Application of Bio-FAEG, a Biofouling Assessment Model in Engine Performance Simulation

1Application of BIO-fAEG: A biofouling assessment model in gas turbines and the effect of degraded fuels on engine performance

Tosin Onabanjo1; Giuseppina Di Lorenzo1; Theoklis Nikolaidis1; Yinka Somorin2 1School of Energy, Environmental and Agrifood (SEEA), Cranfield University2National University of Ireland, Galway

2Outline

— Background

— Model Development

— Methodology

— Results

— Conclusion

3Background (1)

Fuels are critical for reliable and efficient operation

Maintainability

Availability

Reliability

Durability

Emissions

4Background (2)

Fuels get contaminated

x Hydrocarbon loss

x Sludge accumulation

x Induced corrosion

x Physiological changes

x Chemical changes

during production, transportation, storage, use

entry via water, rust, air, seepage, vent, particulates, microbes, other fuels/additives

5Background (3)

x Component FailureInjectors, Filters, Fuel line, Wall Liner, Blade fouling

x Reduced Engine Performance

x Increased smoke tendency and particulate emissions

6Background (4)

Microbes: bacteria, mould, yeasts

Mechanisms of contamination: rust,

dust, soil, air, water, fuel

Mechanisms of hydrocarbon

degradation: aerobic, anaerobic, acid-

producing, symbiotic

Successes & Challenges

7Background (5)

Ecology: fuel-water interphase

Bio-surfactant, Biofilms

TEA: O2, NO3, SO4, CO2

Growth factors: pH, Temp., Water,

nutrients, enhancer/inhibitor

By-products: sludge, sulphide, water,

CO2

Biocides

Courtesy: Denver Petroleum, 2012


8Background (6)

Hydrocarbon loss –degree?

Sludge accumulation – microbial % and chemical %?

Induced corrosion – microbial %

Physiological changes – Sig.?

Chemical changes – Sig.?

9Background(7)

Good Handling Practices

Biocide Application

Water Elimination

Routine Inspection


10

Background(8)

Component FailureInjectors, Filters, Fuel line, Wall Liner, Blade fouling

Reduced Engine Performance

Increased smoke tendency and particulate emissions

Metal Corrosion

Degree?

11

Background (9)

Root Cause Analysis- conventional culturing method

x Reactive: symptomatic

x Cost intensive

x One-way approach

Traditional approach Multidisciplinary approach

Proactive

Reduce downtime & associated cost

Predictive maintenance and condition monitoring

Root cause analysis –advance microbiology techniques

Modelling: fuel chemical kinetics, microbial kinetics, abiotic factors

Gross observation –representative sampling

Microbiology EngineeringMicrobiology Engineering

12

Bio-fAEG ModelDevelopment

13

Methodology

Mass-balance stoichiometric equation

Microbial bioenergetics

Microbial kinetics

Fuel Module Biomass Module Kinetic Module

—Bio-fAEG Model Development

14

Fuel Module

Biomass Module

Kinetic Module

Fuel composition

Assign to a broad & sub-classification

Assign a relative biodegradability & accessibility rate

Initial substrate concentration

Mass balance stoichiometric equation

Accessibility of Hydrocarbon

Inherent biodegradability

Methodology—Bio-fAEG Model Development

15

Fuel Module

Biomass Module

Kinetic Module

Electrons in the donor are partitioned between

energy generation and cell synthesis

donor substrate follows a two-step reaction—

substrate is converted to an intermediate

compound (acetyl Co-A) and a further

conversion to cells

• Substrate uptake

• Product formation —CO2, H2O, Biomass


+ + →

16

Fuel Module

Biomass Module

Kinetic Module

Actual/Predicted Growth Rate

Actual/Predicted Death Rate

Residence Time

Abiotic Losses

Rate of reaction for substrate uptake

Rate of reaction for biomass

formation


Stot = Stot0 – { *– 1} - kabSsatt

Assumptions Uniform dispersion of oil in aqueous solution

reaction not limited by dissolution kinetics

Microbes have access according to Xacc factor

Substrates are degraded according to Xin factor

17

Methodology

Fuel Module

Biomass Module

Kinetic Module

Fuel Thermodyna

mic Properties

Performance Analysis

Emission Analysis

Economic Analysis

Degraded Fuel

Clean Fuel

Turbomatch

Software

Emission Module

Economic Module

NASA CEA

—Bio-fAEG Model Integration

Bio-mathematical Model

18

Bio-fAEG ModelApplication in Gas Turbine Performance

Analysis

19

Methodology

Power: 22.4 MW

PR: 18

Mass Flow: 69.8 kg/s

EGT: 538oC

Efficiency: 34%

—Model Application & Engine Simulation

20

Results— Engine Validation

21

Results— Preliminary Fuel Analysis

22


23


— EGT increases by 4oC assuming TET is kept constant

— Increases engine heat rate by nearly 12%

— Reduces thermal efficiency by about 10%.

24


Fuel degradation is at a cost to the plant operator

25

Summary

— reduces engine efficiency by 10%

— increase maintenance cost by addition $30000

— occurs over time

— viability of the microbes, presence of biofilms, bio-surfactant production and metabolites

— presence of other nutrients from fuel addictive

— fuel’s operating condition & environmental factors

— free water to support growth

• Hydrocarbon Loss• Loss of FCV of the bulk fuel

10%

26

Conclusion

first time bio-fouling assessment model

a step towards predictive condition monitoring

Acknowledgement

Dr Athanasios Kolios SEEA, Cranfield University