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.
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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performance prediction. Energy Convers. Manag. , 83, 3, 14-24, 2014.
https://doi.org/ 10.1016/ j.enconman.2014.03.073
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load operated combined cycle power plant using machine learning
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computing based multi-objective optimization of steam cycle power
plant using NSGA-II and ANN. Applied Soft Computing, 12, 3648–55,
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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,
Volume: 10 Issue: 1, 380 - 392, 15.01.2021
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.
[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
[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
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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. NOHU J. Eng. Sci. 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”, NOHU J. Eng. Sci., 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. NOHU J. Eng. Sci. 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. NOHU J. Eng. Sci. 2021;10(1):380-92.