which the biomass energy can be produced and used [18, 20]. The total global supply of biomass is estimated to be 300 billion tons and nearly 50% of it is lignocellulose [21]. Stem wood is the most abundant source of organic chemicals on earth and that explains the widespread attention towards lignocellulose as an alternative source of energy and materials. Examples of lignocellulosic biomass already used for industrial purposes are corn stover, sugarcane bagasse, wheat straw, rice straw, willow tree, Eucalyptus wood and coffee waste [22]. Amazing as it might seem to anyone who is not a chemist, just as large a range of chemicals and plastics can in theory be produced from biomass carbohydrates as can be made from petrochemical hydrocarbons [23]. In fact cellulosic material can supply many of the fuels and chemicals for which society currently depends on petrochemicals [24]. This potential has been confirmed by several other authors for example Stringer, 1996 [25] believes that plant based material, or biomass, provides a plentiful and renewable alternative source of materials for many industrial uses, but it is little used for such purposes. Or Tom Binder, 2005 from ADM [26] presenting to the first biorefinery workshop in Washington D.C. acknowledges the same potential as the motive of their company: "We believe that nature holds solutions to many of the world's problems and we will not stop until we find them. We find and create hundreds of products from nature's resources". Table 1.2 lists several of the biomass-derived materials and their increasing share on the market between 1992 and 1996 [27]. The Table shows that in 1996 plant-derived adhesives made up 48% of the total adhesives market; 50% of surfactants on the market were plant-based; 18% of detergents were based on plant matter and vegetable oil-based inks made up 16% of the ink market. While the costs of the plant-derived products were falling over time, there remained a price difference of 20 percent for inks, 50 percent for detergents and 100 percent for paints higher than their petro-based alternatives [28]. Such cost differentials suggest that these products will need both technical and market-scale improvements if they are to grow out of the niche markets. The following section will further explore the economic aspect of biomass refining.
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product 1996 production (million tons/year)
Percent of totalMarket, 1992
Percent of total Market 1996
Wall paints 7.8 3.5 9.0 Specialty paints 2.4 2.0 4.5 Pigments 15.0 6.0 9.0 Dyes 4.5 6.0 15.0 Inks 3.5 7.0 16.0 Detergents 12.6 11.0 18.0 Surfactants 3.5 35.0 50.0 Adhesives 5.0 40.0 48.0 Plastics 30.0 1.8 4.3 Plasticizers 0.8 15.0 32.0 Acetic acid 2.3 17.5 28.0 Furfural 0.3 17.0 21.0 Fatty acids 2.5 40.0 55.0 Carbon black 1.5 12.0 19.0
Table1.2 Industrial products derived from biomass [27]. 1.4 Economy "The threat of the materials problem is not that we will suddenly wake up to find the last barrel of oil exhausted or the last ton of coal gone, and that economic activity has suddenly collapsed. The real and deeply serious threat is that we shall have to devote constantly increasing efforts to acquire each pound of materials from natural resources which are dwindling both in quality and quantity, thus finding ourselves running faster and faster in order to stay standing still. In short the essence of the materials problem is costs". Paley commission report, 1952 [29] The idea of biorefinery sounds attractive but for a bioproduct to succeed in the market its production needs to be economically feasible, just like any other product. The price of raw materials and the technologies to process them are among the main challenges. And when it comes to substituting fossil-based products with bio-based products, the challenge is if the latter can compete with the existing products. As a matter of fact, biofuels and bioenergy are today seldom competitive with fossil alternatives without tax incentive [30]. Cost is not only a weak point for biofuels and bioenergy but also rather a disadvantage for many biopolymers. Table 1.3 shows a comparison between the price of biodegradable and petro-based polymers in Europe [31]. As seen in Table 3.1, there exists a very unfavorable ratio of 2.2 to 7.8 between average prices of similar biodegradable and petro-based polymers.
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Biodegradable polymer Traditional Polymers
Price ratio
Foamed starch
HDPE (PE) 2.2
Starchblends (film type)
LDPE (PE) 4.4
Polyesters (synthetic BDP)
PP 5.8
Polylactic acid (PLA)
PS 2.9
Cellulose acetate
PVC 7.8
Cellophane
PET 3.5
Table 1.3 Price ratio between average prices of biodegradable and traditional polymers in Europe [31] This could change in future, however, and there are possibilities to promote the economy of biomass processing. Biorefinery is one such possibility to reduce costs. Process integration and by-product management which are the core elements associated with biorefinery are ways to promote the efficiency and economy of the production. Finding use and market for by-products and diverting waste into usable products is particularly important in biological processes since such processes are naturally associated with considerable amount of by-products. According to Ragauskas et al., 2006 [32] "The integration of agroenergy crops and bioenergy manufacturing technologies offers the potential for the development of sustainable biopower and biomaterial that will lead to a new manufacturing paradigm". Electricity production is a key issue, especially taking into account the potential for reducing CO2 emissions, which could be reduced to below zero if co-generated electricity displaces high emission electricity from fossil fuels [33]. An advantage of bio-based chemicals over petroleum based source materials is that the latter require oxygenization because petroleum contains little oxygen while plant based chemicals already have a high oxygen content. This creates a great opportunity by eliminating the need for several capital intensive, oxidative processes used in the petroleum industry [32, 34].
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1.5 Existing biorefineries "Our products reflect our philosophy…respect for other cultures, the past, the natural world, and our customers. It's a partnership of profits with principles". Anita Roddick-The Body Shop promotional literature, 1990 [35] There are existing biorefinery plants mostly in the pilot or demonstration scale in the globe. Some of the current projects in Europe are summarized in Table 1.4. The idea is to present an insight of the possible raw materials, routes and end products and to exemplify the biorefinery concept. Observe that this is only a sample collection and may not represent the most important, in any aspects, or biggest biorefineries. In addition to the list in Table 1.4, pulp and paper mills could be examples of biorefineries where forest biomass is traditionally being converted into pulp, paper and energy. Future products are likely to be more diverse including various biofuels deriving from herbaceous and wooden biomass [39]. An illustration of pathways to possible future products from a pulp mill is presented in Figure 1.2 [40].
Chips pulp paper Bark, Forest Residues Suberin, Hemicellulose Lignin Extractives Fatty Acids Phenols Pharmaceuticals Fiber Additives Carbon Fiber Antioxidants Barriers Binders Bioactive Chemicals Hydrogels Soil improvement
Solid fuel Solid/Liquid fuels Electric power
Figure 1.2 Pathways to possible products from a pulp mill biorefinery [4].
Pulp mill
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1.6 Biorefinery of tomorrow "The next stage will not follow. The changes required will not be realised solely by political decisions, nor by market forces in industry alone, nor by individual farmers and forest farmers who see future opportunities for profit. Not until all the positive forces in society aim for the same goals can Sweden achieve independence from oil". Göran Persson, Prime minister of Sweden 1996-2006 [15] The vision of the future biorefinery is an industrial complex where diverse products are produced from renewable resources with least environmental impacts. A biorefinery in this vision is very different from current industrial production plants where they are normally pictured as polluting and hazardous. The biorefinery of tomorrow will need to use and link different scientific expertise including plant science, industrial agriculture, waste management, process technology and marketing. Table 1.5 shows what could be associated with a future biorefinery [11]. Certainly this is only one way of visualizing the future. Others have shared their vision in different ways. Kenneth Geiser, for example, in his book "Materials Matter", 2001 [34] describes an intriguing and interesting vision. "Material Factories of the future which look like large green houses with long tanks of microbially infused carbohydrates in which tiny organisms manufacture well-tailored materials from recycled organic wastes". Plant science
Production Processing End uses
Genomics wood, trees hydrolysis Fuel Enzymes grasses fermentation Power Metabolism energy crops bioconversion Plastics Composition agricultural residues chemical conversion Solvents MSW gasification Chemical intermediates Combustion Phenolics pulping Adhesives Hydraulic fluids Fatty acids Carbon black Paints Dyes, pigments Ink Detergents Pulp and paper Horticultural products Fiber boards Plastic filler abrasives Table 1.5 Vision of what could be involved in a future biorefinery [11].
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Whatever the vision would be, there are challenges ahead. As the world population and consumption rate is increasing, the main challenge would be to get the most out of the raw materials. A sustainable extraction of the natural resources requires us to aim for the highest possible efficiencies for our processes to utilize biomass. This could perhaps explain why professor B.K. Ahring from the maxifuel project in Denmark believes the keyword of their project is "maximum exploitation of the raw materials" and describes the developing biorefinery as "a carbon slaughterhouse", where every single carbon atom of the raw material is exploited [36]. In short optimal exploitation of the natural resources is the main challenge of the future and this underscores the importance of evaluation and optimization tools being available. 1.7 Sustainability "Can we move nations and people in the direction of sustainability? Such a move would be a modification of society comparable in scale to only two other changes: the Agricultural Revolution of the late Neolithic and the Industrial Revolution of the past two centuries. These revolutions were gradual, spontaneous, and largely unconscious. This one will have to be a fully conscious operation, guided by the best foresight that science can provide… If we actually do it, the undertaking will be absolutely unique in humanity's stay on the earth". William Ruckelshaus [34] The word "bio" commonly implies greenness or sustainable or at least nature friendly to the audience. Accordingly, biorefinery, bioenergy, biofuels, biopolymers, etc. have a rather high public image and acceptance, at least as a first impression. This perception is not totally wrong but not necessarily always true. There are clear environmental advantages of bio-based products or biological processes but being bio could also mean problems in some other environmental aspects. Soil erosion, loss of biodiversity, land use, water consumption and genetic modification are some of the areas where a bio-based industry does not seem very advantageous. Food for fuel argument is another issue where utilizing food crops to produce fuel and energy has been debated on moral grounds. In a more general level, this fits into a wider debate usually referred as land use competition. Since the quantity of land available on earth is limited, one main question arising is whether to use land for production of food, fuel, urbanization, etc. Another environmental aspect to take into account is that a bioproduct might have utilized considerable amount of fossil fuels and energy in the production of the raw materials. A life cycle approach, therefore, is needed to analyze the sustainability issue in these cases. In fact shifting from fossil-based production to bio-based production in large scale, if not approached scientifically, could only replace one set of environmental problems with other ones.
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While the primary barrier to biorefining is generally recognized to be the lack of low-cost processing options or platforms [41], the sustainability of the issue should not be overlooked. Reflecting on the lessons learnt from petroleum industry development, mere economical viewpoint is not an enough broad basis for evaluation. Admitting the benefits of biomass as a source of energy and materials, biorefinery could potentially contribute to sustainable development by replacing petrochemical-based products. However, Scientific, robust tools to evaluate the environmental performance of the replacing processes are needed, already in the early stage of the development of the concept, in order to realize this potential. Otherwise our today's solutions will become tomorrow's problems. 1.8 Conclusions
• A unique definition of biorefinery is difficult to decide on because the concept is still in its infancy. One definition is a facility that integrates biomass conversion processes and equipment to produce fuels, power and value-added products from biomass, analogous to a petroleum refinery.
• There is an increasing trend towards biomass worldwide as an alternative
source of energy and materials due to economical, environmental, social and political reasons.
• Lignocellulosic biomass is the most abundant source of organic chemicals on earth and has the potential of supplying many of the fuels and chemicals for which society currently depends on oil.
• High cost of production is currently the main challenge of biomass conversion and an integrated biorefinery is, in fact, a solution to reduce costs.
• Optimal exploitation of biomass sources is a main challenge of future and
biorefining is a solution to this. What needed are tools to evaluate and assist in selecting the best possible combination of raw materials, process routes and products between the various alternatives.
• As sustainability is being more and more in focus, there is a need for a
tangible quantitative description that allows the sustainability of technological processes to be systematically evaluated, compared and improved.
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Chapter 2 Entropy analysis among other system analysis tools 2.1 Introduction Referring to the conclusions in the first chapter, optimization of biomass conversion processes in biorefineries is where more improvement is necessary and important. To achieve this, evaluation tools are needed to measure and compare different processes in terms of efficiency, environmental performance and cost reduction potentials. In this chapter several of such tools are briefly introduced and the underlying bases for their development are discussed. This is presented in Section 2.2 and is mainly inspired from a review done by Krotscheck, 1997 [42]. Entropy analysis is the suggested tool in this thesis and is going to be elaborately discussed in Section 2.6 comprising its necessity in a biorefinery (2.6.1), the previous applications (2.6.2) and suggested methodology (2.6.3). Before that and to get to that point of discussion, some thermodynamic background is reviewed in Section 2.3; different versions of second law analysis are introduced and discussed in Section 2.4; and the link between entropy and sustainability in the scientific literature is reviewed in Section 2.5. 2.2 Flow based measures for eco-sustainability There are number of tools available to analyze a process or a product. Christian Krotscheck, 1997 [42] has done a review and comparison of eco-sustainability measurement tools. His study reviews and compares Life Cycle Assessment (LCA), Material Input Per unit Service (MIPS), Sustainable Process Index (SPI), Appropriated Carrying Capacity (ACC), Pollution Control Costs (PCC) and Waste Potential Entropy (WPE) as different types of mass and/or energy flow based highly aggregated indicators. These eco-sustainability indicators will be briefly discussed here. However the discussion on LCA will be more elaborative since LCA will be further used in our case study to compare and analyze the entropy tool results. 2.2.1 Life Cycle Assessment (LCA) Life Cycle Assessment (LCA) is a tool to evaluate the environmental performance of a product or service throughout its life span, from raw material extraction to disposal. The main characteristic and the strong point of LCA is its holistic approach; it analyses a product from its cradle to grave and considers different environmental issues at a time. In this way trade-offs between different types of environmental impact and between different parts of life cycle are made evident [43, 44].
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LCA procedure consists of four distinct phases namely Goal and scope definition, Inventory analysis, Impact assessment and Interpretation of results. Goal and scope definition is the first step in the analysis in which the purpose of the study and the system boundary is determined. One important decision of this stage is the choice of functional unit. In the Inventory analysis the incoming and outgoing flows to the system are determined and calculated per functional unit. Normally only environmentally relevant flows are considered. These include resource use, emission and waste streams. Impact assessment is translation of life cycle inventory results into environmental impacts [43, 44]. Flows including emissions and resource use are classified into different categories of environmental harm and the impact for each category is quantified by calculating the flows into a common unit for a specific impact. A weighting stage might follow to aggregate the results further into a single number of environmental harm. Finally, the results need to be interpreted and discussed according to the goal of the study. There is a series of international standards for LCA, ISO 14040-14043 issued from 1997 onwards which indicates the importance and wide acceptance of LCA. The standardization of LCA became necessary when some enterprises started to perform and publish biased LCA studies in the 1990s to promote their own product [43]. There were some cases of contradictory results which led to criticism that LCA results show what the analyst had been paid to show. Although a uniform and standard LCA methodology now obstructs misusing of the tool, there are still subjective elements in LCA methodology dependent on the will of the performer. One problem with LCA, therefore, is subjectivity. For example even though LCA outlook implies a holistic view and approach, not all the environmental impact categories could be dealt with in a study in practice [45]. Therefore, the choice of impact categories is always one decision by the commissioner or performer of LCA to specify their viewpoint. Another example of subjective elements in LCA is the weighting stage where different ready-made weighting methods measure people's values in different ways [43, 45]. It is not only lack of time and data which limits an analyst but also lack of characterization methods for some impact categories. Environmental impacts such as biodiversity loss, land use, soil erosion are very difficult to be quantitatively assessed in LCA. Another characteristic of LCA which is its strong point in one way is that it potentially follows the fate of an emission in the environment to identify the environmental harm it would cause. Impact assessment is based on scientific models of cause-effect chains in natural systems. In this way, Life Cycle Impact Assessment is able to answer specific questions such as effect of an emission on Global Warming for instance. This capability, however, only applies to known impacts. If an emission harms the environment in a way other than the identified environmental impacts, LCA tends to neglect it. One good example to clarify the point here is the case for CFC (Chloro Fluoro Carbon) compounds. When CFCs were first marketed, it was as a nontoxic substitute for the ammonia used in refrigeration and the chlorinated solvents used throughout many workplaces [34]. They were never identified as hazardous to human
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health but they were damaging the environment in another way, unknown at that time. The dissipation of CFCs into the atmosphere were stimulating chemical changes in the ozone layer. Only after quite considerable damage happened in the ozone layer, the crisis started to attract attention. Krotscheck also confirms there are many outputs that are not seen as hazardous today but may emerge as hazards on our way to a more sustainable society [42]. His example is carbon dioxide which would not have entered a list of hazardous substances as recently as the 1970s, yet today is acknowledged as a major ecological risk factor. He concludes that LCA fails to reflect these potential pressures and is rather suitable for short-term evaluation. Impacts such as climate change or stratospheric ozone depletion once happened will take a very long time to stop and manage, if at all possible. To move towards sustainability, a more extensive commitment is needed. A more precautionary approach would be to show concern for dissipation of energy and materials to the environment even when the mechanism of the environmental damage is not totally clear. The scale of production today, the rapidity of industrial development around the world, and the kind of environmental concerns that are arising - climate change, endocrine disruption, biodiversity loss - suggest conventional approach to the management of industrial materials is inadequate [34]. 2.2.2 Material Input Per unit Service (MIPS) The Material Input Per unit of Service (MIPS) methodology was developed by Fredrich Schmidt-Bleek, 1993-94, as a measure of potential environmental impact [46, 47]. He and his colleagues in German Wuppertal Institute refer to the amount of material to be moved or processed and the amount of energy consumed per unit of finished material as the ecological rucksack or the ecological burden of each unit of material. For example, the catalytic converter used in cars which weighs less than 20 pounds had been found to carry a rucksack of some 2.5 tons, because the mining and processing of the platinum requires the movement of such large amounts of overburden and ore [47]. MIPS is very input oriented and according to Krotscheck neglects the emission side of activities [42]. Baumann and Tillman, 2004 [43] argue that MIPS is a simpler tool than LCA in the sense that environmental impact has been approximated by material usage. 2.2.3 Sustainable Process Index (SPI) Sustainable Process Index (SPI) is based on the assumption that in a sustainable economy the only real input that can be utilized over the long term is solar exergy and that its utilization per se is bound to the surface of planet earth [42]. Furthermore, area is a limited resource in a sustainable economy because the surface is finite.
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Developed by Krotscheck and Narodoslawsky, 1996 [48], SPI is the fraction of the area per inhabitant related to the delivery of a certain service and Krotscheck claims very high abilities for SPI as a measure of sustainability [42]. 2.2.4 Appropriated Carrying Capacity (ACC) The appropriated carrying capacity is proposed by Rees, 1994 [49] to be the fundamental base for demographic accounting. The concept initially was designed to make the human "footprint on the ecosphere", a concept proposed by Wackernagel et al., 1993 [50], of regions visible to their inhabitants. ACC, thus, is the area that a region uses to run its economy. Among the drawbacks of the tool, according to Krotscheck are the poor reflection of non-agricultural resources and negligence of waste side of the activities. 2.2.5 Pollution Control (abatement) Costs (PCC) Pollution Control Costs (PCC) concept developed by Krozer, 1995 [51] shows the costs for control measures (eg. pollution control technology) to attain a specific environmental quality (carrying capacity). The underlying assumption is that coevolution of man and ecosphere requires efforts to prevent the degradation by residuals. As an economic based measure, PCC has the advantage of direct compatibility with currently used economic evaluation methods. However, Krotscheck argue that like any full cost assessment approach, there are inherent problems. The levels to which emissions should be reduced (sustainable standard) are subjective and abatement costs depend on the state of technology. He concludes that the standardization of pollution prevention systems will discourage initiatives to invent new process alternatives or strategies [42]. 2.2.6 Waste Potential Entropy (WPE) Waste Potential Entropy (WPE) is developed by Ayres and Martinas, 1994 [52] as a thermodynamic based measure for eco-toxicity. It is argued that what is dangerous to the environment is the emission of material fluxes (i.e. waste streams) that are not in thermodynamic equilibrium with the environmental systems into which they are emitted. The most dangerous aspect of disequilibrium is argued to be differential physico-chemical composition between the waste stream and the environmental medium into which it is emitted. Such differentials constitute potential entropy increase. Waste potential entropy is, thus, a measure of the potential of waste streams for driving uncontrolled chemical or physical processes in environmental systems; it is not an adequate predictor of the actual damage that will be done by given pollutants. Despite this and despite the fact that Krotscheck rejects the WPE valuation in his conclusion [42], The authors, Ayres and Martinas, argue that "it is quite reasonable to postulate that the aggregated damage potential of human economic activity is proportional to the total entropy potential of aggregated waste emissions" [52].
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The entropy tool to be presented in this thesis report in the following sections is different from WPE in the approach and outlook even though the scientific bases for both are the same (2nd law of thermodynamics and entropy generation as a measure of unsustainability). Although not dealt with in this thesis, further research to possibly link the two approaches is worth considering in future developments of the proposed tool. 2.3 Thermodynamic background Before getting into the discussion of the methodology of entropy analysis tool, some background information on scientific basis of the proposed tool is presented in this section. These basics are necessary for better understanding of the evaluation and proper interpretation of the results. Thermodynamics is essentially the science of conversion of energy and matter. This is of importance to environmental scientists as it allows us to understand and predict the extent of energy and material utilization. It is more and more recognized that thermodynamic process analysis can play a significant role in the design and evolution of future technological processes [53]. The starting point for most thermodynamic considerations are the laws of thermodynamics. The established basic laws of thermodynamics are presented in Box 2.1. Sometimes a forth law is discussed in the scientific literature which is basically an attempt to apply thermodynamics to evolution of the organic world [54]. However, that is not presented in the Box since it is yet not a solidified concept [55]. Some authors also introduce a combined law of thermodynamics which is basically a combination of first and second law [56], again not presented in the Box. The second law of thermodynamics, also known as entropy law, is the basis of the evaluation tool under study in this thesis and therefore will be more in focus in this section. Box 2.1 Established basic Laws of thermodynamics
• Zeroth law. If two systems are separately in thermal equilibrium with a third,
then they must also been in thermal equilibrium with each other [57].
• First law. Energy can neither be created nor destroyed; it can only change forms [58].
• Second law. The sum of the entropy changes of a system and its exterior can
never decrease [59].
• Third law. The entropy change of a system during a reversible isothermal process tends towards zero when the thermodynamic temperature of the system tends towards zero (Nernst Principle) [60].
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Entropy and second law are two of the vaguest concepts going in current science [61]. According to Bridgman, 1941 [62], "there's as many formulations of second law as there have been discussions of it". Box 2.2 presents some various interpretations of the 2nd law of thermodynamics. Entropy is often taken as being something hard to grasp [68]. For the sake of clarity, entropy may be defined, in its cleanest form as "the quantitative measure of amount of thermal energy not available to do work in a closed system" [69, 70]. Box 2.3 presents some standard textbook descriptions of entropy. Box 2.2 Some interpretations of second law of thermodynamics [61]
• The ratio of the heat content of a closed system to its absolute temperature
always increases in any process. Clausius 1850 [62]
• There is at present in the material world a universal tendency to the dissipation
of mechanical energy. Kelvin 1852 [63]
• For any process the sum of all entropy changes occurring as a result of the process is greater than zero and approaches zero in the limit as the process becomes reversible. Clausius 1865 [64]
• The entropy of universe tends to increase. Bent 1965 [65]
• No process is possible whose sole result is the transfer of heat from a colder to a hotter body (Clausius statement) Adkins 1983 [57]
• No process is possible whose sole result is the complete conversion of heat into work (Kelvin statement) Adkins 1983 [57]
• The entropy of universe increases in a spontaneous process and remains unchanged in an equilibrium process. Chang 1998 [66]
• No process will occur spontaneously unless it is accompanied by an increase in the entropy of universe. Haynie 2001 [67]
• When energy does work, its quality, its exergy, diminishes. Kay 2002 [61]
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In classical thermodynamics, the change of entropy of a thermodynamic system could be mathematically defined as the ratio of a reversible heat interaction to the absolute temperature [71] as shown in Equation (2.1).
dS = δQ/T (2.1)
Standard molar entropy of a substance is then defined as the entropy content of one mole of substance under conditions of standard temperature and pressure (STP) [83]. It is shown by S0 in this report, as it is also common in thermodynamic texts and the unit is usually Joules per mole-Kelvin. Standard entropy is the same concept but the unit is normally in Joules per gram-Kelvin.
Box 2.3 Examples of standard textbook descriptions of entropy [72]
• Energy broken down in irretrievable heat [73]. • Boltzmann's constant times the logarithm of a multiplicity, where the
multiplicity of macrostate is the number of microstates that correspond to the macrostate [74].
• The number of ways of arranging things in a system (times the Boltzmann's
constant) [75].
• A non-conserved thermodynamic state function, measured in terms of the number of microstates a system can assume, which corresponds to a degradation in usable energy [76].
• A direct measure of the randomness of a system [77].
• A measure of energy dispersal at a specific temperature [78].
• A measure of the partial loss of the ability of a system to perform work due to
the effects of irreversibility [79].
• An index of the tendency of a system towards spontaneous change [80].
• A measure of the unavailability of a system's energy to do work; also a measure of disorder; the higher the entropy the greater the disorder [81].
• A parameter representing the state of disorder of a system at the atomic, ionic
and molecular level [69].
• A measure of disorder in the universe or of the availability of the energy in a system to do work [82].
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Another important term in relation to the second law of thermodynamics which is discussed quite frequently especially in the domain of sustainability, is exergy. Exergy is described by Szargut, 1988 [84] as the maximum amount of work that can be done by a subsystem as it approaches thermodynamic equilibrium with its surroundings by a sequence of reversible processes. Exergy represents the portion of energy available to do work and is accepted as a measure of the quality of energy [85]. The exergy loss is proportional to the entropy increase and is the product of the entropy increment and the ambient temperature [86]. The mathematical relationship between the two concepts, Equation (2.2), also confirms this.
dB/dT = -S (2.2) Where dB is an element of exergy, T represents temperature of the environment and S is entropy. Closing this section, some interesting quotations around the 2nd law of thermodynamics are presented in Box 2.4, indicating the "mysterious" nature of this principle of universe; "the principle of evolution" as Perrot, 1998 [60] called it. Box 2.4 Some quotations on 2nd law of thermodynamics
• The law that entropy always increases, holds, I think, the supreme position among the laws of nature. If someone points out to you that your pet theory of universe is in disagreement with Maxwell's equations- then so much the worse for Maxwell's equations. If it's found to be contradicted with observation-well, these experimentalists do bungle things sometimes. But if your theory is found to be against the second law of thermodynamics I can give you no hope; there is nothing for it but to collapse in deepest humiliation. Sir Arthur Stanley Eddington [87]
• The tendency for entropy to increase in isolated systems is expressed in the second law of thermodynamics. Perhaps the most pessimistic and amoral formulation in all human thought. Greg Hill & Kerry Thornley [88]
• Everything tends towards disorder. Any process that converts energy from one form to another must lose some as heat. Perfect efficiency is impossible. The universe is a one-way street. Entropy must always increase in the universe and in any hypothetical isolated system within it. James Gleick [89]
• ─ What would you do to look for life on Mars? ─ I'd look for an entropy reduction, since this must be a general characteristic of life. James Lovelock [90]
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2.4 Second law analysis Second law analysis is understandably based on the second law of thermodynamics for system analysis and is in contrast with first law analysis which is basically application of direct energy balance. There are different approaches to second law analysis. According to Rosen, 1999 [91] the main approaches discussed in the literature are exergy analysis, physical exergy analysis, exergy consumption analysis, negentropy analysis and entropy analysis. Figure 2.1 depicts different types of second law analysis in order of level of subjectivity. As the level of subjectivity increases, the environment needs to be specified in more detail but Rosen believes that the results, then, would contain more information.
Figure 2.1 Different approaches to Second Law Analysis stem from 2nd law of thermodynamics, shown in this diagram in order of increasing subjectivity (starting from the bottom), based on Rosen, 1999 [91]
Exergy Analysis
Physical Exergy Analysis
Exergy Consumption Analysis
Negentropy Analysis
Entropy Analysis
2nd law of thermodynamics
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Rosen seems to prefer exergy analysis while German scientist Götz Alefeld, 1988 [92] favors entropy analysis over exergy. Back in 1988, he wrote about the problems with exergy analysis. He believes the exergy concept is not the second law in its full general way and it is a reduced version of the second law. He explains that if the process temperatures of the system you are considering are not identical with the ambient temperature, exergetic efficiencies signalize values which are often too high and even contradicting the second law [92]. Bejan, 1982 [93] agrees that to calculate entropy generation rate and minimize it, the analyst does not need to rely on the concept of exergy. Huang et al., 1992 [86] also argued that entropy balance was a "more rigorous solution" in their analysis than was the exergy balance at describing the system's performance. They used the entropy balance method to study thermal systems and concluded: "The entropy method yield not only the same information as is conveyed by the methods of energy and exergy analysis but it also predicts clearly the influence of irreversibilities of individual components on the coefficient of performance and its effectiveness, based on the process properties, rather than on ambient conditions". Different authors, however, disagree over which approach is the most useful. The usefulness of the approach also depends on the application being studied. Comparing with first law analysis (energy balance), second law analysis is more rigorous by taking into consideration the availability of energy not only its existence. Such advantage has been discussed in several studies. For example according to Fartaj, 2004 [94] second law analysis provides an insight that the first law analysis can not. He analyzed a double-stage heat transformation cycle by energy, exergy and entropy balance methods. The results of his investigation showed that the second law could help to locate the system non-idealities that either were not identified or misevaluated by energy analysis. He believes that comparison of energy quantities from the first law (energy balance method) alone could be misleading. Kotas, 1985 [95] also argues that energy balances in general provide no information about internal losses. An energy balance for an adiabatic system could lead one to believe that these processes are free of losses of any kind. This is because the first law of thermodynamics does not distinguish between heat and work while the second law deals with quality of energy. Dealing with conservation of energy, the first law can not indicate that one form of energy can or can not be completely converted to one another. For example, it is always impossible to completely convert heat into work [94]. Entropy analysis is the focus of this report and is going to be elaborately discussed in the following sections. There have been previous applications of entropy analysis which will be briefly reviewed in section 2.6.2 but not in the field of biorefineries. This thesis is an attempt to test this tool in a biorefinery and to suggest future directions.
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2.5 Entropy and sustainability Several authors observed and argued for a link between entropy and sustainability and some developed and proposed measures for sustainability based on the entropy law. Some of the arguments in this debate are briefly discussed in this section. The interested reader is suggested to refer to the cited articles for more in depth discussions. Minimum production of entropy is suggested to be a criterion of sustainability by T.M. Addiscott, 1995 [96]. He specifically elaborates this in agriculture where he suggests that the principle of Minimum Entropy Production provides a useful framework within which sustainability can be discussed. He goes on to suggest a way of assessing sustainability developed by Bertilsson, 1992 [97] known as "audit of small molecules". This oversimplified approach argues that the larger emission of small molecules in a system, the larger production of entropy; which means less sustainability. Entropy as an indicator of sustainability in agro-ecosystems is also suggested by Steinborn and Svirezhev, 2000 [98]. Hornbogen, 2003 [99] also discusses the concept of sustainability based on the rate of entropy production and believed it to be useful as "foundation for a science of recycling". He argues that "positive entropy is not only a measure for the devaluation of heat but also for dispersing, mixing of matter on the solid surface, dissolving it in groundwater, oceans and in the atmosphere" and therefore entropy minimization describes many important aspects implied in sustainability. He postulates: "The industrial process leads to desired and undesired increases on entropy due to mixing or dispersion of matter. Material sustainability is achieved if enough energy is provided to create negative entropy (ordering force) to compensate for the undesired raise in entropy" and uses entropy for a definition of a quantitative concept for sustainability in regard to matter. For Ayres and Martinas, 1994 [52] the criterion for sustainability from a thermodynamic perspective would seem to be fairly simple to state: " The earth system is sustainable if the average rate of entropy production does not exceed the average rate of influx of entropy potential from the sun, which is the only sustainable source". Earlier, they defined entropy potential as stock of physical information, namely the difference between the actual entropy of a system and its final entropy, after it reaches equilibrium - or becomes indistinguishable from - its local environment. They went on to introduce "waste potential entropy" as a measure of potential eco-toxicity, which was also discussed in section 2.2.6 of this report. More recently, Ernei Jowsey, 2007 [100] proposes what he calls "a new basis for assessing the sustainability of natural resources". In his view entropy concept is an important way of measuring the quality of natural resources and for understanding the irreversible nature of environmental and resource processes. Another recent paper by Wolfgang Winkler, 2006 [101] recommends that "reduction of entropy flows to the environment reduces primary energy consumption of
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mankind". He uses the second law of thermodynamics to define the principle design rules of sustainable technologies in minimizing the irreversible entropy production. He explores his idea by discussing reversible power generation in fuel cell hybrids, reversible transportation and reversible heat processes. One last interesting relation in this section is the interface between entropy theory and Chaos theory, commented on by Addiscott [96]. Chaos theory describes systems that are in a state of pure disorder but in which order and even beauty can be found [102]. People like Cambel, 1993 [103] have started to explore this interface. 2.6 Entropy Analysis Entropy analysis is a method to evaluate a process based on the 2nd law of thermodynamics. It is basically calculating entropy generation in a system and its surroundings (caused by the process or the product under study) and using it as a proxy for the evaluation of the process. The magnitude of the entropy generation calculated in this way is not useful by itself; it makes sense in comparison. The comparison could be done with the entropy generation in an alternative process (with the same goal) or with an imaginary chemically ideal process. In the latter case, a thermodynamic efficiency will be resulted as an indicator of how far the studied process is from the best thermodynamically possible. Optimization possibilities could be identified in this case. The comparison could also be done between entropy generations in different stages of production. To conduct a fair comparison, it is obviously important to apply equivalent system boundaries and methodological choices for the different alternatives. It is also important to compare different routes to a product on a life cycle basis. This is because a claimed better process could be worse in the production of raw materials for example. In such fair conditions for comparison, a higher entropy generation for the same amount of product or service is an indicator of a less efficient process. Process optimization from chemical engineering point of view would hence correspond to minimizing the entropy generation. Therefore, the first step in the analysis is very similar to LCA's where a clear definition of goal and scope of the study is needed. Furthermore, it is wise to have a conceptual understanding of what being measured before applying the tool. Entropy generation calculated in this way is a thermodynamic concept. A physical translation would be dissipation in case of materials and dispersal in case of energy. When a process generates entropy in the universe - which is a fact for every real process according to the 2nd law of thermodynamics - the raw materials and input energy are being dissipated and dispersed. Therefore, entropy generation is an indicator of how efficient a process is utilizing the natural resources.
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Dissipation is both an economical and environmental problem [34]. Diminishing the quality of input materials and energy is an economic loss while dispersal and release of the output flows of energy and materials to the environment might be a threat to the ecosystem. Therefore, the result of entropy analysis could be meaningful and valuable from both economical and environmental view points. 2.6.1 A tool in a biorefinery? Overlooking minor gains from meteorites and leakage of light gases from upper atmosphere to space, the earth could be considered a closed system with respect to matter. But it is definitely an open system with respect to energy because it receives light from the sun. According to 2nd law of thermodynamics, entropy could decrease locally in an open system. The flow of energy from the sun creates structure and order from disorder. Sunlight is responsible for almost all increases in net material quality on the planet through photosynthesis [104]. Chloroplasts in plant cells take energy from sunlight for plant growth. Utilizing the plants, how much disorder do human create from this source of quality and ordered materials? In biorefining, dissipation is a matter of increasing concern since utilizing biomass as a source of energy and materials is likely to increase in the future. In order to satisfy the increasing demands, efficiency of biomass conversion is of crucial importance. According to Tynjälä, 2006 [39] economy of scarcity will have a major role in future and the use of natural resources will have to be optimized. Whether the resources should be used for energy, food or other commodities is among the questions which are arising. In a future biorefinery, there will exist a variety of raw materials, process technologies and end products as demonstrated in Table 1.5. This raises the question of how to choose the best possible way to reach a product or the best way ahead from a raw material. Whether thermodynamics can provide answers to such questions is an issue to be investigated in this thesis. To give an example of the complexity of a biorefinery, a range of possible feedstocks and different pathways for the production of biodiesel and bioethanol is presented in Figure 2.2. It is worth reminding that biodiesel and bioethanol are of course not the only possible end products of a biorefinery and are presented in Figure 2.2 just as examples. The range of feedstocks, processing routes and products would be more diverse than this in a future biorefinery. This underscores the necessity of scientific measures in hand to evaluate and compare different alternatives in a biorefinery. 2.6.2 Previous applications Entropy generation has been used as a basis for evaluation previously. The methodology and approach, however, has not been exactly the same as what is suggested in this thesis. For example "waste potential entropy" suggested by Ayres and Martinas, 1994 [52], which was discussed in section 2.2.6, uses the potential for entropy generation (and not the entropy generation itself) in the waste flows as a measure for eco-toxicity. This concept could be added to the methodology suggested here in future works to further develop the method to predict eco-toxicity better.
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Dry Milling Corn Wet Milling Wheat Malting Sugarcane Fermentation Bioethanol Sugarbeet Acid Hydrolysis Straw Enzymatic Hydrolysis Wood Gasification Fischer-Tropsch Biodiesel Oil seeds Esterification Figure 2.2 Pathways to biodiesel and bioethanol production as examples of products from a biorefinery [105]. Evaluation and comparison of paths in more complex networks in biorefineries is a major challenge. Huang et al., 1992 [86] (also mentioned in Section 2.4) applied the entropy balance method to thermal systems. The analysis in a biorefinery would be more advanced since thermal irreversibilities are not the only source of thermodynamic losses there. One interesting aspect of the work of Huang et al. is the correlation between irreversibilities and economic loss. The closest approach to what is introduced in this thesis is perhaps the work of Tobias Richards, 2001 [68]. In fact, Richards' work is a main reference for the methodology proposed here. He used entropy analysis to compare different processes of recovery of kraft black liquor from thermodynamic point of view. In 1996, Adrian Bejan [106] published a nice review of entropy generation minimization method which he described as an optimization method for real (irreversible) devices and processes. His comprehensive review traced the development of the method in several disciplines including refrigeration plants, nuclear and fossil power plants, solar power plants, storage systems and heat transfer. The optimization method which Bejan discussed is mainly carried out subject to physical constraints that are responsible for the irreversible operation of the device. Table 2.1 lists examples of earlier applications of entropy generation minimization method based on Bejan's review.
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Analyst, year application comment Martynovskii et al., 1971 [110]
cryogenics Determined the optimal intermediate cooling (and optimal shield temperatures) for minimum entropy generation rates in stacks of radiation shields
Eyssa and Okasha, 1978 [111]
cryogenics Optimised stacks of radiation shields where the space between shields is filled with super-insulation
Agsten, 1973 [112] cryogenics Developed optimal intermediate cooling regime for cryogenic cables by minimising the total power required to operate the cooled cable
Angulo-Brown et al., 1993 [113]
cryogenics Cooling of Superconducting transition
Anderson et al., 1992 [114] and Bejan et al., 1995 [115]
cryogenics Cooling of Electronic packages in computers
Jeong and Smith, 1994 [116]
cryogenics Development of the optimal temperature staging of cryogenics refrigerators
Schultz and Bejan, 1983 [117]
cryogenics Optimization of intermediate cooling regime for parallel heat leak paths based on the entropy generation minimization approach
Sekulic et al., 1994 [118] Heat transfer Thermodynamic optimization of smooth and enhanced ducts Zimparov and Vulchanov, 1994 [119]
Heat transfer Applied the entropy generation minimization method to assess the merits of using spirally corrugated tubes
Sekulik and Herman, 1986 [120]
Heat transfer Optimization subject to fixed total number of heat transfer units
Sarangi et al., 1982 [121] and da costa et al., 1985 [122] and Sekulic, 1985-86 [123] and Sekulic, 1986 [124]
Heat transfer Thermodynamic optimization of several types of heat exchangers where the fluid friction irreversibility is negligible
London and Shah, 1983 [125] and Zubair et al. 1987 [126] and Lau et al. 1987 [127]
Heat transfer Optimization of boilers and condensers
Bejan, 1977 [128] and Sarangi and Chowdhury, 1982 [121] and Huang, 1984 [129]
Heat transfer Entropy generation in counterflow heat exchangers
Baclic and sekulic, 1978 [130]
Heat transfer Crossflow heat exchangers
Wepfer et al., 1979 [131] Heat transfer Relationship between entropy generation minimization and cost minimization in the problem of deciding the optimal size of a steam pipe and its insulation
Kirna et al., 1993-94 [132-136]]
Materials design They used the entropy generation calculations to explain and predict elastic systems
Badar et al., 1993 [137] and Kotas and Jassim, 1993 [138]
Storage systems Entropy generation combined with overall cost minimization to propose a thermoeconomics model of sensible heat storage process
Bellecci and Conti, 1994 [139]
Storage systems Optimization of latent heat storage units coupled in series with a power plant
Fujiwara, 1983 [140] Solar power plants Entropy generation in solar collectors Bejan, 1996 [141] Nuclear power plants Minimizing the total entropy generation rate associated with the
power plant to derive the maximum power efficiency Ibrahim et al., 1991 [142] Fossil power plants Entropy minimization combined with life cycle economic
optimization of the power plant Angulo-Brown, 1991 [143]
Fossil power plants Power plant optimization by minimizing entropy generation and with a focus on ecological impacts
Wu, 1987 [144] Power plants Optimization of ocean thermal energy conversion Bejan et al., 1994 [145] Power plants Optimization of on and off operation of power plants that have to be
shut down to have their heat exchangers cleaned (defouled) Alefeld, 1989 [146] Refrigeration plants Improving the thermodynamic performance of refrigeration
machines
Table 2.1 Examples of earlier applications of entropy analysis based on Bejan, 1996 [106]
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Also confirmed in Table 2.1 is the fact that entropy analysis in the field of biorefinery has not been applied before, at least to the author's knowledge. There have been a few exergy analyses in the field of biofuels. One example is the exergy analysis on biodiesel production by Talens et al., 2007 [107]. Tynjälä, 2006 [39], too, raises the applicability and usefulness of exergy analysis in biorefineries. Arif Hepbasli, 2006 [108] has done a comprehensive review of exergy analysis on renewable energy resources which includes biomass among other renewable sources such as solar, wind, geothermal, waves and tides. Another interesting paper is the work of Wagendorp et al., 2006 [109] who suggest a quantitative assessment method for evaluation of land use in life cycle assessment based on the second law of thermodynamics. Considering the shortcomings of LCA in land use impact evaluation, their study could potentially be useful to establish better evaluation approaches in this field. 2.6.3 Method As stated earlier, the first step in the analysis is to define the goal of the study and the system boundaries. The goal of the study and the system boundaries should be defined wisely so that the results of the calculated entropy generation would lead to a meaningful conclusion. Therefore this first step is a very important part of the analysis. The next step is to determine the flows of materials and energy passing the system boundaries. Figure 2.3 depicts a sample system in relation to the flows crossing the system's borders. The incoming flows could be raw materials, ancillary materials or energy input and the outgoing flows could be products, waste or heat loss. Qout min,i mout,i min,j mout,j min,k mout,k Elout Qin Elin Figure 2.3 A general configuration of a supposed system with flows of materials and energy in and out of the system's boundary.
Process X Process Y
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2.6.3.1 Entropy generation in the system Next, entropy of each stream of material passing the system boundaries should be determined. To assign an entropy load for each incoming and outgoing material stream, enough information of the flows are needed. This includes the composition, temperature, ion content if relevant and the size of the flow. Standard entropies of the materials in question could be found in thermodynamic literature in forms of charts, tables or equations. Examples of useful thermodynamic references, in this regard, are NIST Chemistry WebBook, 2005 [147], Perry and Green, 1984 [148] and Shomate Equation. The general form of Shomate equation for entropy calculation is formulated as:
S = A*ln(t) + B*t + C*t2/2 + Dt3/3 - E/2t2 + G (2.3)
Where S is the molar entropy in J/mol.K and t = T/1000 while T is the temperature in K [147]. Seven phase based coefficients in Shomate Equation could be found in reference literature, for example NIST Chemistry WebBook, for different substances. This is not always the case, however, since the entropies of organic compounds, for example, seem to be very difficult to find in the literature. If no standard entropy could be found, then the choice of the entropy value of the substance shall be justified on scientific grounds. For example, the entropy value of similar substances or the information content of the material or the extent of order in the structure of the molecules could be used as the basis for estimation. However, such guesses should only be the choice as a last resort and when no other information is available. If the entropy at a certain temperature could not be found in the sources (Shomate Equation or other charts or tables), it could be calculated provided that the standard entropy for a reference temperature is known. One useful equation to calculate entropy from the standard entropy is Equation 2.4 which shows the relationship between entropy change and heat capacity of the substance.
∆S = ∫T0T1
Cp/T dT (2.4) In Equation 2.4, Cp is the heat capacity of the substance which again is to be found in reference literature. Finally after assigning all the material flows with corresponding entropy values, entropy generation in the system could be easily calculated using Equation 2.5.
∆Ssystem = ∑Sout – ∑Sin (2.5) Equation (2.5) can be translated as follows: "The sum of entropy of material streams leaving the system (entropy out) minus the entropy of all the material flows entering the system (entropy in) is the entropy generation in the system".
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2.6.3.2 Entropy generation in the environment It is not though enough to take into consideration only the entropy generation in the system. The exchange of energy and materials between the system and its environment also generates entropy in the environment. This is in line with the second law of thermodynamics which studies the entropy generation in a system and its surroundings. The generated entropy in the environment should also be taken account of by adding it to the entropy generation in the system. The origin of entropy generation in the environment is either the exchange of heat between the system and its environment or the release of waste and emissions to the environment. Heat Transfer Transfer of energy between the system and its surroundings creates entropy equal to the heat transferred over the temperature of the environment. This is shown in Equation 2.6. It is assumed that heat transfer does not change the temperature of the environment.
∆Ssurroundings = ∆Q/T (2.6) Mixing The entropy generation in the environment due to release of emissions and waste materials can be calculated according to mixing effect. The entropy of mixing is the change in the entropy when two different chemical substances or components are mixed. This is obviously the case for environmental releases of waste or emissions since the composition of the releasing chemical is different from the composition of the environment. The entropy change due to mixing effect could be calculated according to Gibbs theorem, as shown in Equation 2.7.
∆Smixing = -nR(X1 lnX1 + X2 lnX2) (2.7) Where R is the gas constant, n the total number of moles and Xi the molar fraction of each of the mixed components. It should be noted that the underlying assumption for the calculation of the entropy generation in the environment by means of Equations 2.6 and 2.7 is steady state approximation. That means all flows and concentration gradients are approximated to be time-independent. The total entropy generation caused by the system under evaluation is the entropy generation in the system plus entropy generation in the environment. This would mean the sum of calculated entropy generations by Equations 2.5 to 2.7.
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2.7 Summary of the methodology To summarize the entropy method, the first step is a clear definition of goal and scope of the analysis. The goal could be a comparison of different alternative processes or comparison with a thermodynamically ideal case. In the latter case, a thermodynamic efficiency will be resulted as an indicator of how far the studied process is from perfection. After defining the goal and system boundaries, the flows of material and energy entering and leaving the system shall be determined. The entropy flows for each stream of material is then calculated based on the material flows information and standard entropies of the materials. Standard entropies are derived from charts, tables or equations in thermodynamic source literature. The entropy generation in the system is then calculated as the sum of entropy of outgoing materials minus the sum of entropy of incoming materials (Equation 2.5). This shall be added, then, to the entropy generation in the environment. The origin of entropy generation in the environment is either heat transfer between the system and the environment or release of waste materials to the environment. Equations (2.6) and (2.7) can be used to calculate these entropy generations in the environment. Finally, the total entropy generation equals the entropy generation in the system plus surroundings i.e. the sum of entropy generations calculated by Equations (2.5) and (2.6) and (2.7). It is worth reminding that a conclusive comparison needs to be based on a life cycle perspective. For example when a process consumes energy, the production of energy carriers (either electricity or heating or whatever form of energy) should also be included in the study. That means it would not be enough to only take account of the flow of the energy into the system by means of Equation (2.6). The upstream activity to generate that flow should also be considered in order to maintain a sound holistic evaluation.
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2.8 Conclusions
• As sustainability gains increasing attention, need for quantitative evaluation of the concept rises, in addition to its qualitative description.
• There are number of measures developed to evaluate eco-sustainability which includes life cycle assessment among others.
• LCA evaluates the environmental performance of a process based on known environmental impact categories while a broader commitment to sustainability requires us to search for ways to foresee future emerging ecological problems.
• The second law of thermodynamics could be used as the basis for evaluation
of processes. It has been used before in the form of exergy analysis or entropy analysis but seldom (if ever) in a biorefinery.
• Simple heat or energy balances are based on 1st law of thermodynamics but
they lack the insight and information which could be gained by 2nd law analyses.
• According to 2nd law of thermodynamics energy spontaneously disperses from
being localized to becoming spread out if not hindered from doing so. Entropy measures the spontaneous dispersal of energy: how much energy is spread out in a process or how widely spread out it becomes at a specific temperature [149].
• Entropy analysis uses the entropy generation in a system and its exterior as a
measure of efficiency. • Entropy analysis in a biorefinery could be used as one selection parameter for
the most optimal way of using resources i.e. the best combination of feedstocks, processes and products.
• The total generation of entropy caused by a process is the entropy generation
in the system plus entropy generation in the surroundings which is resulted as either heat interaction or mixing of materials with the environment.
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Chapter 3: Case Study 3.1 Introduction In this chapter, a biorefinery case is introduced and the trial of the proposed evaluation tool on this case is presented. This includes a description of the process, definition of goal and scope of the study, data, method of analysis, sample calculations and results. The results are then compared with the results of a life cycle assessment and also a cost analysis performed on the case in order to motivate the discussion of the results. An important objective of this thesis is to identify the necessary developments and modifications in the proposed tool for evaluation. Therefore the assumptions in the study are tried to be stated in a transparent manner in order to highlight the areas where modifications and developments could improve the entropy analysis. After all, modifying the assumptions is one way of improving and developing the tool in future studies. The case study selected is the IBUS project in Denmark. This is an interesting example of an emerging biorefinery yet being simple enough to test the proposed methodology. Furthermore, they discuss different alternative processes for the production of bioethanol which makes this a proper case for comparison by entropy analysis. The other reason to select IBUS is their openness to share their information via publicized reports [153, 156]. Regarding the structure of the chapter, sections 3.1 to 3.4 are descriptive of the case, sections 3.5 to 3.9 discuss the entropy evaluation, section 3.10 presents the life cycle assessment of the case, section 3.11 presents the total results, section 3.12 discusses the results and finally conclusions are summarized in section 3.13. 3.2 IBUS The principle behind Integrated Biomass Utilization System (IBUS) is integrated utilization of lignocellulose feedstocks and conventional sugar/starch feedstocks co-located with combined heat and power plant [150]. The general objective is development of a cost and energy effective production system for bioethanol and electricity. Figure 3.1 illustrates IBUS idea and principle. According to the project's specific website (www.bioethanol.info) by such integration with existing power plants, biomass is expected to provide ethanol for the transport sector and electricity to the grid at prices lower than any other production system of pollution free renewable energy [151].
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Figure 3.1 The idea in IBUS [150, 152] In the course of the project, a pilot plant demonstrating the complete process from straw to ethanol has been built on Fynsvaerket, Odense, Denmark [153]. The pilot plant includes the following parts:
• Straw reception • De-baling • Stone trap • Cutting • Pre-soaking • Thermal treatment (2 reactors) • Hydrolysis reactors/fermentation tanks • Stripping and rectifying columns • Control system
In addition to bioethanol, the plant produces the following products:
• Distilled Dried Grains (DDGs) as animal feed • Biofuel (lignin) • C5-molasses as animal feed
One work package of the IBUS project contains feasibility studies based on data from trials on the integrated pilot plant and relevant external data [153]. The data used in these feasibility studies are also useful for the entropy analysis.
Biomass plant CHP plant
Grain in bulk
Whole crop in big bales
Straw in big bales
Municipal solid waste
Electricity Steam
Biofuel
Fossil fuels
Co-firing
Non-organic components
Bioethanol
Animal feed, ca. 30% protein Animal feed, ca. 60% protein
Electricity
Heat
39
3.3 Goal and Scope definition Several alternative IBUS configurations are discussed in feasibility studies done by IBUS group from which three cases will be analyzed and compared in this thesis. The cases are addressed in this report as Alt. C2, Alt. D2a and Alt. D2b (names unchanged from IBUS report). For all these three cases, the feedstock is straw and the products are ethanol, lignin as biofuel and C5 molasses as animal feed. The fermentation process is as follows:
• Alt. C2: Fermentation of C6 (glucose) sugars by yeast • Alt. D2a: Fermentation of C6 (glucose) and C5 (xylose) sugars by
thermophilic micro-organism • Alt. D2b: Fermentation of C6 (glucose) and C5 (xylose) sugars by yeast
The goal of the study is to compare these three processes by entropy analysis method. This is done by calculating the entropy increase for production of the same amount of ethanol for each case and comparing the results. This means that ethanol is decided as the main product and goal of the biorefinery in this analysis, which is in fact a reasonable choice for the IBUS case.
Each alternative is studied based on two different scenarios for steam production. Scenario 1 is the integration of the biomass plant with a combined heat and power (CHP) plant where the low pressure steam from the coal-fired power plant would be used in the biorefinery as the steam source. Scenario 1 is also addressed as "integrated scenario" in this report. Scenario 2 is when no integration is possible and steam boiler fueled by natural gas is erected in the biorefinery. Scenario 2 is also addressed as "no integration scenario" in this report.
One side goal, therefore, could also be to assess the effect of integration of the biorefinery with a CHP plant on the results.
Regarding the scope of the analysis, the agricultural production and the use phase of products are excluded in this study. The system boundaries of this analysis are shown in Figure 3.2. Since the goal of the study is to compare three different processes of ethanol production, the exclusion of the use phase would not have any effect on the results of the comparison and is, therefore, a wise choice. On the other hand, the inclusion of agricultural activities could have affected the results because there are different amounts of feedstock input for the three cases (even though the type of the feedstock is the same, wheat straw, for all three cases). Due to time limitations, the agricultural part is excluded in this thesis but the recommendation for future improvement studies is to include this phase in the analysis as well.
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Figure 3.2 Technical system boundaries of the analysis.
The production of electricity and steam are included in the analysis while the processes of production of enzymes and other additives are not considered due to their relatively very small contributions in the process. However, it is worth noticing that additives that are used in minor quantities might need comparatively large inputs in their turn (since some of them are rather complicated) and are recommended to consider in a more complete study. Water catchment activity is also assumed to have negligible impacts. The biorefinery construction and the effects of personnel-related activities are not considered in this study which is a quite reasonable choice for this analysis. In Figure 3.2, the ethanol production activity includes several sub-activities. These include [153]:
• Particle generation and cleaning • Pretreatment • Enzymatic hydrolysis • Ethanol Fermentation • Ethanol recovery
These sub-activities are not shown in Figure 3.2 because the ethanol production is seen as a black box in this analysis and the details of sub-activities are not in focus.
Agriculture Ethanol
production from straw
Enzyme production
Other additives
production
Electricity generation
Steam generation
End use
Animal production
Heat and power
production
CO2
kernel
straw
ethanol
C5-molasses
biomass
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3.4 Data Ethanol production's input and output flows for the three alternatives that will be studied here are mainly taken from the IBUS published report [153] and are presented in Table 3.1.
Unit Alt. C2 Alt. D2a Alt. D2b
Fermentation type C6, yeast C6, C5, TMO C6, C5, yeast
Input Straw t/year 945000 683000 645000
Raw water t/year 66900 8688 11400 Steam GWh 957 782 692
Cooling GWh 948 799 703 Electricity GWh 158 143 112
Output Ethanol t/year 140000 140000 140000 Biofuel t/year 330000 251300 227500
C5 molasses t/year 422000 175600 163900 Carbon Dioxide1 t/year 140000 140000 140000
Table 3.1 Input/output of the processes under evaluation [153]
1 CO2 mass flow is based on [154] The input of enzymes, micro-organisms and other additives are assumed to be negligible due to their small input quantities comparing to other flows. For instance, based on the data from an Elsam 2005 report [155], 1.2 kg A-amylase (enzyme) and 25 kg Sulfuric acid (the largest additive in quantity) are used for every ton of ethanol produced. Such low numbers will not have a tangible impact on the results and their omission is, therefore, justifiable (The effect of exclusion of sulfuric acid flow was calculated to be approximately 0.03 percent of the total entropy generation for the C2 case, for example). Based on the claims of the IBUS development team, no waste water is assumed to be generated in the processes. The carbon dioxide flow is estimated to be the same in mass quantity as ethanol output, based on an expert advice [154] and also quite closely confirmed by the mass balance data of IBUS energy balance report [156]. All other flows data in Table 3.1 are taken from IBUS published report [153] which affirms the data source as their own experimental data and current commercially available processes.
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3.5 Method The general methodology is described in Section 2.6.3 and is summarized in Section 2.7. This method will be applied to ethanol process, steam production and electricity production activities for the three cases under evaluation in the following sections. The approach for the ethanol process will be as follows. After determining the goal of the study (section 3.3) and identifying the mass and energy flows (Table 3.1), the entropy value for each of the material flows should be determined. This is done by first finding out the standard entropy of the materials and then calculating the entropy flow of the stream from the specific characteristics of the flow such as temperature and mass flow. This step will be explained in Section 3.6. Entropy generation in the system is then going to be calculated in Section 3.7 which is basically the difference between entropy of outgoing and incoming material flows (see Equation 2.5). Section 3.8 explains the calculation of entropy generation in the environment which needs to be added to the entropy generation in the system. The approach for steam and electricity generation will be similar but with simplifications in the emission flows. They are explained in Section 3.9. 3.6 Entropy flows After identifying the flows of materials and energy crossing the system boundaries, the next stage is to assign an entropy value for each stream of material. For the ethanol process, the streams of accounted materials entering and leaving the system are shown in Figure 3.3. The entropy value for each flow and the assumptions and basis of determination of entropy are explained below. The diagram in Figure 3.3 and the explanations for the flows are valid for all three alternative cases under investigation. Figure 3.3 Accounted material flows for the ethanol process in the analysis.
STRAW
WATER
ETHANOL
BIOFUEL
C5 MOLASSES
CO2
ETHANOL PROCESS
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3.5.1 Straw Standard entropy could not be found for straw in thermodynamic sources or elsewhere. As discussed in Section 2.6.3.1, an intelligent estimate could be made as a last resort in such cases. Straw mainly consists of cellulose, hemicellulose, lignin and water. The entropy of straw was assigned on the grounds of its water content. That means the entropy of cellulose, hemicellulose and lignin are assumed to be zero. The explanation is that cellulose molecule as a highly ordered structure, lignin as a macro-molecule in solid state and different kinds of hemicelluloses in solid states shall have very low entropies. This reasoning is inspired from the same type of reasoning for the entropy of wood in Richards' thesis, 2001 [68]. However, more thorough investigation and thinking to this matter is recommended for future studies. The temperature of the incoming straw is assumed to be 300 K and its water content, inspired from IBUS energy balance report [156], is assumed to be 14 percent. The standard entropy of water is taken from Nist chemistry webBook [147] as 3.91 J/g.K. 3.5.2 Raw water
The temperature of raw water input is assumed to be 300 K and its entropy, therefore, is the same as above, 3.91 J/g.K.
3.5.3 Ethanol
The temperature of output flow of ethanol from biorefinery is assumed to be 40°C. The calculation of entropy is based on the relationship between entropy change and heat capacity (see Equation 2.4). The standard entropy of liquid ethanol is taken as 160 J/mole.K and the heat capacity of liquid ethanol is taken as 118.4 J/mole.K [147]. The entropy of ethanol at 40°C, then, could be easily calculated to be 3.83 J/g.K.
3.5.4 Biofuel
Biofuel in this case is mostly comprised of lignin. The assumption is again as in the case for straw i.e. the entropy of this stream is taken as the entropy of its water content (see Section 3.5.1). The water content of biofuel is assumed to be 10 percent based on the IBUS energy balance report [156]. The temperature of this flow is assumed to be 300K. 3.5.5 C5-molasses
C5-molasses are mainly comprised of Xylose and similarly to straw, the entropy of its water content is assumed to be the entropy of this flow. The water content of molasses is estimated to be 30 percent. The temperature of this output flow is also taken as 300K. 3.5.6 Carbon dioxide
The temperature is assumed to be 310K for the outgoing Carbon Dioxide flow and the entropy value is then taken from Nist Chemistry WebBook [147] as 4.89 J/g.K.
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3.7 Entropy generation in the system for the ethanol process
Based on the mass flows (Table 3.1) and the entropy flows in J/g.K (Section 3.6), the entropy flows in KW/K for the three alternative processes could be derived. This is shown in Table 3.2.
The total entropy generation in the system is the difference between the total entropy out and the total entropy in (see Equation 2.5). A simple calculation from the entropy flows in Table 3.2 would give the entropy generation in the system for the three cases, which are also shown in Table 3.2. However, these values for the entropy generation in the system are not meaningful without taking into consideration the entropy generation in the environment (see Section 2.6.3). Alt. C2 Alt. D2a Alt. D2b S (kW/K) S (kW/K) S (kW/K) Input Straw 16.4 11.9 11.2 Raw water 8.3 1.1 1.4 Output Ethanol 17.0 17.0 17.0 Biofuel 4.1 3.1 2.8 C5 molasses 15.7 6.5 6.1 Carbon dioxide Entropy created
21.7 21.7 21.7
∆Ssystem 33.8 35.3 35.0
Table 3.2 Entropy of the material flows and entropy generation in the system for the ethanol process in the three alternative processes.
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3.8 Entropy generation in the environment As discussed in Section 2.6.3.2, the entropy generation in the environment is either due to heat transfer or material mixing effect. 3.8.1 Heat transfer Heat exchange between the system and its environment generates entropy in the environment which can be calculated by Equation 2.6. Using this Equation and based on the input of steam, cooling and electricity for the three cases (see Table 3.1), the following entropy generations could be calculated. The temperature of the environment is set as 300K. ∆Sheat transfer = 785.0 kW/K for Alt. C2 (3.1) ∆Sheat transfer = 656.0 kW/K for Alt. D2a (3.2) ∆Sheat transfer = 573.4 kW/K for Alt. D2b (3.3) 3.8.2 Mixing Carbon dioxide mixing effect in the atmosphere is not taken account of. The explanation is that carbon dioxide emission from a biorefinery is from renewable sources. Therefore, its emission will not alter the natural cycle because it would have eventually released from the plant if it was not utilized in the biorefinery. 3.9 Entropy generation due to energy production As discussed in the goal and scope of the analysis (Section 3.3), the processes of generation of steam and electricity are part of the analysis. Therefore, the entropy generation caused by these two processes will be considered. In addition to steam and electricity, the input energy to the biorefinery also includes cooling. Production of cooling effect has not taken into consideration in this study and is assumed to have negligible impacts. For both electricity and steam generation, the input flows are approximated by one flow of primary energy input. The energy input flow is an estimate of the total direct and indirect energy input into the process throughout the life cycle of production of steam or electricity. In other words, it is not enough to consider only the direct energy input to electricity or steam production; all energy inputs in the full life cycle need to be taken account of. The calculation of the full life cycle energy input into production of electricity or steam is done by using a "life cycle correction factor" applied to the direct energy input. This idea is based on the IBUS energy balance report [156]. This
46
correction factor is explained in their report as follows: "The whole life cycle inventory for Danish electricity is estimated using a large LCA study of generation technologies" [157]. "The results of this study were that using a correction factor on the direct energy inputs would yield a fairly precise estimate of the required energy input for electricity for the full life cycle. Correction-factors were 1.3 for coal, 1.2 for oil, 1.3 for natural gas and 1.03 for renewables". The emissions other than carbon dioxide are also excluded due to their small quantities compared to carbon dioxide flow. For example in coal-fired electricity production, the biggest emission after carbon dioxide is sulfur dioxide which is 260 times less in mass quantity [161]. Similar proportions apply to steam generation emissions [159]. However, it should be noted that in some processes such as natural gas combustion, quite considerable amounts of water vapor is dissipated to the atmosphere. Water vapor is not considered in this study with the justification that it is not an environmentally relevant flow. It is though recommended to be considered in future studies because entropy analysis is claimed to be a tool with a broader view than current environmental concerns. 3.9.1 Steam generation assumptions As stated in section 3.3, two alternative scenarios for steam generation are studied in this analysis. Scenario 1 assumes that surplus steam is available at low cost. This is the case if the biorefinery is integrated with a large power plant. According to IBUS energy balance report [156], the additional fuel requirement for steam production in case of integration is in the range of 0.5-0.7 MJ of additional primary fuel per MJ of steam required. Using the life cycle correction factor of 1.3 for coal [156, 157], the life cycle based input range estimate of 0.75 MJ of primary energy per MJ of steam is used in this study. Scenario 2 assumes that surplus steam is not available for the biorefinery and a separate steam boiler is erected at the biorefinery. The preferred fuel for separate steam generation in Denmark is natural gas [156]. With a boiler efficiency of 97% [158] and full life cycle correction factor of 1.3 for natural gas [156, 157], a primary energy need of 1.34 MJ/MJsteam is selected. The entropy generation for the two scenarios is assumed to be due to input of primary energy and also mixing effect of carbon dioxide emission from the fuel combustion. That means that other emissions from the combustion process are neglected based on their relatively small quantities. It also means the entropy generation in the system is assumed to be negligible compared with the entropy generation in the environment. Carbon dioxide emission is assumed to be 91.6 g/MJ of coal combusted in scenario 1 and 55.2 g/MJ of natural gas combusted in scenario 2 based on emission factors provided by Tillman et al., 1991 [159]. 3.9.2 Electricity generation assumptions The electricity is assumed to be generated at mix from fossil fuels (4% oil, 25% natural gas and 46% coal) and renewables (9% biomass and waste, 16% wind and hydro) as reported by Danish Energy Authority [160]. The direct energy input of 2.06
47
MJ primary energy per MJ of Danish electricity produced is used [160]. Using the correction factors discussed above and a grid loss estimate of 1% in the transmission grid and 5% in the distribution grid [156], the primary energy input at the point of use of 2.71 MJ/MJel or 9.8 MJ per KWh of electricity is used [156]. The entropy generation from electricity production is assumed to be due to input of primary energy and also the mixing effect of carbon dioxide emission in the environment. That means that other emissions and waste from the electricity production are neglected due to their relatively small quantities. It also means the entropy generation in the system is assumed to be negligible compared with the entropy generation in the environment. Based on the reference inventory table for electricity production [161, 162] and the mix of electricity source fuels mentioned above, carbon dioxide emission is calculated to be 198.7 g/MJ of electricity produced. 3.9.3 Entropy of mixing The entropy generation due to mixing effect of carbon dioxide emission with the atmosphere is calculated based on the Gibbs theorem (Equation 2.7). The mixture gas is assumed to be an ideal gas and the total mean mass of the earth's atmosphere is taken as 5.1480*1018 kg [163]. This mixing entropy generation is calculated for both steam and electricity productions. 3.9.4 Results of entropy generations due to energy production The entropy increases due to production of steam and electricity are shown in Table 3.3 for the three different cases. Sample calculations of Table 3.3 are presented in section 3.9.5. 3.9.5 Sample calculations Calculation of entropy generations in the energy production processes are shown for two sample results (the samples are shown in italics in Table 3.3).
• Calculation of entropy generation due to energy input for production of electricity in Alt. C2:
The process Alt. C2 uses 158 GWh electricity in one year, according to Table 3.1. And based on the assumption in section 3.9.2, the primary energy input is 9.8 MJ / KWhel. Therefore the primary energy input for the production of electricity in one year would be: 9.8 MJ/ KWhel * 158 * 106 KWhel = 1548.4 TJ As shown in Table 3.3. The generation of entropy due to transfer of this energy in the environment could be calculated according to Equation 2.6, as follows: (The temperature of the environment is set as 300K).
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Alt. C2 Alt. D2a Alt. D2b
Energy input
CO2 emission
Entropy generation
Energy input
CO2 emission
Entropy generation
Energy input
CO2 emission
Entropy generation
unit TJ kton kW/K TJ kton kW/K TJ kton kW/K
Steam Production 2583.9 273.1 2111.4 223.2 1868.4 197.5
Scenario 1 (integrated)
236.7 34.3 193.4 28.3 171.1 25.2
Steam Production 4616.6 488.0 3772.4 398.7 3338.2 352.8
Scenario 2 (no integration)
254.8 36.8 208.2 30.4 184.3 27.0
Electricity production 1548.4 163.7 1401.4 148.1 1097.6 116.0
113.0 16.9 102.3 15.4 80.1 12.2
Table 3.3 Entropy generation due to steam and electricity production for the different alternatives.
∆Ssurroundings = ∆Q/T = 1548.4 TJ / 300K = 5.16 TJ/K This is the flow in one year. To calculate this entropy generation in kW/K, we can write: 5.16 TJ/K = (5.16 * 109) KJ / (365*24*3600) K.s = 163.7 kW/K • Calculation of entropy generation due to carbon dioxide emission from
production of steam in Alt. D2b (Scenario 1): The process Alt. D2b uses 692 GWh of steam in one year, according to Table 3.1. 692 GWh = 692*3600 GJ = 2491.2 TJ And based on the assumption for Scenario 1 in section 3.9.1, the primary energy input is 0.75 MJ / MJsteam. Therefore, the primary energy input for steam production would be:
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0.75 MJ / MJsteam * 2491.2 TJsteam = 1868.4 TJ As shown in Table 3.3. And again according to section 3.9.1, carbon dioxide emitted is 91.6 g/MJ of input energy in Scenario 1 where coal is being combusted. This means the total emission of carbon dioxide in one year from steam production would be: 91.6 g/MJ * 1868.4 TJ = 171.1 kton CO2 This number is also shown in Table 3.3. Now, the entropy of mixing due to emission of this mass of carbon dioxide needs to be calculated. The entropy of mixing could be calculated by Equation 2.7, rewritten below: ∆Smixing = -nR(X1 lnX1 + X2 lnX2) In this case, X1 represents the molar fraction of atmosphere and X2 the molar fraction of carbon dioxide in the mixture where n and R represents the total number of moles in the mixture and the gas constant, respectively. If we show the number of moles in the atmosphere and the number of carbon dioxide moles by n1 and n2 respectively, then: n1 = 5.1480 * 1018 kg / 29 kg/kmol = 0.1775 * 1018 kmol The mass of the earth's atmosphere is taken as 5.1480 * 1018 kg [163], as also stated in Section 3.9.3 and the molar weight of air is 29 gr/mole. n2 = 171.1 Kton / 44 kg/mol = 3.89 * 106 kmol CO2 44 is the molar weight of Carbon Dioxide. Based on this, we can write: n = n1 + n2 = 0.1775 * 1018 X1 = n1 / n = 1.00
X2 = n2 / n = 2.19 * 10-11
The mixture gas is assumed to be an ideal gas and the constant is 8.314 J/K.mol. Finally by substituting the values for n, R, X1 and X2 in the formula, the entropy of mixing would be resulted, as follows: ∆Smixing = -nR(X1 lnX1 + X2 lnX2) = 7.93 * 108 kJ/K in one year = 25.2 kW/K
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3.10 LCA on IBUS Life cycle assessment is conducted of the three IBUS cases investigated by entropy analysis. The objective is to compare entropy results with LCA results in order to gain more insight for the interpretation of the entropy results. As such, the focus is here more on the characterization and weighting results even though a general description of the method, data sources and assumptions are also presented. This LCA study is primarily based on an LCA study performed on the IBUS project in Chalmers University of Technology [6]. The original study is then adapted to resemble the entropy analysis performed in this study in terms of system boundaries, input and output flows and basic assumptions. This is done in order to produce comparable results. The LCA is performed using life cycle assessment inventory tool (LCAiT) software. A flowchart of the activities included in the LCA is given in Figure 3.4. The two different scenarios for steam production are shown in different colors in Figure 3.4. As a reminder, scenario 1 (integrated) is based on coal while scenario 2 (no integration) is based on natural gas for steam supply of the biorefinery (refer to sections 3.3 and 3.9.1). Electricity production is assumed to be at mix generated from oil, natural gas, coal, renewables and hydro, resembling a similar assumption in the entropy analysis (see section 3.9.2). As seen in Figure 3.4, the agricultural part and few other activities are excluded, again to resemble the same system boundaries as was supposed for the entropy analysis. The activities included in the entropy analysis are illustrated in Figure 3.2. The functional unit is 1 ton of ethanol because the results could then be compared with the entropy generation per ton of ethanol. In the following, the data sources and assumptions for the LCA are described for each activity in the flowchart. 3.10.1 IBUS process As for the entropy analysis the main reference for flow data is the IBUS published report [153], except for the carbon dioxide mass flow. Carbon dioxide mass flow is assumed to be the same as the ethanol mass flow according to an approximation by Christian Morgen [154] and also quite closely confirmed by the mass balance data of IBUS energy balance report [156]. The main inputs and outputs for the three alternative IBUS cases are given in Table 3.1. 3.10.2 Electricity IBUS Similar to entropy analysis, the electricity is assumed to be generated at mix from fossil fuels (4% oil, 25% natural gas and 46% coal) and renewables (9% biomass and waste, 16% wind and hydro) as reported by Danish Energy Authority [160].
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Elec
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Heavy fuel oil production, cradle to gate Data for the extraction and refining of heavy fuel oil are taken from Frischknecht et al., 1994 [164]. The data cover exploration, extraction, refining and transport to stock. Heavy fuel oil combustion Data representing combustion of heavy fuel oil in industrial boilers (1 MW) are taken from Frischknecht et al., 1996 [165]. Emission of Sulphur dioxide has been adjusted from a sulphur-content of 2.6% in the original source to 0.65% S [166]. Emission of particles is also dependent to some extent on the sulphur content. This has been taken into account by using an equation from Frischknecht et al., 1996 [165]:
Emission of particles = 1.25 S + 0.38
where S is the sulphur content in percent, the emission of particles is given in kg per 1000 litres.
This gives an adjustment of the emission of particles from 50 to 29 kg per TJ. The adjustment of the emission of sulphur dioxide gives a reduction from 1.2 g per MJ to 0.3 g per MJ. Efficiency of oil power plant: gate to gate
Assumption of efficiency of electricity production in an oil power plant: 30% Electricity natural gas powered including 9% grid losses: cradle to gate The data is taken from different sources [164, 167, 168] and compiled by [175]. Electricity coal powered including 9% grid losses: cradle to gate The data is taken from different sources [164, 165, 167, 169, 170, 171, 172] and compiled by [175]. CFB furnace renewable fuel Combustion of renewable fuel in a CFB-furnace at the paper mill of HylteBruk, Sweden is the basis of data and were provided by Arnold Älmqvist, Hyltebruk, in August 1992 [176] as documented in [175]. Efficiency of bio-fueled power plant: gate to gate Assumption of efficiency of electricity production in a bio-fueled power plant: 30% similarly to oil power plant assumption. Electricity hydropower: cradle to gate Data is based on Brännström-Norberg et al., 1996 [173]. No grid losses are taken into account.
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3.10.2 Steam coal-fired: cradle to gate (integrated scenario) This activity exists in Scenario 1, when the biorefinery is integrated with a coal-fired power plant. The surplus steam from the power plant supplies the steam need of the biomass plant in this scenario (refer to section 3.9.1). Amount of extra coal needed to produce steam in a power plant integrated with the IBUS process is taken from IBUS energy balance report [156]. Hard coal production Data is based on Frischknecht et al., 1994 [164]. Hard coal production includes extraction and refining of hard coal. Hard coal combustion: cradle to gate Data is based on Frischknecht et al., 1994 [164]. Except for methane, the emissions to air for coal extraction come from energy use. The methane emission is well documented and comes from underground mines. The water emission is caused by impurities and trace elements in raw coal and is ascribed the use of process water in mining and preparation of the raw coal. 3.10.3 Steam natural gas: cradle to gate (no integration scenario) This activity exists in Scenario 2, when the biorefinery is not integrated with a power plant. The steam supply for the biorefinery is provided by a separate steam boiler fuelled by natural gas in this scenario (refer to section 3.9.1). Natural gas production: cradle to gate Data is based on a study performed by Bakkane, 1994 [174]. Natural gas combustion Data for the combustion of natural gas in industrial boilers (>100kw) is taken from Frischknecht et al., 1996 [165]. Efficiency steam production from natural gas Similar to entropy analysis, the efficiency of steam production from natural gas is assumed to be 97% [158].
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3.11 Results
The sum of entropy generation results for the activities analyzed namely ethanol process, steam generation and electricity generation for the three alternatives under study are presented in Section 3.11.1. The results of the LCA study conducted with similar system boundaries and assumptions are presented in Section 3.11.2 for the sake of comparison. And finally the results of a cost analysis performed on the three alternative cases are presented in Section 3.11.3. 3.11.1 Results of the entropy analysis
The total increase in entropy is the generation of entropy in the system plus surroundings. The entropy increases in the system for the ethanol process are shown in Table 3.2 for the three alternatives. The entropy generations in the environment for the ethanol process are shown in Equations 3.1 to 3.3 for the three alternatives. The entropy increases in the steam and electricity production activities are shown in Table 3.3. Simply adding the relevant figures for each alternative would result the total entropy generation for each alternative which is shown in Table 3.4.
Recalculating per ton of ethanol, the results of entropy analysis presented in Table 3.4 are shown in Figure 3.5 in form of a chart. The chart in Figure 3.5 is arranged per ton of ethanol to provide the same format as LCA charts which will be further presented. Figure 3.5 shows that from entropy perspective, Alt. D2b is the most efficient process and Alt. C2 the least efficient while Alt. D2a is in between. This is seen to be true for both Scenarios of steam production.
Alt. C2 Alt. D2a Alt. D2b
Fermentation type C61, yeast C52, C6, TMO3 C5, C6, yeast
unit kW/K kW/K kW/K Scenario 1 (integrated) 1306.8 1106.3 959.3 Scenario 2 (no integration) 1524.2 1283.9 1116.4
Table 3.4 Total entropy increases for different alternatives based on 2 different scenarios. 1 C6 sugars (glucose), 2 C5 sugars (xylose), 3 Thermophilic Micro-Organism
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0
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Entr
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ratio
n (M
J/K
.ton)
Alt. C2 Alt. D2a Alt. D2b
Integrated ScenarioNo integration Scenario
Figure 3.5 Entropy generation in MJ/K per ton of ethanol for the three alternative processes and based on two different scenarios for steam supply.
Another observation is that Scenario 1 performs better than Scenario 2 based on entropy analysis. This is seen to be true for all the three cases. As a reminder, Scenario 1 is when supplying the steam need of the biorefinery with the surplus steam from an integrated coal-fired power plant and Scenario 2 is when such integration is not present and the steam is provided by a natural gas steam boiler for the biorefinery (refer to Sections 3.3 and 3.9.1). 3.11.2 Results of the life cycle assessment
The LCA inventory results were characterized [180] regarding impacts on climate change (EDIP-GWP [181]), resource depletion (R-to-use [182]), acidification (EDIP-AP [181]) and photochemical ozone creation potential (EDIP-POCP [181]). Figures 3.6 and 3.7 illustrate the results of LCA for Global Warming Potential (GWP) and Resource Depletion (R-to-use), respectively. They show that concerning global warming and resource depletion, Alt. D2b is the best option and Alt. C2 the worst. Scenario 1 is also seen superior to Scenario 2 for all the three alternatives. This is the same observation as entropy analysis. The difference is in the relative distance between the two scenarios. If we consider resource depletion (Figure 3.7), scenario 2 is much worse than Scenario 1 while the Global Warming potential (Figure 3.6) of Scenario 2 is very slightly higher than Scenario1. The distance between the two scenarios in entropy analysis (see Figure 3.5) is somewhere between. The reason why Scenario 2 is scored this heavily in resource depletion is the use of natural gas instead of coal in Scenario 2. Two other environmental categories discussed in this report are Acidification and Photochemical Ozone Creation. Impact assessment results for Acidification (AP) and
56
Photochemical Ozone creation (POCP) are presented in Figures 3.8 and 3.9, respectively.
0
500
1000
1500
2000
2500
kg C
O2 e
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t/ton
Alt. C2 Alt. D2a Alt. D2b
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Figure 3.6 Global warming potential (GWP) results calculated per ton of ethanol for the three alternative processes and based on two different scenarios for steam supply.
0
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Figure 3.7 Resource depletion (R-to-use) results calculated per ton of ethanol for the three alternative processes and based on two different scenarios for steam supply.
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0
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12
14kg
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2 equ
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Alt. C2 Alt. D2a Alt. D2b
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Figure 3.8 Acidification Potential (AP) results calculated per ton of ethanol for the three alternative processes and based on two different scenarios for steam supply.
0
0,05
0,1
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0,2
0,25
0,3
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/ton
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Integrated ScenarioNo integration Scenario
Figure 3.9 Photochemical Ozone Creation Potential (POCP) results calculated per ton of ethanol for the three alternative processes and based on two different scenarios for steam supply.
Figures 3.8 and 3.9 show that in consistence with entropy method and two previous LCA charts, Alt. D2b is better than Alt. D2a which is in turn better than Alt. C2,
58
concerning acidification and ozone creation. This is seen to be true for both scenarios. However, there's a difference in these two charts with the previous ones. Scenario 2 is more attractive than Scenario 1 If only Acidification is considered or if only Ozone creation is considered, according to Figures 3.8 and 3.9 respectively. The explanation is that coal firing (Scenario 1) is very much more hazardous than natural gas combustion (Scenario 2) if Acidification or Photochemical Ozone creation are the assessed impacts. The results of entropy analysis could also be compared with LCA weighting results. The results of the LCA were weighted based on two alternative weighting methods. Ecoindicator 99 and EPS 2000 were the chosen weighting methods. Ecoindicator 99 is a method based on the distance to target principle [43] and EPS 2000 is based on the willingness-to-pay for avoiding damages on environmental safeguard subjects [43, 45, 177, 178]. The results are illustrated in Figures 3.10 and 3.11 for Ecoindicator 99 and EPS 2000 weighting methods, respectively. Figures 3.10 and 3.11 show that the results of LCA weighting are in rather good agreement with the results of entropy analysis. That means weighting also indicate Alt. D2b as the most and Alt. C2 as the least interesting process and Alt. D2a is in between. And Scenario1 is again advantageous to Scenario 2 for all the three processes. This is true based on both methods of weighting. The difference is in the distance between the two scenarios in different weighting methods and also in the entropy analysis. Comparing with Scenario 1, Scenario2 is far more harmful in EPS 2000 weighting. The explanation is that resource depletion is weighed heavier in EPS method [43] and since Scenario 2 uses natural gas instead of coal, its environmental loads are heavier than Ecoindicator method. The entropy method shows a quite smaller difference between the two alternative scenarios of steam production (but still the difference is in the same direction).
0
20
40
60
80
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120
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Eco
poin
ts/to
n
Alt. C2 Alt. D2a Alt. D2b
Integrated ScenarioNo integration Scenario
Figure 3.10 Ecoindicator 99 weighting results calculated per ton of ethanol for the three alternative processes and based on two different scenarios for steam supply.
59
0100200300400500600700800900
1000
ELU
/ton
Alt. C2 Alt. D2a Alt. D2b
Integrated ScenarioNo integration Scenario
Figure 3.11 EPS 2000 weighting results calculated per ton of ethanol for the three alternative processes and based on two different scenarios for steam supply.
3.11.3 Results of the cost analysis
Feasibility studies performed by the IBUS project team also include cost analysis [153]. To carry out cost analysis was not part of this thesis and therefore only parts of the results of cost analysis performed by the IBUS team is going to be presented here for the sake of comparison with entropy results. Once again, the comparison is in order to attain more insight into the proposed entropy method. More information on the cost analysis could be found in the IBUS published report [153].
Figure 3.12 illustrates production costs per liter of ethanol for the three alternative cases. The cost analysis is performed based on the integrated Scenario (Scenario 1). The IBUS report [153] claims that integration of the biorefinery with an existing power plant has provided cost reductions up to 18 % mainly deriving from lower investment cost and lower energy cost. Some price estimates in the cost analysis are as follows:
• Straw: 60 Euro/ton based on Danish prices. Straw prices around 30 Euro/ton
are more realistic in most of Europe [153, 179] • Cellulases (enzyme): 0.16 Euro/liter ethanol but it is claimed that this price is
expected to decrease to 0.03 Euro/liter ethanol [153]. • Solid biofuel: 4.46 Euro/GJ
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0
10
20
30
40
50
60
70Eu
ro c
ent/l
iter
Alt. C2 Alt. D2a Alt. D2b
integrated scenariono integration scenario
Figure 3.12 Production costs per liter of ethanol [153].
Comparing the ethanol production costs in Figure 3.12 with the current ethanol price in Europe (58 Euro cent/liter, May 2006 [153]) reveals that Alt. D2a and especially Alt. D2b are potentially profitable while Alt. C2 is hardly feasible from economical point of view. However, realizing the potentials for cost reductions could improve the economic performance of the production process. Comparing the chart in Figure 3.12 with the one in Figure 3.5, the results of cost analysis is in rather good agreement with the results of entropy method. Alt. C2 generates more entropy and has higher costs than Alt. D2a and Alt. D2b generates the least entropy and claims the lowest costs among the three. This strengthens the idea of correlation between economy and dissipation suggested by Geiser, 2001 [34] (see Section 2.6) or the correlation between economic loss and irreversibilities put forward by Huang et al., 1992 [86] (see Section 2.6.2).
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3.12 Discussion A closer look into the generation of entropy in the three processes reveals that energy inputs into the processes are far more influential than the material exchange between the system and its environment. Admittedly, there have been simplifications to assign entropy values for organic materials involved in the study when the same standard entropy for lignin, hemicellulose and cellulose were chosen. Better estimates of standard entropies for these quite different compounds and also inclusion of enzymes, micro-organisms and other additives in the analysis would change the results to some moderate extent but even though, the entropy of energy flows would still remain much higher than the entropy of material flows in this study. Comparing entropy method with LCA, one interesting observation in the results is the effect of cooling in the processes. While cooling does not show itself in LCA results, it has a considerable contribution in generation of entropy. As a matter of fact, the share of cooling in entropy generation for all the three processes is approximately 28% of the total entropy generation, assuming the first scenario (24% for the second Scenario). Even though this has not affected the comparison of the three cases, such an observation reveals a potential improvement option where LCA tends to neglect. Admittedly, a simple energy balance could underscore the cooling effect in the process. However, as discussed in Section 2.5 energy balance is weaker than second law analysis in system analysis and does not reveal all the system non-idealities even though an energy balance has not been performed in this case study to confirm this. Understandably, the relative contribution of cooling in entropy generation would have reduced in a more complete study where agricultural part and other omitted activities and emissions were taken into account. Nevertheless, it would still have remained a major area of potential improvement where LCA would not seem much helpful to signal. Reflecting on the Comparison between the results of entropy (Figure 3.5) with LCA results (Figure 3.6 to 3.11), it could be inferred that entropy analysis is in the same level as weighting stage rather than characterization. Entropy analysis is not meant to predict the ecological harm of pollutants for specific impact categories. It could be seen as the predictor of the overall aggregated harms for different impact categories, just as weighting is. The difference is perhaps on the basis of evaluation. LCA weighting stage is based on subjective elements of social, political and ethical values while entropy analysis is a scientific-based method. The sound theoretical base of entropy is an advantage because it allows for a proven and tested method of calculations and comparisons [42]. On the other hand, the everlasting gap between theory and reality makes this method quite abstract and difficult to interpret in relation to current environmental concerns and social or political goals. However, further research is necessary before confidently arguing that entropy could be a measure of the overall aggregated harms of the pollutants. Waste Potential Entropy (WPE) concept suggested by Ayres and Martinas, 1994 [52] (see Section 2.2.6) is an attempt to use entropy as an eco-toxicity measure. This is an interesting idea which could be integrated with the entropy analysis suggested here in future developments of the tool to better predict the ecological hazards of processes.
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3.13 Conclusions
• The results of entropy analysis are consistent with the results of LCA weighting (Ecoindicator 99 and EPS 2000) in this case study. However, it seems too early - before further research is done - to confirm the postulate that entropy could be an ultimate measure for the environmental performance of a process on an overall, aggregated level.
• Entropy analysis results are also consistent with the cost analysis results.
Higher entropy generation corresponds to higher production costs, in this case study. Once again further investigation is necessary to analyze different aspects of such a correlation.
• Entropy analysis was able to signal opportunities for improvement potential in
this case study where LCA did not reflect. Cooling contribution to total entropy generation is considerable (almost 28% in the integrated scenario and 24% for no integration scenario).
• According to the results of this study, among the three alternative processes of
ethanol production from straw, Alt. D2b (C5, C6 fermentation by yeast) is the most efficient process. Alt. D2b (C5, C6 fermentation by thermophilic microorganism) is the second efficient process while Alt. C2 (C6 fermentation by yeast) is the least efficient among the three studied.
• Integration of the biorefinery with a CHP plant to utilize the surplus steam
from the power plant is confirmed as a wise choice by the results of this study.
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Chapter 4 Overall conclusions and recommendations 4.1 Why entropy as sustainability indicator for biorefineries?
• Biorefinery is an attractive concept. There are several problems in relation to petroleum refineries. Fossil fuels in general and petroleum in specific are in one way or another associated with global warming, limitation of resources, increasing demands for energy and materials, war etc.. There is a need for alternative sources of energy and materials especially cleaner and more sustainable technologies. There is a great potential in biomass as a source of power, fuels and materials which needs to be realized and utilized.
• Sustainability is gaining increasing attention and qualitative descriptions of the concept are often discussed. However, chemical process engineering quantitative descriptions of sustainability need to be more developed. There needs to be ways to measure sustainability and systematically evaluate, compare and improve different process technologies. This is particularly important in the emerging field of biorefineries because there are normally many different starting materials to choose from, a multitude of various pathways as well as a range of different products to aim for in a biorefinery.
• Entropy to be used as a measure of thermodynamic efficiency of a process is not a new idea. Even so, its previous applications have been mainly limited to thermal systems, heat transfer in power plants or refrigerator plants. Entropy analysis such as suggested in this report is a quite new approach where not only heat related irreversibilities but also entropy generation due to dissipation of matter and mixing are included.
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4.2 Case study: the IBUS process • Alt. D2b > Alt. D2a > Alt. C2. Results of entropy analysis, life cycle
assessment and cost analysis all point to Alt. D2b (C5, C6 fermentation by yeast) as the most favorable, Alt. D2a (C5, C6 fermentation by thermophilic micro-organism) as the second favorable and Alt. C2 (C6 fermentation by yeast) as the least favorable option among the three studied. The feedstock for all three alternatives is Danish wheat straw and the products are bioethanol, solid biofuel and animal feed.
• Integrated Biorefinery is better than a stand alone plant. Results of entropy analysis, LCA weighting and cost analysis all show that integration of the biorefinery with a CHP plant (Scenario 1) is beneficial compared to the stand alone alternative (Scenario 2). Using the surplus steam from the coal-fired power plant in the biorefinery instead of external supply of steam is shown to be a wise choice as evaluated in this thesis.
4.3 Entropy analysis compared to conventional life cycle assessment
• An Improvement potential was signaled by entropy analysis which LCA did
not raise. Cooling water for the process contributes highly in generation of entropy (28% of total entropy generation in scenario 1 and 24% in scenario 2). Admittedly, a simple energy balance might point to cooling as an important issue but energy balance is not as rigorous as entropy to describe the thermodynamic performance of a system because it does not take into account the availability of energy (refer to section 2.4 for more discussions on this).
• More entropy, more environmental load. More research is needed to postulate entropy as a measure of aggregate environmental harm potential or to interpret it in some other way, but results of the entropy analysis were consistent with the results of LCA based on two different weighting methods, Eco indicator 99 and EPS 2000. Compared with weighting methods, Entropy's pure scientific basis opens the way for rational arguments and comparisons. Besides, entropy does not rely on our knowledge of known environmental impacts and could be related to a more general level of anthropogenic disturbance.
• More entropy, more cost. In this case study results of the entropy analysis were consistent with the results of the cost analysis performed earlier. Once again, more research is recommended to explore different aspects of such a correlation and to investigate the relevance of entropy as a cost optimization tool.
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4.4 Recommendations
• Worth considering. Some interesting observations in the results of the
entropy analysis carried out suggest that further exploration and development of this tool is worth considering. Possibility of integrating entropy analysis with chemical process simulators such as Aspen Plus or ChemCad would pave the way for systematic establishment of entropy as a thermodynamic performance indicator.
• A more comprehensive future study is recommended, investigating more
different cases, covering more phases of the life cycle and more activities and emission flows. The assumptions in this study also need to be improved especially development of entropy data for organic compounds could help better approximations in future.
• Thermodynamic efficiency. One inherent advantage of entropy analysis over
methods such as LCA is that the performance of the investigated system could be compared with a thermodynamically ideal system or process. In this way, a thermodynamic efficiency could be attributed to the studied process as an indicator of the distance of the existing technology from a perfect one. Such a comparison with an ideal case is missing in this study and is recommended for future studies.
• Waste potential entropy is a concept developed by Ayres and Martinas, 1994 [52] and suggested as an ultimate eco-toxicity measure (refer to section 2.2.6 for more discussions on their work). Further research is recommended to possibly integrate this concept with the entropy analysis discussed in this thesis in order to improve the method for better evaluation of eco-toxicity.
• A link between second law analysis and life cycle assessment was suggested by Wagendorp et al., 2006 [109] for quantitative evaluation of land use. Such studies encourage the capability of the second law of thermodynamics as a basis for evaluation. Further research along this line, to investigate the applicability of entropy in improving other evaluation tools especially LCA is recommended.
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Acknowledgement First of all, I would like to express my warmest gratitude to my supervisor Dr. Morgan Fröling for his persistent guidance throughout the project. His interest in the topic and willingness to help was a great encouragement and motive for me and meetings with him were always helpful and comfortable. I would also like to thank professor Tobias Richards for his consultation meetings which were greatly helpful. Many thanks to Eric Sette, Kristian Hansen Gustafsson, Kerstin Oom, Clara Tholin, Emma Söderström and Isabelle Johansson for the insightful seminars that we had together. I am also grateful to Mohammad Raza Naqvi, Rikard Arvidsson and Sara Persson for their comments and corrections on the draft report. The co-operation of Mr. Christian Morgen from Dong Energy to answer some questions via e-mail is also acknowledged. The useful comments made by Professor Peter Ahlström on the report were highly useful and are much appreciated. I am also grateful to Professor Mohammad Taherzadeh for reading the report and the interesting discussion on the presentation seminar. Finally I would like to thank Syed Muzzafar Ali for introducing me to this project and to Dr. Morgan Fröling.
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