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Thermal Engineering

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01.09.2022 | STEAM-TURBINE, GAS-TURBINE, AND COMBINED-CYCLE POWER PLANTS AND THEIR AUXILIARY EQUIPMENT

Modeling of a Combined Cycle Gas Turbine (CCGT) Using an Adaptive Neuro-Fuzzy System

verfasst von: M. B. R. Rodríguez, J. L. M. Rodríguez, C. de H. Fontes

Erschienen in: Thermal Engineering | Ausgabe 9/2022

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Abstract

The multiple advantages of the combined power cycles make their improvement a priority. The modeling of a Combined Cycle Gas Turbine (CCGT) for the identification/recognition of operating variables that can provide an increase in efficiency is a challenge in this type of thermal system. The simulation was carried out using an adaptive neuro-fuzzy inference system (ANFIS). The influence of three input variables on the thermal efficiency of the combined cycle was analyzed: the pressure ratio (compression ratio) used in the gas cycle, the pressure of the steam extraction for preheating of feed water (if applicable) and the heat lost to the outside in the steam turbines. It is shown that the ratio of pressures in the gas cycle has the most significant effect on the efficiency. The optimal value of pressure in steam extraction was obtained, corresponding to the maximum efficiency of the cycle, equal to 62.38%. A comparison of the efficiency values obtained using ANFIS and the results of parametric analysis showed their insignificant discrepancy. The proposed approach using ANFIS can be an alternative to the usual phenomenological model when modeling the operating modes of the combined cycle.

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Here and below, the index indicates the points of the cycle.

Index “s” means isentropic process.

M. Martín-Gamboa, D. Iribarren, and J. Dufour, “Environmental impact efficiency of natural gas combined cycle power plants: A combined life cycle assessment and dynamic data envelopment analysis approach,” Sci. Total Environ. 615, 29–37 (2018). https://doi.org/10.1016/j.scitotenv.2017.09.243CrossRef

P. J. Korhonen and M. Luptacik, “Eco-efficiency analysis of power plants: An extension of data envelopment analysis,” Eur. J. Oper. Res. 154, 437–446 (2004). https://doi.org/10.1016/S0377-2217(03)00180-2CrossRefMATH

S. Munisamy and B. Arabi, “Eco-efficiency change in power plants: Using a slacks-based measure for the meta-frontier Malmquist–Luenberger productivity index,” J. Cleaner Prod. 105, 218–232 (2015). https://doi.org/10.1016/j.jclepro.2014.12.081CrossRef

A. Arceo, W. K. Biswas, and M. John, “Eco-efficiency improvement of Western Australian remote area power supply,” J. Cleaner Prod. 230, 820–834 (2019). https://doi.org/10.1016/j.jclepro.2019.05.106CrossRef

M. Khaljani, R. K. Saray, and K. Bahlouli, “Comprehensive analysis of energy, exergy and exergo-economic of cogeneration of heat and power in a combined gas turbine and organic Rankine cycle,” Energy Convers. Manage. 97, 154–165 (2015). https://doi.org/10.1016/j.enconman.2015.02.067CrossRef

J. Dong, X. Zhang, and X. Xu, “Techno-economic assessment and policy of gas power generation considering the role of multiple stakeholders in China,” Energy Policy 48, 209–221 (2012). https://doi.org/10.1016/j.enpol.2012.05.010CrossRef

H. Haberl, D. Wiedenhofer, S. Pauliuk, F. Krausmann, D. B. Müller, and M. Fischer-Kowalski, “Contributions of sociometabolic research to sustainability science,” Nat. Sustainability 2, 173–184 (2019). https://doi.org/10.1038/s41893-019-0225-2CrossRef

M. Pan, F. Aziz, B. Li, S. Perry, N. Zhang, I. Bulatov, and R. Smith, “Application of optimal design methodologies in retrofitting natural gas combined cycle power plants with CO₂ capture,” Appl. Energy 161, 695–706 (2016). https://doi.org/10.1016/j.apenergy.2015.03.035CrossRef

E. Ersayin and L. Ozgener, “Performance analysis of combined cycle power plants: A case study,” Renewable Sustainable Energy Rev. 43, 832–842 (2015). https://doi.org/10.1016/j.rser.2014.11.082CrossRef

10.

T. K. Ibrahim, M. K. Mohammed, O. I. Awad, M. Rahman, G. Najafi, F. Basrawi, A. N. A. Alla, and R. Mamat, “The optimum performance of the combined cycle power plant: A comprehensive review,” Renewable Sustainable Energy Rev. 79, 459–474 (2017). https://doi.org/10.1016/j.rser.2017.05.060CrossRef

11.

M. A. Ehyaei, M. Tahani, P. Ahmadi, and M. Esfandiari, “Optimization of fog inlet air cooling system for combined cycle power plants using genetic algorithm,” Appl. Therm. Eng. 76, 449–461 (2015). https://doi.org/10.1016/j.applthermaleng.2014.11.032CrossRef

12.

M. Valdés, M. D. Durán, and A. Rovira, “Thermoeconomic optimization of combined cycle gas turbine power plants using genetic algorithms,” Appl. Therm. Eng. 23, 2169–2182 (2003). https://doi.org/10.1016/S1359-4311(03)00203-5CrossRef

13.

A. Polyzakis, C. Koroneos, and G. Xydis, “Optimum gas turbine cycle for combined cycle power plant,” Energy Convers. Manage. 49, 551–563 (2008). https://doi.org/10.1016/j.enconman.2007.08.002CrossRef

14.

Z. Liu and I. A. Karimi, “New operating strategy for a combined cycle gas turbine power plant,” Energy Convers. Manage. 171, 1675–1684 (2018). https://doi.org/10.1016/j.enconman.2018.06.110CrossRef

15.

R. Kehlhofer, B. Rukes, F. Hannemann, and F. Stirnimann, Combined-Cycle Gas & Steam Turbine Power Plants, 3rd ed. (PennWell, Tulsa, Okla., 2009).

16.

İ. Dinçer and M. A. Rosen, Exergy: Energy, Environment and Sustainable Development, 2nd ed. (Elsevier, Amsterdam, 2013). https://doi.org/10.1016/C2010-0-68369-6CrossRef

17.

T. K. Ibrahim and M. M. Rahman, “Study on effective parameter of the triple-pressure reheat combined cycle performance,” Therm. Sci. 17, 497–508 (2013). https://doi.org/10.2298/TSCI111016143ICrossRef

18.

T. K. Ibrahim and M. M. Rahman, “Effective parameters on performance of multipressure combined cycle power plants,” Adv. Mech. Eng. 2014, 781503 (2014). https://doi.org/10.1155/2014/781503CrossRef

19.

T. K. Ibrahim and M. M. Rahman, “Optimum performance improvements of the combined cycle based on an intercooler–reheated gas turbine,” J. Energy Resour. Technol. 137, 061601 (2015). https://doi.org/10.1115/1.4030447CrossRef

20.

N. J. Mendoza, Análisis del Diseño Termodinámico de Centrales Eléctricas de Ciclo Combinado (Univ. de Piura, 2012).

21.

N. Bonyadi, E. Johnson, and D. Baker, “Technoeconomic and exergy analysis of a solar geothermal hybrid electric power plant using a novel combined cycle,” Energy Convers. Manage. 156, 542–554 (2018). https://doi.org/10.1016/j.enconman.2017.11.052CrossRef

22.

J. Wu, “A new approach to determining the intermediate temperatures of endoreversible combined cycle power plant corresponding to maximum power,” Int. J. Heat Mass Transfer 91, 150–161 (2015). https://doi.org/10.1016/j.ijheatmasstransfer.2015.07.077CrossRef

23.

T. K. Ibrahim, M. Rahman, M. Mohammed, and F. Basrawi, “Statistical analysis and optimum performance of the gas turbine power plant,” Int. J. Automot. Mech. Eng. 13, 3215–3225 (2016). https://doi.org/10.15282/ijame.13.1.2016.8.0268CrossRef

24.

A. Swain and M. Das Kumar, “Prediction of heat transfer coefficient in flow boiling over tube bundles using ANFIS,” Heat Transfer Eng. 37, 443–455 (2016). https://doi.org/10.1080/01457632.2015.1057450CrossRef

25.

M. A. Elhosseini, R. A. El Sehiemy, A. H. Salah, and M. Abido, “Modeling and control of an interconnected combined cycle gas turbine using fuzzy and ANFIS controllers,” Electr. Eng. 100, 763–785 (2018). https://doi.org/10.1007/s00202-017-0547-xCrossRef

26.

M. B. R. Rodríguez, J. L. M. Rodríguez, and C. H. Fontes, “Determination of design and operation parameters of a surface condenser using an adaptive neuro-fuzzy inference system,” Int. J. Heat Mass Transfer 138, 17–24 (2019). https://doi.org/10.1016/j.ijheatmasstransfer.2019.04.031CrossRef

27.

A. Swain and M. K. Das, “ANFIS modeling of boiling heat transfer over tube bundles,” in Soft Computing for Problem Solving, Ed. by J. Bansal, K. Das, A. Nagar, K. Deep, and A. Ojha (Springer, Singapore, 2019), pp. 433–440. https://doi.org/10.1007/978-981-13-1595-4_34CrossRef

28.

R. Bettocchi, M. Pinelli, P. R. Spina, and M. Venturini, “Artificial intelligence for the diagnostics of gas turbines — Part II: Neuro-fuzzy approach,” J. Eng. Gas Turbines Power 129, 720–729 (2007). https://doi.org/10.1115/1.2431392CrossRef

29.

A. Zaaoumi, A. Bah, M. Alaoui, A. Mechaqrane, and M. Berrehili, “Application of artificial neural networks and adaptive neuro-fuzzy inference system to estimate the energy generation of a solar power plant in Ain Beni-Mathar (Morocco),” in Proc. 10th Int. Conf. on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, June 28–30, 2018 (IEEE, Piscataway, N.J., 2018). https://doi.org/10.1109/ECAI.2018.8679015

30.

T. R. Kiran and S. Rajput, “An effectiveness model for an indirect evaporative cooling (IEC) system: Comparison of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and fuzzy inference system (FIS) approach,” Appl. Soft Comput. 11, 3525–3533 (2011). https://doi.org/10.1016/j.asoc.2011.01.025CrossRef

31.

T. A. Tahseen, M. Ishak, and M. Rahman, “Performance predictions of laminar heat transfer and pressure drop in an in-line flat tube bundle using an adaptive neuro-fuzzy inference system (ANFIS) model,” Int. Commun. Heat Mass Transfer 50, 85–97 (2014). https://doi.org/10.1016/j.icheatmasstransfer.2013.11.007CrossRef

32.

A. K. Gupta, P. Kumar, R. K. Sahoo, A. K. Sahu, and S. K. Sarangi, “Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA,” J. Comput. Des. Eng. 4, 60–68 (2017). https://doi.org/10.1016/j.jcde.2016.07.002CrossRef

33.

A. Karami, T. Yousefi, S. Ebrahimi, E. Rezaei, and S. Mahmoudinezhad, “Adaptive neuro-fuzzy inference system (ANFIS) to predict the forced convection heat transfer from a v-shaped plate,” Heat Mass Transfer 49, 789–798 (2013). https://doi.org/10.1007/s00231-013-1125-zCrossRef

34.

H. Esen, M. Inalli, A. Sengur, and M. Esen, “Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems,” Int. J. Refrig. 31, 65–74 (2008). https://doi.org/10.1016/j.ijrefrig.2007.06.007CrossRef

35.

M. Zangeneh, M. Omid, and A. Akram, “A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran,” Span. J. Agric. Res. 9, 661–671 (2011). https://doi.org/10.5424/sjar/20110903-371-10CrossRef

36.

J.-S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man Cybern. 23, 665–685 (1993). https://doi.org/10.1109/21.256541CrossRef

37.

M. Panella, “A hierarchical procedure for the synthesis of ANFIS networks,” Adv. Fuzzy Syst. 2012, 491237 (2012). https://doi.org/10.1155/2012/491237MathSciNetCrossRefMATH

38.

S. Kar, S. Das, and P. K. Ghosh, “Applications of neuro fuzzy systems: A brief review and future outline,” App-l. Soft Comput. 15, 243–259 (2014). https://doi.org/10.1016/j.asoc.2013.10.014CrossRef

39.

H. A. Zamani, S. Rafiee-Taghanaki, M. Karimi, M. Arabloo, and A. Dadashi, “Implementing ANFIS for prediction of reservoir oil solution gas–oil ratio,” J. Nat. Gas Sci. Eng. 25, 325–334 (2015). https://doi.org/10.1016/j.jngse.2015.04.008CrossRef

40.

G. Petchinathan, K. Valarmathi, D. Devaraj, and T. K. Radhakrishnan, “Local linear model tree and neuro-fuzzy system for modelling and control of an experimental pH neutralization process,” Braz. J. Chem. Eng. 31, 483–495 (2014). https://doi.org/10.1590/0104-6632.20140312s00002287CrossRef

41.

A. Akpınar, M. Özger, and M. I. Kömürcü, “Prediction of wave parameters by using fuzzy inference system and the parametric models along the south coasts of the Black Sea,” J. Mar. Sci. Technol. 19, 1–14 (2014). https://doi.org/10.1007/s00773-013-0226-1CrossRef

42.

J. He and C. Valeo, “Comparative study of ANNs versus parametric methods in rainfall frequency analysis,” J. Hydrol. Eng. 14, 172–184 (2009). https://doi.org/10.1061/(ASCE)1084-0699(2009)14:2(172)CrossRef

43.

M. Nikaein, “Multi-objective optimization of gas turbine power cycle,” World Acad. Sci. Eng. Technol. 76, 114473 (2011).

44.

S. Zahin, H. H. Latif, S. K. Paul, and A. Azeem, “A comparative analysis of power demand forecasting with artificial intelligence and traditional approach,” Int. J. Bus. Inf. Syst. 13, 359–380 (2013). https://doi.org/10.1504/IJBIS.2013.054469CrossRef

45.

K. Pabreja, “An adaptive neuro-fuzzy inference system based on vorticity and divergence for rainfall forecasting,” Int. J. Comput. Sci. Inf. Secur. 9 (12) (2011).

46.

P. Kumar, Optimization of Gas Turbine Cycle using Optimization Technique (2010).

47.

G. Bonforte, J. Buchgeister, G. Manfrida, and K. J. E. Petela, “Exergoeconomic and exergoenvironmental analysis of an integrated solar gas turbine/combined cycle power plant,” Energy 156, 352–359 (2018). https://doi.org/10.1016/j.energy.2018.05.080CrossRef

48.

Electric Energy Systems: Analysis and Operation, Ed. by A. Gomez-Exposito, A. J. Conejo, and C. Canizares (CRC, Boca Raton, Fla., 2018).

49.

Y. Li and Y. J. E. Xiong, “Thermo-economic analysis of a novel cascade integrated solar combined cycle system,” Energy 145, 116–127 (2018). https://doi.org/10.1016/j.energy.2017.12.128CrossRef

50.

Z. Liu and I. A. Karimi, “Simulation and optimization of a combined cycle gas turbine power plant under part-load operation,” Chem. Eng. Res. Des. 131, 29–40 (2018). https://doi.org/10.1016/j.cherd.2017.12.009CrossRef

51.

L. Malinowski, M. Lewandowska, and F. Giannetti, “Design and analysis of the improved configuration of the secondary circuit for the EU-DEMO power plant,” Fusion Eng. Des. 146, 1035–1038 (2019). https://doi.org/10.1016/j.fusengdes.2019.01.152CrossRef

52.

Y. A. Çengel and M. A. Boles, Thermodynamics: An Engineering Approach, 9th ed. (McGraw-Hill, New York, 2019).

53.

N. Walia, H. Singh, and A. Sharma, “Adaptive neuro-fuzzy inference system — A survey,” Int. J. Comput. Appl. 123 (13), 32–38 (2015). https://doi.org/10.5120/ijca2015905635CrossRef

54.

S. Masoudi, M. Sima, and M. Tolouei-Rad, “Comparative study of ANN and ANFIS models for predicting temperature in machining,” J. Eng. Sci. Technol. 13, 211–225 (2018).

55.

A. Kheirandish, E. Akbari, M. Nilashi, and M. Dahari, “Using ANFIS technique for PEM fuel cell electric bicycle prediction model,” Int. J. Environ. Sci. Technol. 16, 7319–7326 (2019). https://doi.org/10.1007/s13762-019-02392-6CrossRef

56.

P. Sihag, N. Tiwari, and S. Ranjan, “Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS),” ISH J. Hydraul. Eng. 25, 132–142 (2019). https://doi.org/10.1080/09715010.2017.1381861CrossRef

Titel: Modeling of a Combined Cycle Gas Turbine (CCGT) Using an Adaptive Neuro-Fuzzy System
verfasst von: M. B. R. Rodríguez
J. L. M. Rodríguez
C. de H. Fontes
Publikationsdatum: 01.09.2022
Verlag: Pleiades Publishing
Erschienen in: Thermal Engineering / Ausgabe 9/2022
Print ISSN: 0040-6015
Elektronische ISSN: 1555-6301
DOI: https://doi.org/10.1134/S0040601522090038

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