Gas turbine compressor washing economics and optimisation using genetic algorithm

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dc.contributor.authorMusa, Gali
dc.contributor.authorIgie, Uyioghosa
dc.contributor.authorDi Lorenzo, Giuseppina
dc.contributor.authorAlrashed, Mosab
dc.contributor.authorNavaratne, Rukshan
dc.date.accessioned2022-08-18T12:13:01Z
dc.date.available2022-08-18T12:13:01Z
dc.date.issued2022-08-09
dc.description.abstractStudies have shown that online compressor washing of gas turbine engines slows down the rate of fouling deterioration during operation. However, for most operators, there is a balancing between the performance improvements obtained and the investment (capital and recurring cost). Washing the engine more frequently to keep the capacity high is a consideration. However, this needs to be addressed with expenditure over the life of the washing equipment rather than a simple cost-benefit analysis. The work presented here is a viability study of online compressor washing for 17 gas turbine engines ranging from 5.3 to 307MW. It considers the nonlinear cost of the washing equipment related to size categories, as well as nonlinear washing liquid consumption related to the variations in engine mass flows. Importantly, the respective electricity break-even selling price of the respective engines was considered. The results show that for the largest engine, the return of investment is 520% and the dynamic payback time of 0.19 years when washing every 72 hours. When this is less frequent at a 480-hour interval, the investment return and payback are 462% and 0.22 years. The optimisation study using a multi-objective genetic algorithm shows that the optimal washing is rather a 95-hour interval. For the smallest engine, the investment was the least viable for this type of application.en_UK
dc.description.sponsorshipPetroleum Technology Development Funden_UK
dc.identifier.citationMusa G, Igie U, Di Lorenzo G, et al., (2022) Gas turbine compressor washing economics and optimisation using genetic algorithm. Journal of Engineering for Gas Turbines and Power, Volume 144, Issue 9, September 2022, Paper number GTP-22-1056en_UK
dc.identifier.eissn1528-8919
dc.identifier.issn0742-4795
dc.identifier.urihttps://doi.org/10.1115/1.4055187
dc.identifier.urihttps://asmedigitalcollection.asme.org/gasturbinespower/article/144/9/091012/1145425/Gas-Turbine-Compressor-Washing-Economics-and
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18329
dc.language.isoenen_UK
dc.publisherAmerican Society of Mechanical Engineers (ASME)en_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcompressorsen_UK
dc.subjecteconomicsen_UK
dc.subjectgas turbinesen_UK
dc.subjectgenetic algorithmsen_UK
dc.subjectoptimizationen_UK
dc.subjectenginesen_UK
dc.subjectcost benefit analysis (dynamics)en_UK
dc.titleGas turbine compressor washing economics and optimisation using genetic algorithmen_UK
dc.typeArticleen_UK

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