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Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant

Year 2021, , 380 - 392, 15.01.2021

Andaç Batur Çolak

https://doi.org/10.28948/ngumuh.754411

Abstract

In this study artificial neural network (ANN) has been developed in order to estimate the electricity production of cogeneration power plant, which produces a total of 11.52 MW electric power, consisting of two V type and 12 cylinders each of which is 5.760 kW diesel engines running with heavy fuel oil no 6. In the ANN which was developed for the estimation of electric power generation of cogeneration, power plant(W), Time period (t), working hours (h), fuel consumption (m) and internal power consumption (Wp) values were used as input variables. After evaluating the performance of different ANNs, an ANN, consisting of one hidden layer and 10 neurons, was considered to be the most ideal one. As a result of the comparison with experimental data, it is concluded that this model estimates the electricity generation values of the cogeneration power plant with an R-value of 0,99073 and mean square error 4.734e-8.

Keywords

cogeneration diesel engine Power plant Electricity consumption Artificial Neural Network

Thanks

The author would like to thank the management of the BIRKO factory for their cooperation and supply of data during this study.

References

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  • [37] P. Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Int J Electr Power Energy Syst, 60, 126-40, 2014.
  • [38] F. Hajabdollahi, Z. Hajabdollahi, and H. Hajabdollahi, Soft computing based multi-objective optimization of steam cycle power plant using NSGA-II and ANN. Applied Soft Computing, 12, 3648–55, 2012.
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  • [53] C.-T. Hsu, H.-J. Chuang and C.-S. Chen, Adaptive load shedding for an industrial petroleum cogeneration system. Expert Systems with Applications, 38 13967-974, 2011. https://doi.org/10.1016/j.eswa.2011.04.204
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Dizel motorlu kojenerasyon santralinin elektrik enerjisi üretiminin yapay sinir ağı (YSA) ile tahmini üzerine deneysel çalışma

Year 2021, , 380 - 392, 15.01.2021

Andaç Batur Çolak

https://doi.org/10.28948/ngumuh.754411

Abstract

Bu çalışmada iki adet V tipi 12 silindirli dizel motordan oluşan ve her biri 5.760 kW olmak üzere toplam 11.52 MW elektrik enerjisi üreten kojenerasyon enerji santralinin elektrik üretiminin tahmin edilmesi için yapay sinir ağı (YSA) geliştirilmiştir. 6 no'lu fuel oil ile çalışan kojenerasyon enerji santralinin elektrik enerjisi üretiminin (W) tahmini için geliştirilen YSA'da, zaman (t), çalışma saatleri (h), yakıt tüketimi (m) ve iç tüketim (Wp) değerleri giriş değişkenleri olarak kullanılmıştır. Farklı YSA'ların performansı değerlendirildikten sonra, bir gizli katman ve 10 nörondan oluşan YSA en ideal model olarak değerlendirilmiştir. Deneysel verilerle yapılan karşılaştırma sonucunda, bu modelin kojenerasyon enerji santralinin elektrik üretim değerlerini 0,99073 R değeri ve 4.734e-8 MSE ile tahmin edebileceği sonucuna varılmıştır.

Keywords

Kojenerasyon Dizel motor Enerji santrali Elektrik tüketimi Yapay Sinir Ağı

References

  • [1] International Energy Agency, World energy outlook. ISBN: 978-92-6420805-6. 2014.
  • [2] S.D. Oh, H.S. Pang, S.M. Kim and H.Y. Kwak, Exergy analysis for a gas turbine cogeneration system. J Eng GasTurboPower,118:78291,1996.https://doi.org/10.1115/1.2816994
  • [3] Nato, 3rd World Countries Cogeneration Case Studies, 2001.
  • [4] Energy Nexus Group, Technology characterization: reciprocating engines, Environmental Protection Agency Climate Protection Partnership Division, Washington DC, 2002.
  • [5] R. Benelmir and M. Feidt, Energy cogeneration systems and energy management strategy, Energy Conversion and Management 39, 1791– 1802, 1998. https://doi.org/10.1016/S0196-8904(98)00055-7
  • [6] J.A. Orlando, Cogeneration design guide. USA: ASHRAE, Inc; 1996.
  • [7] C.A. Frangopoulos, EDUCOGEN, The European educational tools on cogeneration. European Commission. December 2001.
  • [8] Small-scale cogeneration, why? In which case? A guide for decision makers. European Commission, Directorate General for Energy DGXVII; July; 1999.
  • [9] R. Benelmir and M. Feidt, Energy cogeneration systems and energy management strategy. Energy Convers Manage 39(16–18), 1791–802, 1998. https://doi.org/10.1016/S0196-8904(98)00055-7
  • [10] B.W. Wilkinson and R.W. Barnes, Cogeneration of electricity and useful heat. CRC Press Inc.; 1980.
  • [11] G. Major, Small scale cogeneration. The Netherlands: Centre for the Analysis and Dissemination of Demonstrated Energy Technologies. CADDET Energy Efficiency Analysis Series 1. IEA/OECD; 1995.
  • [12] M. Kanoglu. and İ. Dincer, Performance assessment of cogeneration plants, Energy Conversion and Management, 50, 76-81, 2008. https://doi.org/10.1016/ j.enconman.2008.08.029
  • [13] J. Kartano, Power plant business. Wartsila NSD, Finland; 2002.
  • [14] A. Abusoglu and M. Kanoglu, Exergetic and thermoeconomic analyses of diesel engine powered cogeneration: Part 2 – Application, Applied Thermal Engineering 29, 242–249, 2009.
  • [15] M.A. Rosen, Energy- and exergy-based comparison of coal-fired and nuclear steam power plants. Exergy: Int J;1(3),180–92, 2001. https://doi.org/10.1016/S1164-0235(01)00024-3
  • [16] M.A. Rosen, M.N. Le and İ. Dincer, Efficiency analysis of a cogeneration and district energy system. Appl Thermal Eng, 25, 147-59, 2005. https://doi.org/ 10.1016/j.applthermaleng.2004.05.008
  • [17] L. Ozgener, A. Hepbasli and İ. Dincer, Performance investigation of two geothermal district heating systems for building applications: energy analysis. EnergyBuild, 38(4), 286-92. 2006. https://doi.org/ 10.1016/ j.enbuild.2005.06.021
  • [18] M.A. Rosen, Reductions in energy use and environmental emissions achievable with utility-based cogeneration: simplified illustrations for Ontario. Appl Energy;61:163–74, 1998.
  • [19] C.D. Rakopoulos and E.G. Giakoumis, Simulation and exergy analysis of transient diesel engine operation. Energy, 22(9), 875-85, 1997. https://doi.org/ 10.1016/S0360-5442(97)00017-0
  • [20] K. Nakonieczny, Entropy generation in a diesel engine turbocharging system. Energy, 27:1027–56, 2002.
  • [21] C.Y. Lin, Reduction of particulate matter and gaseous emission from marine diesel engines using a catalyzed particulate filter. Ocean Eng, 29, 1327–41, 2002.
  • [22] A. Parlak, H. Yasar and B. Sahin, Performance and exhaust emission characteristics of a lower compression ratio LHR diesel engine. Energy Conversion Management 44, 163-75. 2003. https://doi.org/10.1016/S01968904(01)00201-1
  • [23] C.M. Nam and B.M. Gibbs, Application of the thermal DeNOx process to diesel engine DeNOx: An experimental and kinetic modeling study. Fuel 81, 1359–67, 2002.
  • [24] Y. Ust, B. Sahin and T. Yilmaz, Optimization of a regenerative gas-turbine cogeneration system based on a new exergetic performance criterion:exergetic performance coefficient. Proc Institut Mech Engineers A – J Power Energy, 221(A4), 447–57, 2007. https://doi.org/10.1243/09576509JPE379
  • [25] I.S. Ertesva, Exergetic comparison of efficiency indicators for combined heat and power (CHP). Energy, 32, 2038–50, 2007.
  • [26] A. Khaliq and T.A. Khan, Energetic and exergetic efficiency analysis of an indirect fired air-turbine combined heat and power system. Int J Exergy, 4(1), 38- 53,2007.https://doi.org/10.1504/IJEX.2007.011578
  • [27] G. Bidini, U. Desideri, S. Saetta and P.P. Bacchini, Internal combustion engine combined heat and power plants: Case study of the University of Perugia power plant. Appl Thermal Eng, 18, 401–12, 1998.
  • [28] A. Castillo, Risk analysis and management in power outage and restoration: a literature survey. Electr Power Syst.Res.107:9-15, 2014. https://doi.org/10.1016/ j.epsr.2013.09.002
  • [29] J.H. Lee, T.S. Kim and E.H. Kim, Prediction of power generation capacity of a gas turbine combined cycle cogeneration plant, Energy 124, 187-197, 2017.
  • [30] I.H. Witten and E. Frank, Data mining : practical machine learning tools and techniques. second ed. CA: MORGAN Kaufmann Publisher; p. 4-9, 2005.
  • [31] Y. Song, C.W. Gu and X.X. Ji, Development and validation of a full- range performance analysis model for a three-spool gas turbine with turbine cooling. Energy, 89, 545-57, 2015. https://doi.org/10.1016/ j.energy.2015.06.015
  • [32] J. Duan, L. Sun, G. Wang and F. Wu, Nonlinear modeling of regenerative cycle micro gas turbine. Energy, 91, 168-75, 2015.
  • [33] Li YG. Gas turbine performance and health status estimation using adaptive gas path analysis. J Eng Gas Turbines Power, 041701, 132, 2010. https://doi.org/ 10.1115/1.3159378
  • [34] J. Smrekar, D. Pandit, M. Fast, M. Assadi and S. De, Prediction of power output of a coal-fired power plant by artificial neural network. Neural Comput Appl, 19, 725-40, 2009. https://doi.org/10.1007/s00521- 009-0331-6
  • [35] Y. Tunckaya and E. Koklukaya, Comparative prediction analysis of 600 MWe coal fired power plant production rate using statistical and neural-based models. J Energy Inst, 88, 11-8, 2015.
  • [36] S.Z. Boksteen, J.P. Van Buijtenen, R. Pecnik and D. Van Der Vecht, Bayesian calibration of power plant models for accurate performance prediction. Energy Convers. Manag. , 83, 3, 14-24, 2014. https://doi.org/ 10.1016/ j.enconman.2014.03.073
  • [37] P. Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Int J Electr Power Energy Syst, 60, 126-40, 2014.
  • [38] F. Hajabdollahi, Z. Hajabdollahi, and H. Hajabdollahi, Soft computing based multi-objective optimization of steam cycle power plant using NSGA-II and ANN. Applied Soft Computing, 12, 3648–55, 2012.
  • [39] O. Arslan, Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34. Energy, 36, 2528–34, 2011. https://doi.org/10.1016/j.energy.2011.01.045
  • [40] M.V. J.J. Suresh, K.S. Reddy, and A.K. Kolar, ANN–GA based optimization of a high ash coal-fired supercritical power plant. Applied Energy, 88, 67–4873, 2011.
  • [41] M.M. Rashidi, N. Galanis, F. Nazari, A. Basiri Parsa, and L. Shamekhi, Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network. Energy, 36, 5728–40, 2011.
  • [42] P. Olausson, D. Häggståhl, J. Arriagada, E. Dahlquist and M. Assadi, Hybrid model of an evaporative gas turbine power plant utilizing physical models and artificial neural network. Proceedings of the ASME Turbo Expo. (2003) Atlanta, Georgia, USA., 2003. https://doi.org/10.1115/GT2003-38116
  • [43] S. De, M. Kaiadi, M. Fast and M. Assadi, Development of an artificial neural network model for the steam process of a coal biomass co-fired combined heat and power (CHP) plant in Sweden. Energy, 32, 2099–2109, 2007.
  • [44] T. Bekat, M. Erdogan, F. Inal, and A. Genc, Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks. Energy, 45 882-7, 2012. https://doi.org/10.1016/j.energy.2012.06.075
  • [45] J. Ma, B.-S. Wang and Y.-G. Ma, ANN-based real-time parameter optimization via GA for superheater model in power plant simulator. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC, 4, 2269–73, 2008.
  • [46] X. Du, L. Liu, X. Xi, L. Yang, Y. Yang, Z. Liu, Back pressure prediction of the direct air cooled power generating unit using the artificial neural network model. Applied Thermal Engineering, 31, 3009- 14. 2011.https://doi.org/10.1016/j.applthermaleng.2011.05.034
  • [47] L. Pan, D. Flynn and M. Cregan, Statistical model for power plants performance monitoring and analysis. Universities Power Engineering Conference, UPEC 2007, 121–126. 42nd International Conference. 2007.
  • [48] S. Lu and B.W. Hogg, Dynamic and nonlinear modelling of power plant by physical principles and neural networks. International Journal of Electrical Power & Energy Systems, 22, 67–78, 2000. https://doi.org/10.1016/S0142-0615(99)00036-8
  • [49] F. Fantozzi and U. Desideri, Simulation of power plant transients with artificial neural networks: application to an existing combined cycle. Proceedings of the Institution of Mechanical Engineers Part A, 212, 299–313, 1998.
  • [50] S. Tronci, R. Baratti and A. Servida, Monitoring pollutant emissions in a 4.8 MW power plant through neural network. Neurocomputing, 43, 3–15, 2002. https://doi.org/10.1016/S0925- 2312(01)00617-8
  • [51] D. Flynn, J. Ritchie and M. Cregan, Data mining techniques applied to power plant performance monitoring. Paper presented at IFAC world congress, Prague, 2005.
  • [52] C.E. Romero and J. Shan, Development of an artificial neural network-based software for prediction of power plant canal water discharge temperature. Expert Systems with Applications, 29, 831–838, 2005. https://doi.org/10.1016/j.eswa.2005.06.009
  • [53] C.-T. Hsu, H.-J. Chuang and C.-S. Chen, Adaptive load shedding for an industrial petroleum cogeneration system. Expert Systems with Applications, 38 13967-974, 2011. https://doi.org/10.1016/j.eswa.2011.04.204
  • [54] M. Moghavvemi, S.S. Yang and M.A. Kashem, A practical neural network approach for power generation automation. Proceedings of Energy Management and Power Delivery. 1998 International Conference, 1, 305–310, 1998.
  • [55] A.B. Çolak, O. Yıldız, M. Bayrak and B.S. Tezekici, Experimental study for predicting the specific heat of water based Cu-Al2O3 hybrid nanofluid using artificial neural network and proposing new correlation, International Journal of Energy Research, 1-18, 2020. https://doi.org/10.1002/er.5417
  • [56] MAN, 32/40 Project Guide – Stationary Four-stroke diesel engines, version 1.5, 2013.
  • [57] M. Bayrak and A. Gungor, Fossil fuel sustainability: Exergy assessment of a cogeneration system, Int. J. Energy Res, 35, 162-68, 2011. https://doi.org/ 10.1002/er.1759
  • [58] M.H. Esfe, M. Reiszadeh, S. Esfandeh and M. Afrand, Optimization of MWCNTs (10%) – Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network, Physica A 512, 731–744, 2018.
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There are 61 citations in total.

Details

Primary Language English
Subjects Mechanical Engineering
Journal Section Mechanical Engineering
Authors

Andaç Batur Çolak 0000-0001-9297-8134

Publication Date January 15, 2021
Submission Date June 18, 2020
Acceptance Date October 1, 2020
Published in Issue Year 2021

Cite

APA Çolak, A. B. (2021). Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 380-392. https://doi.org/10.28948/ngumuh.754411
AMA Çolak AB. Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant. NÖHÜ Müh. Bilim. Derg. January 2021;10(1):380-392. doi:10.28948/ngumuh.754411
Chicago Çolak, Andaç Batur. “Experimental Study for Artificial Neural Network (ANN) Based Prediction of Electric Energy Production of Diesel Engine Based Cogeneration Power Plant”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10, no. 1 (January 2021): 380-92. https://doi.org/10.28948/ngumuh.754411.
EndNote Çolak AB (January 1, 2021) Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10 1 380–392.
IEEE A. B. Çolak, “Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant”, NÖHÜ Müh. Bilim. Derg., vol. 10, no. 1, pp. 380–392, 2021, doi: 10.28948/ngumuh.754411.
ISNAD Çolak, Andaç Batur. “Experimental Study for Artificial Neural Network (ANN) Based Prediction of Electric Energy Production of Diesel Engine Based Cogeneration Power Plant”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10/1 (January 2021), 380-392. https://doi.org/10.28948/ngumuh.754411.
JAMA Çolak AB. Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant. NÖHÜ Müh. Bilim. Derg. 2021;10:380–392.
MLA Çolak, Andaç Batur. “Experimental Study for Artificial Neural Network (ANN) Based Prediction of Electric Energy Production of Diesel Engine Based Cogeneration Power Plant”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 10, no. 1, 2021, pp. 380-92, doi:10.28948/ngumuh.754411.
Vancouver Çolak AB. Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant. NÖHÜ Müh. Bilim. Derg. 2021;10(1):380-92.

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