The recent advances for flexible fuel operation and the integration of biofuels and blends in gas turbines raise concern on engine health and quality. One of such potential threats involves the contamination and the growth of microorganisms in fuels and fuel systems with consequential effect on engine performance and health. In the past, the effects of microbial growth in fuels have been qualitatively described; however their effects in gas turbines have not necessarily been quantified. In this presentation, the effects of fuel deterioration are examined on a simulated aero-derivative gas turbine. A diesel-type fuel comprising of thirteen (13) hydrocarbon fractions was formulated and degraded with Bio-fAEG, a bio fouling assessment model that defines degraded fuels for performance simulation and analysis, predicts biodegradation rates as well as calculates the amount of water required to initiate degradation under aerobic conditions. The degraded fuels were integrated in the fuel library of Turbomatch (v2.0) and a twin shaft gas turbine was modeled for fuel performance analysis. The results indicate a significant loss in performance with reduced thermal efficiency of 1% and 10.4% and increased heat rate of 1% and 11.6% for the use of 1% and 10% degraded fuels respectively. Also parameters such as exhaust gas temperature and mass flow deviated from the baseline data indicating potential impact on engine health. Therefore, for reliable and safe operation, it is important to ensure engines run on good quality of fuel. This computational study provides insights on fuel deterioration in gas turbines and how it affects engine health.

1. Application 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 University
2National University of Ireland, Galway
1

2. Outline
2
— Background
— Model Development
— Methodology
— Results
— Conclusion

3. Background (1) 3
 Fuels are critical for reliable and efficient operation
 Maintainability
 Availability
 Reliability
 Durability
 Emissions

4. Background (2) 4
 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

5. Background (3) 5
x Component Failure
Injectors, Filters, Fuel line, Wall Liner, Blade fouling
x Reduced Engine Performance
x Increased smoke tendency and particulate emissions

6. Background (4) 6
 Microbes: bacteria, mould, yeasts
 Mechanisms of contamination: rust, dust,
soil, air, water, fuel
 Mechanisms of hydrocarbon degradation:
aerobic, anaerobic, acid-producing,
symbiotic
 Successes & Challenges

7. Background (5) 7
 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
 Successes & Challenges

8. Background (6) 8
 Hydrocarbon loss –degree?
 Sludge accumulation – microbial
% and chemical %?
 Induced corrosion – microbial %
 Physiological changes – Sig.?
 Chemical changes – Sig.?

9. Background(7) 9
 Good Handling Practices
 Biocide Application
 Water Elimination
 Routine Inspection
 Successes & Challenges

10. Background(8) 10
 Component Failure
Injectors, Filters, Fuel line, Wall Liner, Blade fouling
 Reduced Engine Performance
 Increased smoke tendency and particulate emissions
 Metal Corrosion
Degree?

11. Background (9) 11
 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. 12
Bio-fAEG Model
Development

13. Methodology
13
 Mass-balance stoichiometric equation
 Microbial bioenergetics
 Microbial kinetics
Fuel Module
Biomass
Module
Kinetic
Module
—Bio-fAEG Model Development

14. 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. 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
Methodology
—Bio-fAEG Model Development
1
Y
C16H34 + 1.81NH4
+
+ 1.81HCO3
−
+ 15.44O2 → 1.81C5H7O2N + 15.19H2O + 8.75CO2

16. 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
Methodology
—Bio-fAEG Model Development
Stot = Stot0 – {
𝑘𝐶Xo
YkC− kd
* (e YkC− kd ∗ t) – 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
17
Fuel
Module
Biomass
Module
Kinetic
Module
Fuel
Thermodynamic
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. 18
Bio-fAEG Model
Application in Gas Turbine Performance
Analysis

19. Methodology
19
Power: 22.4 MW
PR: 18
Mass Flow: 69.8 kg/s
EGT: 538oC
Efficiency: 34%
—Model Application & Engine Simulation

20. Results
20— Engine Validation

21. Results
21— Preliminary Fuel Analysis

22. Results
22— Preliminary Fuel Analysis

23. Results
23— Preliminary Fuel Analysis
— EGT increases by 4oC assuming TET is kept constant
— Increases engine heat rate by nearly 12%
— Reduces thermal efficiency by about 10%.

24. Results
24— Preliminary Fuel Analysis
Fuel degradation is at a cost to the plant operator

25. 25Summary
— 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 26
 first time bio-fouling assessment model
 a step towards predictive condition monitoring
Acknowledgement
Dr Athanasios Kolios
SEEA, Cranfield University