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Research Article

Thermal performance analysis of gas turbine power plant using soft computing techniques: a review

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Article: 2374317 | Received 20 Mar 2024, Accepted 25 Jun 2024, Published online: 22 Jul 2024

References

  • Abdalla, E. A. H., Kumar, M., Abdalla, I. I., Mohamed, S. E. G., Soomro, A. M., Irfan, M., Rahman, S., & Nowakowski, G. (2023). Modeling and optimization of isolated combined heat and power microgrid for managing Universiti Teknologi PETRONAS energy. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3296428
  • Abdul-Wahab, S. A., Omer, A. S. M., Yetilmezsoy, K., & Bahramian, M. (2020). Modelling the clogging of gas turbine filter houses in heavy-duty power generation systems. Mathematical and Computer Modelling of Dynamical Systems, 26(2), 119–143. https://doi.org/10.1080/13873954.2020.1713821
  • Abubaker, A. M., Ahmad, A. D., Singh, B. B., Akafuah, N. K., & Saito, K. (2021). Multi-objective linear-regression-based optimization of a hybrid solar-gas turbine combined cycle with absorption inlet-air cooling unit. Energy Conversion and Management, 240, 114266. https://doi.org/10.1016/j.enconman.2021.114266
  • Ahmed, I., Alvi, U. E. H., Basit, A., Khursheed, T., Alvi, A., Hong, K. S., & Rehan, M. (2022). A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines. PLoS One, 17(1), e0261709. https://doi.org/10.1371/journal.pone.0261709
  • Ahmed, I., Rehan, M., Basit, A., & Hong, K. S. (2022). Greenhouse gases emission reduction for electric power generation sector by efficient dispatching of thermal plants integrated with renewable systems. Scientific Reports, 12(1), 12380. https://doi.org/10.1038/s41598-022-15983-0
  • Al-Awad, N. A. (2020). Steam turbine controllers design based on soft-computing techniques. International Journal of Robotics and Automation (IJRA), 9(4), 281–291. doi:10.11591/ijra.v9i4.pp281-291
  • Alirahmi, S. M., Behzadi, A., Ahmadi, P., & Sadrizadeh, S. (2023). An innovative four-objective dragonfly-inspired optimization algorithm for an efficient, green, and cost-effective waste heat recovery from SOFC. Energy, 263, 125607. https://doi.org/10.1016/j.energy.2022.125607
  • Alirahmi, S. M., Mousavi, S. F., Ahmadi, P., & Arabkoohsar, A. (2021). Soft computing analysis of a compressed air energy storage and SOFC system via different artificial neural network architecture and tri-objective grey wolf optimization. Energy, 236, 121412. https://doi.org/10.1016/j.energy.2021.121412
  • Aquize, R. A., Cajahuaringa, A., Machuca, J., Mauricio, D., & Mauricio Villanueva, J. M. (2023). System identification methodology of a gas turbine based on artificial recurrent neural networks. Sensors, 23(4), 2231. doi:10.3390/s23042231
  • Assareh, E., Hoseinzadeh, S., Ghersi, D. E., Farhadi, E., Keykhah, S., & Lee, M. (2023). Energy, exergy, exergoeconomic, exergoenvironmental, and transient analysis of a gas-fired power plant-driven proposed system with combined Rankine cycle: Thermoelectric for power production under different weather conditions. Journal of Thermal Analysis and Calorimetry, 148(16), 8283–8307. https://doi.org/10.1007/s10973-022-11651-7
  • Chen, Y. Z., Tsoutsanis, E., Xiang, H. C., Li, Y. G., & Zhao, J. J. (2022). A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions. Applied Energy, 317, 119148. https://doi.org/10.1016/j.apenergy.2022.119148
  • Chen, Y. Z., Zhao, X. D., Xiang, H. C., & Tsoutsanis, E. (2021). A sequential model-based approach for gas turbine performance diagnostics. Energy, 220, 119657. https://doi.org/10.1016/j.energy.2020.119657
  • Cheng, X., Zheng, H., Yang, Q., Zheng, P., & Dong, W. (2023). Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions. Energy, 278, 127944. https://doi.org/10.1016/j.energy.2023.127944
  • de Castro-Cros, M., Velasco, M., & Angulo, C. (2021). Machine-learning-based condition assessment of gas turbines—A review. Energies, 14(24), 8468. https://doi.org/10.3390/en14248468
  • Deng, C., Abdalla, A. N., Ibrahim, T. K., Jiang, M., Al-Sammarraie, A. T., & Wu, J. (2020). Implementation of adaptive neuro-fuzzy model to optimize operational process of multiconfiguration gas-turbines. Advances in High Energy Physics, 2020, 1–17. https://doi.org/10.1155/2020/6590138
  • Djeddi, C., Hafaifa, A., Iratni, A., Hadroug, N., & Chen, X. (2021). Robust diagnosis with high protection to gas turbine failures identification based on a fuzzy neuro inference monitoring approach. Journal of Manufacturing Systems, 59, 190–213. https://doi.org/10.1016/j.jmsy.2021.02.012
  • Dubey, K. K., & Mishra, R. S. (2021). Thermo-statistical study of sustainable refrigeration system for stack flow heat recovery of combined gas turbine-steam turbine power generation. Materials Today: Proceedings, 43, 74–83. https://doi.org/10.1016/j.matpr.2020.11.211
  • Effiom, S. O., Ajor, J. A., Effiom, P. C. O., Edem, I., Ubi, P., Abam, F., & Diemuodeke, O. E. (2023). Experimental study on the optimal performance of gas turbine (GT) inlet air filtration system for offshore application. Journal of Engineering and Applied Science, 70(1), 131. https://doi.org/10.1186/s44147-023-00303-8
  • Falcone, R., Lima, C., & Martinelli, E. (2020). Soft computing techniques in structural and earthquake engineering: A literature review. Engineering Structures, 207, 110269. https://doi.org/10.1016/j.engstruct.2020.110269
  • Faqih, M., Omar, M. B., & Ibrahim, R. (2023). Prediction of dry-low emission gas turbine operating range from emission concentration using semi-supervised learning. Sensors, 23(8), 3863. https://doi.org/10.3390/s23083863
  • Gandhi, R. R., & Kathirvel, C. (2021). A comparative study of different soft computing techniques for hybrid renewable energy systems. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1667–1677). IEEE. https://doi.org/10.1109/ICOEI51242.2021.9453070
  • Gopal, N., & Panchal, D. (2023). Fuzzy decision support system for sustainable operational performance optimization for boiler unit in milk process industry. Applied Soft Computing, 135, 109983. https://doi.org/10.1016/j.asoc.2023.109983
  • Gul, M., Kalam, M. A., Mujtaba, M. A., Alam, S., Bashir, M. N., Javed, I., Aziz, U., Farid, M. R., Hassan, M. T., & Iqbal, S. (2020). Multi-objective-optimization of process parameters of industrial-gas-turbine fueled with natural gas by using Grey-Taguchi and ANN methods for better performance. Energy Reports, 6, 2394–2402. https://doi.org/10.1016/j.egyr.2020.08.002
  • Hai, T., Dhahad, H. A., Attia, E. A., Ibrahim, B. F., Mohamed, A., Almojil, S. F., Almohana, A. I., Alali, A. F., & Farhang, B. (2022). Proposal 3E analysis and multi-objective optimization of a new biomass-based energy system based on the organic cycle and ejector for the generation of sustainable power, heat, and cold. Sustainable Energy Technologies and Assessments, 53, 102551. https://doi.org/10.1016/j.seta.2022.102551
  • Hai, T., Ma, X., Chauhan, B. S., Mahmoud, S., Al-Kouz, W., Tong, J., & Salah, B. (2023). Techno-economic optimization of a new waste-to-energy plant for electricity, cooling, and desalinated water using various biomass for emission reduction. Chemosphere, 338, 139398. https://doi.org/10.1016/j.chemosphere.2023.139398
  • Hani, E. H. B., Sinaga, N., Khanmohammdi, S., & Diyoke, C. (2022). Assessment of a waste energy recovery (WER) unit for power and refrigeration generation: Advanced thermodynamic examination. Sustainable Energy Technologies and Assessments, 52, 102213. https://doi.org/10.1016/j.seta.2022.102213
  • Hasanzadeh, A., Chitsaz, A., Ghasemi, A., Mojaver, P., Khodaei, R., & Alirahmi, S. M. (2022). Soft computing investigation of stand-alone gas turbine and hybrid gas turbine–solid oxide fuel cell systems via artificial intelligence and multi-objective grey wolf optimizer. Energy Reports, 8, 7537–7556. https://doi.org/10.1016/j.egyr.2022.05.281
  • Huiyong, W., Shuchun, J., & Zhu, J. (2023). Simulation model and fault analysis of air circulation system of the aircraft based on grasshopper optimization algorithm: Support vector machine. Soft Computing, 27(18), 13269–13284. https://doi.org/10.1007/s00500-022-07403-2
  • Jamali, D. H., & Noorpoor, A. (2019). Optimization of a novel solar-based multi-generation system for waste heat recovery in a cement plant. Journal of Cleaner Production, 240, 117825. https://doi.org/10.1016/j.jclepro.2019.117825
  • Jo-Han, N., Lim, Z. X., Wong, K. Y., & Chong, C. T. (2019). Optimisation of syngas composition for emissions minimisation and cost reduction through selective catalytic reduction (SCR) and gas mixtures in a gas turbine. In IOP conference series: Earth and environmental science (Vol. 268, no. 1, pp. 012160). IOP Publishing. https://iopscience.iop.org/article/10.10881755-1315/268/1/012160/meta.
  • Kabengele, K. T., Tartibu, L. K., & Olayode, I. O. (2022). Modelling of a combined cycle power plant performance using artificial neural network model. In 2022 international conference on artificial intelligence, big data, computing and data communication systems (icABCD) (pp. 1–7). IEEE. https://doi.org/10.1109/icABCD54961.2022.9856095
  • Kareem, A. F., Akroot, A., Abdul Wahhab, H. A., Talal, W., Ghazal, R. M., & Alfaris, A. (2023). Exergo–economic and parametric analysis of waste heat recovery from taji gas turbines power plant using rankine cycle and organic rankine cycle. Sustainability, 15(12), 9376. doi:10.3390/su15129376
  • Khani, M. S., Shahsavani, Y., Mehraein, M., & Kisi, O. (2023). Performance evaluation of the savonius hydrokinetic turbine using soft computing techniques. Renewable Energy, 215, 118906. https://doi.org/10.1016/j.renene.2023.118906
  • Latif, A., Hussain, S. S., Das, D. C., & Ustun, T. S. (2020). State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems. Applied Energy, 266, 114858. https://doi.org/10.1016/j.apenergy.2020.114858
  • Liu, Z., & He, T. (2020). Exergoeconomic analysis and optimization of a gas turbine-modular helium reactor with new organic rankine cycle for efficient design and operation. Energy Conversion and Management, 204, 112311. https://doi.org/10.1016/j.enconman.2019.112311
  • Liu, Z., & Karimi, I. A. (2020). Gas turbine performance prediction via machine learning. Energy, 192, 116627. https://doi.org/10.1016/j.energy.2019.116627
  • Liu, D., Zhong, S., Lin, L., Zhao, M., Fu, X., & Liu, X. (2022). Highly imbalanced fault diagnosis of gas turbines via clustering-based downsampling and deep siamese self-attention network. Advanced Engineering Informatics, 54, 101725. https://doi.org/10.1016/j.aei.2022.101725
  • Ma, H., Xie, Y., Duan, K., Song, X., Ding, R., & Hou, C. (2022). Dynamic control method of flue gas heat transfer system in the waste heat recovery process. Energy, 259, 125010. https://doi.org/10.1016/j.energy.2022.125010
  • Manente, G., & Costa, M. (2020). On the conceptual design of novel supercritical CO2 power cycles for waste heat recovery. Energies, 13(2), 370. https://doi.org/10.3390/en13020370
  • Modu, B., Abdullah, M. P., Bukar, A. L., & Hamza, M. F. (2023). A systematic review of hybrid renewable energy systems with hydrogen storage: Sizing, optimization, and energy management strategy. International Journal of Hydrogen Energy, 48, 38354–38373. https://doi.org/10.1016/j.ijhydene.2023.06.126
  • Mohamed, O., & Khalil, A. (2020). Progress in modeling and control of gas turbine power generation systems: A survey. Energies, 13(9), 2358. doi:10.3390/en13092358
  • Mohammadi, K., Ellingwood, K., & Powell, K. (2020). A novel triple power cycle featuring a gas turbine cycle with supercritical carbon dioxide and organic Rankine cycles: Thermoeconomic analysis and optimization. Energy Conversion and Management, 220, 113123. https://doi.org/10.1016/j.enconman.2020.113123
  • Mohan, M., Alom, N., & Saha, U. K. (2023). Role of optimization and soft-computing techniques in the design and development of futuristic Savonius wind turbine blades: A review. Wind Engineering, 47(3), 722–744. https://doi.org/10.1177/0309524X221150491
  • Montazeri-Gh, M., & Nekoonam, A. (2022). Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine. Engineering Failure Analysis, 135, 106115. https://doi.org/10.1016/j.engfailanal.2022.106115
  • Montazeri-Gh, M., Nekoonam, A., & Yazdani, S. (2021). A novel approach to gas turbine fault diagnosis based on learning of fault characteristic maps using hybrid residual compensation extreme learning machine-growing neural gas model. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43(9), 430. doi:10.1007/s40430-021-03136-9
  • Montazeri-Gh, M., & Yazdani, S. (2020). Application of interval type-2 fuzzy logic systems to gas turbine fault diagnosis. Applied Soft Computing, 96, 106703. https://doi.org/10.1016/j.asoc.2020.106703
  • Moradi, M., & Seyedtabaii, S. (2022). Intelligent fuzzy controller design: Disturbance rejection cases. Applied Soft Computing, 124, 109015. https://doi.org/10.1016/j.asoc.2022.109015
  • Nallagownden, P., Abdalla, E. A. H., & Nor, N. M. (2020). Power consumption optimization for the industrial load plant using improved ANFIS-based accelerated PSO technique. In Practical examples of energy optimization models (pp. 35–51). Springer. https://doi.org/10.1007/978-981-15-2199-7_3
  • Nayeri, M. R., Araabi, B. N., & Moshiri, B. (2022). Fault detection and isolation of gas turbine: Hierarchical classification and confidence rate computation. Journal of the Franklin Institute, 359(17), 10120–10144. https://doi.org/10.1016/j.jfranklin.2022.09.056
  • Nikpey Somehsaraei, H., Ghosh, S., Maity, S., Pramanik, P., De, S., & Assadi, M. (2020). Automated data filtering approach for ANN modeling of distributed energy systems: Exploring the application of machine learning. Energies, 13(14), 3750. https://doi.org/10.3390/en13143750
  • Pati, S., Yadav, D., & Verma, O. P. (2021). Synergetic fusion of energy optimization and waste heat reutilization using nature-inspired algorithms: A case study of kraft recovery process. Neural Computing and Applications, 33(17), 10751–10770. https://doi.org/10.1007/s00521-020-04828-4
  • Prabakar, V. (2022). Neural network based soft sensor for critical parameter estimation of gas turbine engine. In 2021 devices for integrated circuit (DevIC) (pp. 450–454). IEEE. https://doi.org/10.1109/DevIC50843.2021.9455825
  • Rajagopalan, A., Swaminathan, D., Alharbi, M., Sengan, S., Montoya, O. D., El-Shafai, W., Fouda, M. M., & Aly, M. H. (2022). Modernized planning of smart grid based on distributed power generations and energy storage systems using soft computing methods. Energies, 15(23), 8889. https://doi.org/10.3390/en15238889
  • Reddy, D., Abo-Al-Ez, K., & Adonis, M. (2023). Scope of soft computing techniques in the development of the ideal design parameters for microgrids: A systematic review. AuthoreaPreprints, https://doi.org/10.22541/au.169735609.93463895/v1
  • Rodríguez, M. B. R., Rodríguez, J. L. M., & Fontes, C. D. H. (2022). Modeling of a combined cycle gas turbine (CCGT) using an adaptive neuro-fuzzy system. Thermal Engineering, 69(9), 662–673. https://doi.org/10.1134/S0040601522090038
  • Sarwar, U., Muhammad, M., Mokhtar, A. A., Khan, R., Behrani, P., & Kaka, S. (2024). Hybrid intelligence for enhanced fault detection and diagnosis for industrial gas turbine engine. Results in Engineering, 21, 101841. https://doi.org/10.1016/j.rineng.2024.101841
  • Talebi, S. S., Madadi, A., Tousi, A. M., & Kiaee, M. (2022). Micro gas turbine fault detection and isolation with a combination of artificial neural network and off-design performance analysis. Engineering Applications of Artificial Intelligence, 113, 104900. https://doi.org/10.1016/j.engappai.2022.104900
  • Talib, K., Putra, A., Ismail, A. F., Shamsudin, S. A., & Musthafah, M. T. (2018). Prediction of generated power from steam turbine waste heat recovery mechanism system on naturally aspirated spark ignition engine using artificial neural network. Soft Computing, 22, 5955–5964. https://doi.org/10.1007/s00500-017-2873-3
  • Tari, A. H. Z., Khosravi, M., Dastjerdi, S. M., Khoshnevisan, A., & Ahmadi, P. (2023). Multi objectives optimization and transient analysis of an off-grid building with water desalination and waste heat recovery units. Sustainable Energy Technologies and Assessments, 59, 103406. https://doi.org/10.1016/j.seta.2023.103406
  • Tsoutsanis, E., Qureshi, I., & Hesham, M. (2023). Performance diagnostics of gas turbines operating under transient conditions based on dynamic engine model and artificial neural networks. Engineering Applications of Artificial Intelligence, 126, 106936. https://doi.org/10.1016/j.engappai.2023.106936
  • Tunckaya, Y. (2021). An experimental modelling and performance validation study: Top gas pressure tracking system in a blast furnace using soft computing methods. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 235(6), 2154–2164. https://doi.org/10.1177/09544089211033117
  • Turja, A. I., Khan, I. A., Rahman, S., Mustakim, A., Hossain, M. I., Ehsan, M. M., & Khan, Y. (2024). Machine learning-based multi-objective optimization and thermal assessment of supercritical CO2 rankine cycles for gas turbine waste heat recovery. Energy and AI, 16, 100372. doi:10.1016/j.egyai.2024.100372
  • Wang, C., Feng, Y., Liu, Z., Wang, Y., Fang, J., Qin, J., Shao, J., & Huang, H. (2022). Assessment of thermodynamic performance and CO2 emission reduction for a supersonic precooled turbine engine cycle fueled with a new green fuel of ammonia. Energy, 261, 125272. https://doi.org/10.1016/j.energy.2022.125272
  • Wang, S., Ma, J., Li, W., Khayatnezhad, M., & Rouyendegh, B. D. (2022). An optimal configuration for hybrid SOFC, gas turbine, and proton exchange membrane electrolyzer using a developed aquila optimizer. International Journal of Hydrogen Energy, 47(14), 8943–8955. https://doi.org/10.1016/j.ijhydene.2021.12.222
  • Wang, Q., Yang, L., & Rao, Y. (2021). Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades. Energy, 214, 118878. https://doi.org/10.1016/j.energy.2020.118878
  • Wood, D. A. (2020). Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm. SN Applied Sciences, 2(3), 441. https://doi.org/10.1007/s42452-020-2249-7
  • Xezonakis, V., & Ntantis, E. L. (2023). Modelling and energy optimization of a thermal power plant using a multi-layer perception regression method. WSEAS Transactions on Systems and Control, 18, 24. DOI: 10.37394/23203.2022.18.24
  • Xia, Y., Yang, Q., Sun, G., Wang, Y., Wang, Q., & Ba, S. (2024). A novel carbon emission estimation method based on electricity-carbon nexus and non-intrusive load monitoring. Applied Energy, 360, 122773. Available at SSRN 4589095. https://doi.org/10.1016/j.apenergy.2024.122773.
  • Yang, S., Gao, H. O., & You, F. (2023). Integrated optimization in operations control and systems design for carbon emission reduction in building electrification with distributed energy resources. Advances in Applied Energy, 12, 100144. https://doi.org/10.1016/j.adapen.2023.100144
  • Yang, R., Liu, Y., Yu, Y., He, X., & Li, H. (2021). Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine. Energy Reports, 7, 5446–5454. doi:10.1016/j.egyr.2021.08.120
  • Yousif, S. T., Ismail, F. B., & Al-Bazi, A. (2024). A hybrid neural network-based improved PSO algorithm for gas turbine emissions prediction. Advanced Theory and Simulations, 2301222. https://doi.org/10.1002/adts.202301222
  • Zhao, Y., & Foong, L. K. (2022). Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm. Measurement, 198, 111405. https://doi.org/10.1016/j.measurement.2022.111405
  • Zhao, J., Li, Y. G., & Sampath, S. (2023). A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics. Applied Energy, 332, 120520. https://doi.org/10.1016/j.apenergy.2022.120520
  • Zou, Z., Yu, X., & Ergan, S. (2020). Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network. Building and Environment, 168, 106535. https://doi.org/10.1016/j.buildenv.2019.106535