Multi-objective-optimization of process parameters of industrial-gas-turbine fueled with natural gas by using Grey-Taguchi and ANN methods for better performance

• Published in: Energy reports 2020-11, Vol.6, p.2394-2402
• Main Authors: Gul, M., Kalam, M.A., Mujtaba, M.A., Alam, Saira, Bashir, M. Nasir, Javed, Iqra, Aziz, Umair, Farid, M. Rizwan, Hassan, M. Tahir, Iqbal, Shahid
• Format: Article
• Language: English
• Subjects: ANOVA analysis; Artificial neural networking; Gas turbine; Grey relational analysis; Grey Taguchi optimization
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2020.08.002

Gas-turbines are widely utilized in the power generation sectors as these require low operational cost, have very good efficiencies among other turbines, and produce less pollution but required to improve their performances further. This study used efficient and simple optimization methods of grey Taguchi and ANN to enhance gas turbine performance. The objective was to increase ηth, horsepower, and to decrease SFC and heat release of the industrial gas turbine (model # T-4502) by optimizing different levels of input process parameters by gyey-Taguchi method. Finally, air inlet temperature of 28.8 °C,14400 rpm and cartridge filter were found as optimal input parameters at which gas turbine’s performance improved with less consumption of natural gas. Moreover, ANOVA analysis revealed that ‘air-inlet-temperature’ is the dominant and ‘type of air-inlet-filter’ is the least effective process parameter with 71.17% and 1.40% impacts on the output parameters of the gas turbine. Confirmatory test was carried out experimentally and by ANN at suggested optimal level of input parameters, satisfactory results obtained which validates the effectiveness of the grey-Taguchi-method.