Energy efficiency optimization of air supply system in a water bottle manufacturing system

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Journal of Cleaner Production xxx (2014) 1e12

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Journal of Cleaner Production

journal homepage: www.elsevier .com/locate/ jc lepro

Energy efficiency optimization of air supply system in a water bottlemanufacturing system

Vukica Jovanovic a,*, Branislav Stevanov b, Dragan �Se�slija b, Slobodan Dudi�c b,Zdravko Te�si�c b

aDepartment of Engineering Technology, 1 Old Dominion University, 214 Kaufman Hall, Norfolk, VA 23529, USAbDepartment for Industrial Engineering and Management, Faculty of Technical Sciences, University of Novi Sad, Serbia

a r t i c l e i n f o

Article history:Received 26 January 2013Received in revised form18 February 2014Accepted 7 March 2014Available online xxx

Keywords:Energy efficiencyGreen manufacturingOptimization

* Corresponding author. Tel.: þ1 757 683 3769; faxE-mail addresses: [email protected],

(V. Jovanovic), [email protected] (B. Stevanov), [email protected] (S. Dudi�c), [email protected]

http://dx.doi.org/10.1016/j.jclepro.2014.03.0210959-6526/� 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Jovanovisystem, Journal of Cleaner Production (2014

a b s t r a c t

This paper presents the use of green manufacturing methods for optimization of energy efficiency of aircompressors. The method developed during this research is applied to one industry problem in a facilitythat fills polyethylene terephthalate (PET) bottles with mineral water. Bottle manufacturing systems inlarge beverage production companies often have multiple PET bottle production lines. A common issuein these lines is failure of one of the compressors used for a machine in such manufacturing systems.These equipment failures prevent companies from manufacturing their products until the compressorand its line get repaired. In this way, companies are losing significant financial resources. Therefore, inthis paper, one possible solution for such a problem is given. The main goal of the proposed optimizationsolution is focused on the improvement of system reliability and higher energy efficiency. This wasachieved by the replacement of previous, multiple compressors of various capacities with one multiplecompressor station with a larger, optimized capacity, customized for a specific solution. In addition,another goal was to establish a platform for a design retrofit in a company with a limited budget. As aresult of this research, a suggestion was given that high-pressure compressors, linked to four individuallines for blowing PET bottles, were linked to one high-pressure tank. In this way, the reliability of thesystem was increased. During this research, discrete event simulation methods were used to verify aproposed solution and to assist researchers in determining an optimal design of an air supply system. Thesolution given was based on a suitable combination of PET bottle blowers work cycles, which wouldbetter meet the total demand for compressed air based on methods related to robust design and Designof Experiments (DoE).

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Various companies are implementing green initiatives, whichare assisting them in gaining new competitive advantages on themarket, building an environmentally conscious public image,exhibiting product stewardship, as well as fulfilling regulatory re-quirements, while also establishing green supply chains (Chang,2011; Jayaraman et al., 2012; Routroy, 2009; Rusinko, 2007;Shang et al., 2010). Many modern companies are using activity-based-costing (ABC) methods to justify capital investmentsrelated to the development of green manufacturing systems (Tsai

: þ1 757 683 [email protected]@uns.ac.rs (D. �Se�slija),.rs (Z. Te�si�c).

c, V., et al., Energy efficiency), http://dx.doi.org/10.1016/j.

et al., 2011). Development and implementation of greenmanufacturing methods is related to various external and internalfactors, specific to different businesses (Deif, 2011). In addition,companies are always searching for ways to use as few resources aspossible to get the job done with full consideration of environ-mental impact of products and processes (Gavronski et al., 2011; Liet al., 2010). One way of achieving this is through green productdevelopment, while other companies focus on more sustainable,green manufacturing processes within a company (Bangert, 2012;Strano et al., 2013).

Recent environmental regulations are posing pressure onmanufacturers to comply with environmental regulations and di-rectives. One such directive is The Packaging Directive in EuropeanUnion, focused on packaging waste minimization, recycling andenergy recovery (Hanssen et al., 2003). For this purpose, the topic ofair supply efficiency has recently become a focus of many research

optimization of air supply system in a water bottle manufacturingjclepro.2014.03.021

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Table 1Fractional factorial orthogonal arrays for compressors.

Compressors Run (KXY)

1 1 0 0 1 1 1 1 1 1 0 0 0 1 0 0 02 1 0 1 0 1 1 1 0 0 1 1 0 0 1 0 03 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 04 1 0 1 1 1 0 0 0 1 0 1 1 0 0 0 1

V. Jovanovic et al. / Journal of Cleaner Production xxx (2014) 1e122

projects, especially with the development of modern mechatronicmeasurement systems, which can retrieve substantial informationabout the leaks and possible system inefficiencies. Especially indeveloping countries, the possibility of designing completely newpneumatic systems is not always an option. For that purpose,retrofit solutions, remanufacturing and energy efficiency im-provements are a very important part of green manufacturing ini-tiatives (Du et al., 2013). Some researchers are proposing ways forgreen accounting initiatives to justify capital budgeting decisions(Farouk et al., 2012).

Some of the reasons for wide application of pneumatic systemsin industry are: widely available and abundant resources, longlifespan of components, good environmental compliance, efficientwork in hazardous environments, and high speed of actuators anddevices, which are controlled with pneumatics. Therefore, com-pressed air has become one of the common forms of energy widelyused in a spectrum of different industries. It is being used in civilengineering and mining, as well as in machining systems, handtools, control systems, foundries, rolling mills, rubber and cablemanufacturing, assembly systems, food, chemical and pharma-ceutical industries. Although air is an abundant resource, air supplysystems still need energy to run all valves and actuators in auto-mation systems. Innovative ways are needed for different im-provements that would enable companies to run their processesmore efficiently and with better scheduling (He et al., 2008).

Many companies that manufacture compressors providedifferent tools and software to assist companies in their planningand optimization of bottle manufacturing systems. However,various companies around the world still use compressors that arenot made by a single company. Some smaller companies try toretrofit compressors that were used for other manufacturing lines.They do not necessarily purchase a new compressor for everymanufacturing line. Industrial standard dimensioning of com-pressed air systems given in this paper are a new approach becausethe manufacturing system that was observed during this study wasnot an ordinary compressed air system. This kind of system wouldoperate on pressures between 6 and 8-bar, and is intended forgeneral consumers. The manufacturing system that was optimizedand analyzed during this study was a specific compressed air sys-tem intended solely for high-pressure compressed air consumers(PET Blowers). Its operating pressure ranged from 32 and up to 40-bar. To replace multiple compressors with a single compressorstation for multiple PET blower lines is a difficult task because, ingeneral, compressors are from different manufacturers, each onewith their own control systems, and with specific energy efficiencycharacteristics. Hence, choosing the right combination of existingcompressors in order to better control production of compressedair, and to improve reliability and energy efficiency, was a challengethat needed careful planning, calculations, and discrete eventsimulation.

Large beverage manufacturers usually have multiple PET bottlemanufacturing lines. Frequently they have to stop production andlose money because of the compressor’s failure. In addition, theyare not using existing compressors efficiently because of thechanges in the production lines. The design of plastic bottles isconstantly changing. Current trends to mineralization and wastereduction are leading to changes in shape. Manufacturers that havelines that were retrofitted and redesigned often face this problem.Many of them do not have financial resources to purchase softwarebought from multiple compressor manufacturers, or even to buynewer control equipment. With this in mind, using what theycurrently have, and developing new optimization techniques, is stilla need of many companies.

This paper presents a new green manufacturing platform formore efficient management of air supply in pneumatic systems in

Please cite this article in press as: Jovanovic, V., et al., Energy efficiencysystem, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.

industry. Themodel presented in this paper has been applied to oneindustry problem focusing on various planning activities done on amodel of a system to find the optimal solution in digital environ-ments with discrete event simulation methods to verify relatedcalculations. The main idea was to design an improvedmanufacturing system that wouldmanufacture a given product in amore eco-efficient way by identifying existing inefficiencies anddeveloping a mathematical model for further optimization andenergy efficiency improvement (Balogun and Mativenga, 2013;Sangwan, 2006). Various planning stages related to identificationof energy inefficiencies of compressors are explained in this paper(Deif, 2011). In this way, a leaner, greener manufacturing systemhas been developed.

2. Bottled water manufacturing and consumption

Total consumption of bottled water is continually increasing dueto recent findings related to possible pollutants that might damagehuman health, as well as increased customer concerns about thequality of water. Many customers are willing to pay more just tomake sure that the water they drink is safe (Vásquez et al., 2009).Other reasons are related to lifestyle changes (Dotsika et al., 2010)bottled water availability at different locations, and the purchasingpower of consumers (Lagioia et al., 2012). Some researchers suggestthat overall consumption of bottled water has increased by seventypercent from 2001 until 2007 (Gleick and Cooley, 2009). This in-crease might also be related to very efficient advertising (Lagioiaet al., 2012; Niccolucci et al., 2011), mostly in Europe and theUnited States of America, as shown in Table 1.

Product lifecycle costs of bottled water include all energyneeded for manufacturing of PET bottles, including processing thewater, packing it into bottles, sealing bottles, transporting them tocustomers, and maintaining cool temperatures before it is sold andconsumed. Research studies have shown that this process needssomewhere between 5,600,000 and 10,200,000 J of energy per 1 Lwhich is 2000 times more than the energy needed to produce 1 L oftap water: 5000 J of energy per 1 L (Gleick and Cooley, 2009). En-ergy being used to produce bottled water is 0.33 percent of overallenergy consumption in the U.S. The majority of the bottles beingproduced are made from virgin polyethylene terephthalate (PET).The largest percentage of energy consumption for producingbottled water is used for bottle manufacturing and transportation,not for water processing, packaging, and storage at cool tempera-tures (Gleick and Cooley, 2009; Pasqualino et al., 2011). Manybottling companies are implementing ISO 14001, but there are stillmany unconformities being identified by various researchers(Djekic and Smigic, 2013). Hence, an optimization method of the airsupply requirements of a bottle manufacturing system is veryimportant for the overall environmental footprint of the lifecycle ofbottled water.

2.1. Energy efficiency of pneumatic systems

Pneumatic systems have good speed, low rate of explosions,easy regulation of speeds and forces, reliability, and longer life-cycles. However, compressed air is not something that is free of


Fig. 1. The most important costs related to compressed air production in industry.

Fig. 2. Reasons for green manufacturing of air supply system (Dudic, 2012).

V. Jovanovic et al. / Journal of Cleaner Production xxx (2014) 1e12 3

costs. Energy costs contribute to 75% of the total costs for com-pressed air production (Dudic et al., 2012). Air is free but onlybefore the compression phasewhen it becomes a fluid used toworkin the manufacturing system. From that moment, any compressedair has its own operation and distribution costs. It is still one of themost expensive sources of energy in manufacturing systems(Thiede, 2012). Its overall lifecycle cost is also related to in-vestments of equipment and system maintenance. Still, it com-prises the fourth most commonly used form of energy afterelectricity, natural gas, and water. The lifecycle costs of compressedair are given in Fig. 1. The biggest percentage of costs associatedwith the use of compressed air in manufacturing systems arerelated to the use of electrical energy, followed by initial in-vestments costs and maintenance.

The problem is that in many manufacturing systems only aportion of the overall compressed air is being used. Around 10% oftotal industrial energy consumption is used to generate com-pressed air in manufacturing systems, which makes compressedair one of the most expensive forms of energy in the industry(Saidur et al., 2010; Thiede, 2012). Annual operating costs of aircompressors, dryers, and supporting equipment can account for70%e90% of the total electric bill (Saidur et al., 2010). Only about10e20% of total input energy is utilized for useful work incompressed-air systems.

Hence, a need for an optimization of air supply in pneumaticsystems is apparent. New green manufacturing methods have to bedeveloped to assist planning engineers in saving their companyresources and enabling more energy efficient production (Dudic,2012). Green manufacturing methods are important because: re-ductions in cost and used energy can be driven by identifyingexisting inefficiencies in the system; the method will improvereliability and performance of the whole pneumatic system;reducing environmental footprint with less use of electrical energyduring the process of compressing air, as shown in Fig. 2.

The high cost of use of pneumatic systems in industry is related tosome basic properties of air as a fluid that is being used in pneumaticsystems (Dudic, 2012). Some of these are: air compressibility, im-purities in compressed air, poor lubricating properties of pure air,high heat transferability, and noise, as shown in Fig. 3.

� Air compressibility: To compress the air in the system, long cyclesof compressors are needed. During the compression phase, a

Fig. 3. Factors related to high cost of pneumatic systems in industry (Dudic, 2012).


great amount of energy is converted to thermal energy which isbasically an energy loss in the system (Dudic, 2012). For thisreason, compressors are basically machines with low rates ofenergy efficiency. Pneumatic actuators can be used with lowerforces up to 30 kN. In addition, generated heat causes anotherproblem related to lubrication, which causes corrosion andmaterial wear in the system and also causes higher tempera-tures in the compressor.

� Impurities in compressed air: Air that is used and compressed inits virgin mode has impurities and humidity, causing corrosionand wear of pneumatic components, and has to be purified.

� Poor lubricating properties of pure air: Pneumatic components(compressors, actuators and other components) that are used inmanufacturing system have to be lubricated.

� Poor heat transferability: Because of its poor convection (cooling)properties, additional cooling is needed in pneumatic systems.

� Noise: Once it exits the pneumatic system, compressed air isvery loud so filters made of porous materials have to be used(outlets).

2.2. Problem of an unbalanced capacity in an air supply system

Many pneumatic production systems have various compressors.The most common characteristics of a compressor are demand of71 kW and operation of 3500 h per year (Thiede, 2012). Theproblem in the system capacity planning might be caused by theenergy efficiency of the overall system not being fully determined.Very frequently, the main concern is related to development ofautomation systems, which would provide the highestmanufacturing output possible. However, some manufacturingsystems are not always operating at their full capacity or theymightbe used for different beverages. On many occasions, these auto-mation systems could control separate air supply units and mightnot necessarily be interconnected in one Computer IntegratedManufacturing system. Various reasons might influence decisionmaking for more efficient use of air supply systems and their futureintegration. Some of these reasons are needed for easier systembalancing and system maintenance.

The greatest influence on overall energy efficiency of onepneumatic system is related to energy losses during themanufacturing, distribution and consumption phases. One of themost important of these is thermal loss during the compression

Fig. 4. Factors related to energy efficiency of compressors in pneumatic system.


Fig. 5. Problems in optimal compressor design (Long et al., 2009).


stage. In addition to these, other very important factors related tothe compression stage are: location of compressors in the pneu-matic system, energy used for compression, lubrication process,compressor choice, and control process of compressor’s use in thesystem, as given in Fig. 4. For that purpose, the work presented inthis paper is focused on a simulation model used to determinebetter usage of existing compressors in the system throughdeveloped control model.

Multi-compressor design optimization assesses the pneumaticsystem and focuses on energy efficiency improvement to result inmore efficient, less costly manufacturing systems that can savecompanies substantial resources and reduce overall greenhouse gasemissions of a company, resulting in a better overall environmentalfootprint and improved image. The optimization method suggestedin this research is focused on the following stages: capacity vali-dation of all compressors that are used in a manufacturing system;compressor design optimization; modeling of the overall air supplycapacity; and evaluation of multiple compressors at a stationthrough simulation, as given in Fig. 5 (Long et al., 2009).

3. Methodology for water bottle production systemoptimization

For a case study, the authors have chosen a PET bottling facilityfor mineral water production. This facility consists of four high-

Fig. 6. Compressors and blowers in series configuration.


pressure compressors (ABC, CE46, B&M i CE24 A), each of themdirectly connected to the one of the PET bottle blowers (SBO 10,BLOMAX, SBO 12 i SIPA). The compressors in this manufacturingsystem were used in a parallel configuration. Operators had tomanually turn them off and on from four different locations in themanufacturing system. Blowers were attached to each one of thecompressors separately. This kind of pneumatic system was chal-lenged by a problem related to the reliability of its air supply. Afailure of one compressor could cause a whole bottle blowing lineto shut down. In addition, compressors do not necessarily work inan energy-efficient way; sometimes their work consumes toomuchelectrical energy.

3.1. Compressors capacity identification

The initial design of pneumatic system optimization, during thisresearch, was that each one of the four compressors could beattached to a single reservoir. In this way, a multi compressor sta-tion would be formed so that any system imbalance could bemonitored and controlled in a given pressure range (air pressureneeded by PET blowers for successful bottle production). The firststep was to map out an existing manufacturing system and un-derstand everything about the current air supply and demand, aswell as to map out all existing system inefficiencies and problems.

The main variable used for mathematical modeling of a systemis time, measured in seconds. The problem is to identify the most

Fig. 7. Proposed change in the air supply system.


Fig. 8. Model validation procedure based on model-based experiment design tech-niques (Franceschini and Macchietto, 2008).

Table 2Fractional factorial orthogonal arrays or blowers.

Blowers Run (DXY)

1 1 0 0 1 1 1 1 1 1 0 0 0 1 0 0 02 1 0 1 0 1 1 1 0 0 1 1 0 0 1 0 03 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 04 1 0 1 1 1 0 0 0 1 0 1 1 0 0 0 1


optimal pattern of when compressors should be operating for agiven demand to manufacture PET bottles.

tcn ¼ f ðQPETÞ (1)

tcn ¼ time in which compressor is in compression phase;QPET ¼ needed quantity of PET bottles that needs to be manufac-tured; n ¼ compressor number in the system.

The main limitation given is a pressure range that has to beachieved in a system for successful blower operation; theminimumand maximum pressure needed to blow one PET bottle.

ps ¼ ðminbp�maxbpÞ (2)

minbp ¼ minimum pressure needed to blow PET bottles;maxbp ¼ maximum pressure needed to blow PET bottles.

It is important to develop an optimal model for all compressorsand all blowers in the system to achieve its given pressure goal. Forany given moment during the production run, it is important toknowwhich compressors and which blowers are on, the current airsupply pressure level, and the current demand need.

The main goal of this optimization model is to reduce overalltime that compressors are compressing air while maintaining agiven pressure level and meeting a demand of blowers of PETbottles in the system. During the research, a limitation given for theneeded pressure in the system was set to be around 39-bar, with atolerance of plus or minus 1 bar.

pgoal ¼ pplanned � ptolerance ¼ 39 bar� 1 bar (3)

The main physical quantity that was observed in this model wasthe airflow per second (for compressors and for the blowers),measured in bar per seconds. It was assumed that the airflow wasconstant for the whole pneumatic system. The main variable in thismodel was the time that the compressors were compressing air.The idea was that the total time that compressors were

Fig. 9. Chart used for mapp


compressing air was as small as possible for a given pressure level,so that blowers could work.

tcn ¼ time in which given compressor is ontbn ¼ time in which given blower is onn ¼ 1, 2, ., n

The initial solution analyzed in this manufacturing system,during this study, was the configuration of compressors andblowers as shown in Fig. 6. Proposed improvement is given in Fig. 7.

To determine the optimal manufacturing system design, it isimportant to determine the capacity of each compressor andblower used in the pneumatic system. In this case study, an airpressure increase per 1 s was determined as a variable that woulddefine the capacity of different components that were evaluated inthis optimization study. Modern software modeling tools, such asdiscrete event simulation methods, are providing further capabilityfor more reliable optimization methods of energy efficiency ofpneumatic systems.

3.2. Compressor utilization optimization: different configurations

The main calculation in the mathematical model of thismanufacturing system was focused on determining a pressure in-crease in the reservoir that would be connected to all four compres-sors and all four blowers. Themain assumption is that the pressure inthe reservoir would rise after any combination of four existing com-pressors would operate. The pressure increase is monitored andcalculated in bar per second. TheDesignof Experiment (DoE)methodwasused todetermine all possible combinations. Thedevelopmentofmathematical models based on experiment design method, such asDesign of Experiments (DoE), is used as one of the basic tools inprocess engineering for process planning and validation, as shown inFig. 8 (Franceschini and Macchietto, 2008).

Mathematical modeling usually explains different physical pro-cesses that are happening in manufacturing systems. It is significantto map out all important variables in the process and identify whichvariables are crucial for understanding the variable that we try tooptimize; in this case the time in which these compressors areoperating in order to feed necessary compressed air to blow thesePET bottles in this manufacturing system. For this purpose, differentcompressors can be turned on or off during all these time periods.

This system had two different fractional factorial orthogonalarrays (Fowlkes and Creveling, 1995), one for the four compressorsand one for the four blowers. Each compressor could be in twodifferent states, on and off, and each blower could also be in twodifferent states (levels), on and off. For each one of these twoorthogonal arrays, different runs are shown in Tables 4 and 5.

LaðbcÞ ¼ L4�216

�(4)

a ¼ the number of experimental runs; b ¼ the number of levels foreach factor; c ¼ the number of columns in each array.

ing blowers’ schedule.


Table 3Table used to identify a need for compressors’ schedule for a given time period.

Period t -

Blowers SBO 10 BLOMAX SBO 12 SIPA �S DpD - -

Compressors ABC CE46 B B&M CE24 A þ S DpK - -

DpPeriod [s]Total Dpu

Table 4Air pressure changes in the bottled water manufacturing system.

Air flow

Supply Consumption Difference

Discharge rate [bar/s] Suction rate [bar/s] [bar/s] [bar/h] [bar/year]

Compressor 1 0.03245 Blower 1 0.02985 0.0026 9.3672 17,212.23Compressor 2 0.03575 Blower 2 0.03555 0.0002 0.7164 1316.39Compressor 3 0.04875 Blower 3 0.03983 0.00893 32.1336 59045.49Compressor 4 0.01381 Blower 4 0.01284 0.00097 3.5064 6443.01Total 0.13077 Total 0.11807 Total 45.7236 84017.11

Table 5Table used to identify a need for compressors schedule for a given time period.

Period t1

Blowers SBO 10 BLOMAX SBO 12 SIPA � S DpD 32 U U U 0.082518Compressors ABC CE46 B B&M CE24 A þ S DpK 24 U U 0.084570

Dp 0.001989Period [s] 300Total Dpu 0.5967


Run 1 (Table 1) is a situation in which all four compressors areon (K41) and Run 2 is a situation in which all four compressors areoff (K40). Runs 3, 4, 5 and 6 (Table 4) are different combinationswhen three compressors are working (K3n), Runs 7 to 12 (Table 4)are combinations in which 2 compressors are working (K2n). Thelast four runs are combinations in which only one compressor isworking (K1n). A similar strategy is used for blowers in Table 2.

KXY ¼ combinations of compressors which are onDXY ¼ combinations of blowers which are onX¼ number of compressors or blowers which are on (0,1, 2, 3, 4)Y ¼ sequence number of given combinations (1, 2, 3, 4, 5, 6).

Initial data needed for the optimization includes mapping out ademand schedule and when and how long each blower would needto work. Based on a production schedule, data has to be collectedand entered into a chart, such as the one shown in Fig. 9, for a giventime period (in this case 20 min was chosen).

After the initial data is collected, the whole period should bebroken down into smaller periods. For each one of these periods, acombination of blowers had to be identified, as well as a similarcombination of compressors, which would result in the closestpressure change per second, shown in Table 3.

After the schedule has been mapped out, it is then broken intosmaller time periods in which blowers work in one of the previ-ously identified combinations (DXY), given in Table 5 (one of theruns). For each one of the new time periods the difference betweenpressure increase and pressure decrease is then identified. All thishas to focus on enabling the pneumatic system with given limita-tions related to blowers and their needed pressure for efficient

Fig. 10. Schedule for given tim


operation. In this case, the pressure had to be between 38 and40 bar. This is an initial solution that can be further optimized withdiscrete event simulations. The difference in the pressures is givenwith Dpm and total difference in pressure is given with Dpu.

4. Results: optimal multi compressor design

Compressed air is a continuous variable. In order to use thediscrete event simulation approach, pressure was observed pereach second. In this way, pressure value over time was divided intosmall chunks of 1 s. The observed parameter was pressure. Com-pressors were adding pressure in the system per second and PETblowers were removing pressure from the system per second. Dataused for verification of a proposed method was retrieved from thecompany Knjaz Milos in Arandjelovac, Serbia.

4.1. Air supply capacity modeling

The proposed solution in this research was to disconnect com-pressors from blowers and to connect all machines to a high-pressure tank; all for the purpose of reducing existing problemsrelated to the reliable and energy efficient air supply. The com-pressed air that occurs as a result of the work of the compressorscan be stored in the tank fromwhere it is distributed to the blowersand used for the production of PET bottles. With a tank, the failureof the compressor means it no longer stops the appropriate blower,and the work of the compressors can be combined in an energyefficient way to meet the total demand for compressed air. In orderto support this decision, a simulation model is built to simulate thework of the facility. This data gives information about availablesupply and consumption in a manufacturing system presented inthe paper and given in Table 4. If all compressors and all blowerswould work 7.5 h each of the 245 working days per year, the dif-ference between supply and consumptionwould be 84017.11 bar ofcompressed air.

A newly proposed, multiple compressor station has beendeveloped during this research and evaluated through differentscenarios through use of discrete event simulation methods.Different combinations of compressors have been used for calcu-lation of possible combinations. There were 16 combinations total.In addition, 16 various combinations of uses for blowers were alsoincluded for calculation.

e period for each blower.


Fig. 11. Schedule for given time period for each compressor.

38.95

39

39.05

39.1

39.15

0 200 400 600 800 1000 1200 1400

p (bar)

t (sec)

CE 46 Compressor

Fig. 13. Simulation of CE 46 compressor operation for given production plan.


Data about working cycles for each blower are then identifiedfor each separate blower, as shown in the 20-min example schedulegiven in a chart on Fig. 10. The next step was to divide the diagraminto periods in which blowers are working in the same combina-tion. In the example shown in Fig. 10, blowers work in five differenttime periods. After that, a combination of working blowers (DXY)was identified, and the pressure was reduced by 1 s. In addition,each combination of blowers was matched with the optimal com-bination of compressors that would result in a similar pressurechange.

One example of an elected combination of compressors KXY forone of the five periods (D32 and K24) is shown in the diagram inTable 5.

A selected combination of compressors KXY for each of the fiveperiods of time is shown in the diagram in Fig. 11. This scheduleserved as an initial model that was used for discrete event simu-lation and further optimization.

4.1.1. Baseline e simulation of existing caseA starting point in this investigationwas an initial state inwhich

compressors were connected directly to the PET blowers and theircorresponding lines. Discrete event simulationwas competed for ina production plan of blowers, given in Fig. 14. For the simulationpurposes, the same conditions were taken as in the latter case withthe added receiver of 10 m3. In the practice, compressors that wereconnected directly to blowers have significantly smaller receivers.This leads to an additional problem related to their control. It iscaused by a necessity to take into account a limited number ofcompressors switching on or off. That problem can be solved withcontrol techniques that do not allow compressors to switch offcompletely. Usually, they stay in free motion with no flow pro-duction or a production of 50% of the nominal flow. This way ofoperating compressors brings additional energy consumption.Nevertheless, in order to investigate only the impact of the pro-posed solution, simulation is done with such prerequisites andresults are given in Figs. 12e15.

As a result of connecting all of the compressors on one jointreceiver, various combinations of compressors can satisfy requireddemands, as shown in Table 6.

37.5

38

38.5

39

39.5

40

40.5

0 200 400 600 800 1000 1200 1400

p (bar)

t (sec)

ABC Compressor

Fig. 12. Simulation of ABC compressor operation for given production plan.


Based on a given criteria for matching compressor combinationsto required combinations of blowers (first combinationwith higherproduction than actual consumption), several combinations ofcompressors were eliminated. For example, the D23 combination ofblowers requires, at least, 0.04269 bar/s of compressed air pro-duction in order not to lose pressure in the tank. For this pressure,the first compressor combination that is suitable is K23. Thatcombination is also suitable for the D13 combination of blowers.Hence, the K13 combinationwas not useful. According to this, threecombinations of compressors were left. Table 7 shows an adequatematching for the first round of optimization.

According to this table, for the given production plan of blowersin Fig. 11, schedule of compressors production is given in Fig. 16.Simulation is done with the same prerequisite as in the case of first,existing solution and obtained diagram of pressure change in thereservoir is given in Fig. 16.

Electric power consumption for this solution is given in Fig. 17.Total consumption was kW/h 182.885.

4.1.2. Improving initial solution with energy efficiency criteriaFurthermore, after the introduction of energy efficiency criteria,

the matching combination of blower operations with adequatecompressor combinations has been changed. Namely, if the actualelectric energy consumption of compressors is taken into consid-eration (Table 8), some remarks must be given.

37.5

38

38.5

39

39.5

40

40.5

0 200 400 600 800 1000 1200 1400

p (bar)

t (sec)

B & M Compressor

Fig. 14. Simulation of B&M compressor operation for given production plan.


38.93939.139.239.339.439.539.639.739.839.9

0 200 400 600 800 1000 1200 1400

p (bar)

t (sec)

CE24 Compressor

Fig. 15. Simulation of CE24 compressor operation for given production plan.

Table 7Matching compressors and blowers combinations e corrected.

Compressorcombination

Production ofcompressors[bar/s]

Matching Consumptionof blowers[bar/s]

Blowerscombination

K41 0.130773 ) 0.118072 D41K34 0.116961 ) 0.105234 D34K32 0.09502 ) 0.08822 D31K24 0.084507 ) 0.082518 D32K22 0.081208 ) 0.078244 D33

) 0.075382 D24) 0.06968 D22

K21 0.068207 ) 0.065406 D21K26 0.062566 ) 0.052666 D26K13 0.048754 ) 0.048392 D25K23 0.046266 ) 0.04269 D23

) 0.039828 D13K12 0.035753 ) 0.035554 D12K11 0.032454 ) 0.029852 D11K14 0.013812 ) 0.012838 D14K40 0 ) 0 D40


It can be seen that compressors ABC and CE46 B have the samenominal power (250 kW), but they differ in capacity. CompressorCE46 B, for the same electric power consumed, produces more flowof compressed air than compressor ABC. A practical consequence ofthis fact is that each combination with compressor ABC, where it ispossible, should be replaced with the combination where thecompressor is CE46B. For example, combination K32 should bereplaced with K31, etc. So, in this way, combinations K32, K22, K23and K11 have been deleted from the list of possible combinations.Also, as the B&M compressor is the most energy efficient one,combinations where it has been included are preferred. Forexample, combination K13 (only B&M operating) has just a little bitsmaller capacity for increasing the pressure (0.48754 bar/s) thancombination K25 (0.49565) but it uses 90 kW less electric power.Finally, combination K33, among three compressors, employs twoleast efficient compressors (CE24 A and ABC), so it will be changedwith combination K24, which has a bigger flow, with 90 kW lesspower employed. This analysis matching is given in Table 9.

New simulation has been done that applies matching fromTable 8 with chosen combinations of compressors for given order ofblower operation in Table 9 and the results in Tables 10 and 11.

4.2. Evaluation of a multiple compressors station

Evaluation of a proposed solution was done with the use ofDiscrete Event Simulation (DES). DES of manufacturing systems,which focuses on the important energy flows, was identified as apromising approach, but commercially available manufacturing

Table 6Matching compressors and blowers combinations.

Compressorcombination

Production ofcompressors[bar/s]

Matching Consumptionof blowers[bar/s]

Blowerscombination

K41 0.130773 ) 0.118072 D41K34 0.116961 ) 0.105234 D34K31 0.098319 ) 0.08822 D31K32 0.09502 ) 0.082518 D32K24 0.084507 ) 0.078244 D33K33 0.082019 ) 0.075382 D24K22 0.081208 ) 0.06968 D22K21 0.068207 ) 0.065406 D21K26 0.062566 ) 0.052666 D26K25 0.049565 ) 0.048392 D25K13 0.048754 ) 0.04269 D23K23 0.046266 ) 0.039828 D13K12 0.035753 ) 0.035554 D12K11 0.032454 ) 0.029852 D11K14 0.013812 ) 0.012838 D14K40 0 ) 0 D40


simulation tools do not support those considerations yet(Herrmann et al., 2011). The simulation was based on the timecontrol of blowers and compressors, and it was done with RockwellAutomation discrete simulation ARENA� software. The control ofthe compressors’ work was determined by work time calculation.By performing simulation, the process parameters were adjusted sothat the work of compressors was redefined to take into accountthe fact that the compressors can be off up to six times within 1 h,e.g. twice within 20 min during the simulation. The simulationmodel defines the process of increasing the air pressure in the tankas a result of the work of compressors and the process of reducingthe air pressure in the tank as a result of the work of blowers. Thesimulation model was built from multiple modules, which repre-sent the four compressors, the tank and four blowers. Input data forcompressors and blowers are the lengths of work time in seconds.Value, which was considered as the referent value, was the airpressure in the tank. The variables are the work times of thecompressors and blowers, as well as the pressure in the tank. Thebasic scheme of the model is given in Fig. 18.

The final schedule for the optimal compressors work is shown inthe time diagram in Fig. 19.

The initial pressure in the tank is 39 bar. The objective functionis to reduce total compressed air production in accordance with airconsumption of the blowers. The main limitation is that the pres-sure in the tank ranges from 38 to 40 bar. Fig. 20 shows the diagramof the pressure change in the tank. The pressure level satisfies theinitial restrictions; it is in the range of 38e40 bar, so with the de-cision support provided by the simulation, there is an optimal so-lution for given work schedule blowers. In this way, compressorsare working just in time; they are creating just enough compressedair as the system needs at a given moment. In this way, a new green

39.00

39.6040.00

38.06

39.5540.00

38.04

39.99

38.04

38.46

38.98

39.93

38.07

38.35

37.5038.0038.5039.0039.5040.0040.50

-100 100 300 500 700 900 1100 1300

p (bar)

t (sec)

Reservoir Pressure Change

Fig. 16. Simulation of pressure change in reservoir for initial solution for the givenproduction plan.


45.17

11.05

0.00

41.00

5.87

0.00

25.44

0.005.42

13.55

27.90

0.00

7.49

0.005.0010.0015.0020.0025.0030.0035.0040.0045.0050.00

0-300

300-359

359-381

381-600

600-639

639-667

667-836

836-864

864-900

900-990

990-1139

1139-1160

1160-1200

K24 K32 K32 K32 K22 K22 K22 K22 K22 K22 K32 K32 K32

Usage

Time / Combination of Compressors

Electrical Energy Consumption for Compressors (kWh)

Fig. 17. Electric power consumption for initial solution.


and lean manufacturing air supply system has been redesigned(Fig. 21).

4.2.1. ReliabilityOne of the key tenets of leanmanufacturing philosophy includes

process stability in the sense of establishing processes that combinemen, machine, and materials to produce 100% quality productswhen they are needed to satisfy customer demand. This involvesattaining demanding standards in equipment reliability, raw ma-terial and purchased parts quality, employee knowledge and skills,and production quality control (Detty and Yingling, 2000). So,improving equipment reliability is a step in the direction of good,lean manufacturing practices. With the proposed solution, even for14 out of 16 possible cases of blowers’ operations, it is possible tomaintain regular operations when one of the compressors is out offunction. Only in the most demanding cases (D41 and D34) con-cerning flow of the compressed air is it not possible to satisfy de-mand in the sense of not losing pressure in a reservoir.

However, if we deeply consider those two cases, it is possible toimprove the operation of production systems even in these cases.Namely, in a case of the D41 blower combination (highest demand)only the K41 compressor combination can satisfy demand. In thatcase, one of the compressors is currently out of production due tosome temporarily problems, and the D41 combination cannot besatisfied according to the proposed criteria that compressorsshould supply more compressed air than the actual demand. Forexample, if compressor CE24 is temporarily out, the K41 combi-nation is not possible, but compressors are operating as in

Table 8Compressor characteristics.

Compressor Nominalelectricalpower, kW

Nominalflow,Nm3/h

Nominalflow per1 kW ofpower

Pressure increaserate for theconsidered 10 m3

reservoir, bar/s

ABC 250 1198 4.792 0.03245CE46 B 250 1320 5.280 0.03575B&M 292 1800 6.164 0.04875CE24 A 132 510 3.864 0.01381


combination K34. In that case, the difference between demand andproduction is �0.001111 bar/s. If failure of the compressorhappenedwhen the pressure in the reservoir was at the upper limit(40 bar), operating in these conditions will leave 30 min time untilreaching the lower limit of pressure (38 bar). During this period it ispossible to change fuses, or do some minor repairs on thiscompressor, and to not go out of the regular way of blower oper-ations. This time period varies between 0 and 30min, depending onthe pressure in the reservoir when the failure of the compressorhappened.

For the second case considered, the blower combination D34,with failure of one compressor, is in a similar situation. If, forexample, compressor ABC is out, the K34 combination is notpossible and, instead, the compressors should work in the K31combination. The difference for this case is �0.006915 bar/s, andthat will allowmaintenance people an interval between 0 and 289sto do inspections and eventually make some quick repairs. So, evenin the worst-case scenario, with the proposed solution, reliability issignificantly improved.

4.2.2. Energy efficiency of proposed solutionThe obtained results clearly indicate an increase in energy effi-

ciency. That increase is obvious even in the first case when specialattention was not paid to the increase in energy efficiency(Table 12). That means that by simply connecting the compressorsto a joint reservoir, energy efficiency is improved, along with anincrease in reliability.

Although an energy efficiency increase of 4.44% does not lookmuch as a number, it is significant because the factory is working330 days in a year with an average of 15 h of daily production. Thetotal saving in one year is 115,775 kWh.

5. Conclusion

On many different occasions, the systems designed have pneu-matic components based on a given variable of pressure needed fordifferent machines, which are placed in a given manufacturingsystem. However, some problems cannot be predicted until thesystem is installed. One of the problems that may occur is related to


Table 9Matching compressors and blowers combinations e corrected with the influence of energy efficiency.

Electricity consumption Compressor combination Production of compressors Matching Consumption of blowers Blowers combination

kW/h bar/s bar/s

924 K41 0.130773 ) 0.118072 D41792 K34 0.116961 ) 0.105234 D34674 K31 0.098319 ) 0.08822 D31

) 0.082518 D32542 K24 0.084507 ) 0.078244 D33

) 0.075382 D24) 0.06968 D22

500 K21 0.068207 ) 0.065406 D21424 K26 0.062566 ) 0.052666 D26

) 0.048392 D25292 K13 0.048754 ) 0.04269 D23

) 0.039828 D13250 K12 0.035753 ) 0.035554 D12

) 0.029852 D11132 K14 0.013812 ) 0.012838 D140 K40 0 ) 0 D40

Table 11Simulation of pressure change in reservoir for energy efficient combination ofcompressors.

t p Rate of pressure increase/decrease

0 39 0.01580163 39.99546 �0.0825287 38.01503 0.015801212 39.99016 �0.08252236 38.00972 0.015801300 39.02099 0.010099399 40.02079 �0.08822422 37.99173 0.010099600 39.78935 0.014827614 39.99693 �0.06968642 38.04589 0.014827776 40.03271 �0.06968805 38.01199 0.014827900 39.42055 0.009125964 40.00455 �0.07538990 38.04462 0.0100991183 39.99373 �0.088221200 38.49399


system variability; different errors that are related to system per-formance at a given time. The pressure may drop some times andmight not always meet the desired level, which is needed for suc-cessful production. Another situation also related to system vari-ability might occur if the system is operating under its optimalcapacity, which can be very important especially for small andmid-size companies. Hence, planning for variability has to be one of thedesign constraints.

One of the main constraints in the water bottling system can besystem variability. Compressors and blowers might not alwaysneed to work at their optimal energy efficiency. Bottled watermanufacturers might have various compressors in their productionlines. Each single bottled water manufacturing line can be attachedto a different compressor. Use of these various compressors mightbe improved if an additional air storage unit is added to storecompressed air at times when not all blowers are working. In thisway, overall system reliability might be improved since, if onecompressor is not working, it does not necessarily mean that thereis not enough air supply for all four blowers. In this case study, fourpresented air compressors have been relocated to a central locationat the factory layout. Use of all four compressors has been carefullyanalyzed and initial data have been collected. A discrete eventsimulation method has been used to analyze possiblemanufacturing situations and to provide researchers with varioussolutions for an easier decision making process.

Different manufacturing scenarios need to be evaluated in orderto identify the optimal design of a manufacturing system. Differentdesign alternatives have to include different system variables, suchas economical, consumption, temperature, pressure, etc. Moreover,optimal design has to focus on one or more of these variables inorder to compare and assess different solutions. In this paper,required flow rate will be the main variable that has been used forsystem analysis and optimal design. The main idea is to try to mapout possible output cases for blowers in the system and needed

Table 10Energy efficient compressors combina-tion for given order of blowersoperation.

D32 K31D31 K31D22 K24D24 K24D31 K31


inputs for the compressors. The main goal is to use less energy thanis currently being used.

After the capacity planning stage has been completed, and amodel has been developed, a proposed solution has been verifiedwith the use of digital manufacturing, discrete event simulationmethod. In this way, a verified model can serve as a strategy forproduction planning which could then be used to program all

Fig. 18. Simulation model.


Fig. 19. Optimal schedule for the compressors.

Fig. 20. Pressure change diagram.

37.53838.53939.54040.5

0 200 400 600 800 1000 1200 1400

p (bar)

t (sec)

Pressure Change in Reservoir for Energy Efficient Combination

Fig. 21. Simulation of pressure change in reservoir for energy efficient combination ofcompressors.


Programmable Logical Controllers (PLCs) that are controllingworking cycles of these machines (compressors and blowers). Inaddition, fuzzy methods and multi-response parameter optimiza-tion methods might be used for further improvement(Sivapirakasam et al., 2011), or multi-objective decision makingmodels (Tan et al., 2008). This simulation study provides parame-ters that could be used for sequential automation ProgrammableLogic Controllers (PLCs). However, additional inputs of feedback areneeded from pressure sensors, which were used in this case studyin the given company and in this specific industrial application.Uncertainties related to leakage in this kind of system of discretemanufacturing, such as water bottles, can be avoided with the dataretrieved from the pressure sensors. They will react in a shorterperiod than is envisioned in this study, and leakage will only in-crease the consumption, but the control issues can be solved in thesame way (Dudic et al., 2012). There are no leakages due to handtools because this is a specific compressed air system operating at

Table 12Energy efficiency increase.

Energy consumed, kWh Increase in energy efficiency %

Initial case 189.82 e

First solution 182.89 3.65Second solution 181.40 4.44


high-pressure level (32e40 bar) and intended only for discreteproducts, such as supply of PET blowers.

The main goal of air supply system optimization is balancingproduction needs with air supply capacity at any given moment. Byconnecting output and input variables in a pneumaticmanufacturing system, better energy efficiency might be achieved.Hence, platforms for optimization and better planning should bedeveloped. In this paper, a suggestion is given for one possibleoptimization model for the production of bottled water. The mainidea was to improve the existing manufacturing system, which hadfour different compressors and four different blowers for PET bot-tles. The design solution that was suggested after an initial brain-storming phase was to use sequential controllers to turn on and offany of the four given compressors that were used in thismanufacturing system. These pneumatic system componentswould be integrated through a local network and controlled from adistributed location. No previous attempts have been made toconnect and build CAS on 40-bar level in this way. Furthermore, thegiven procedures for verifying energy efficiency yield and com-pressors’ control are general and can be used for energy efficiencyoptimization of multiple PET blowing in the same location forcompanies considering revitalization, or for the design of a newproduction site. Future work will be focused on applying thisoptimization method to another industrial application.

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Energy efficiency optimization of air supply system in a water bottle manufacturing system