A Genetic Algorithm Optimized ANN for Prediction of Exergy and Energy Analysis Parameters of a Diesel Engine Different Fueled Blends
DOI:
https://doi.org/10.18100/ijamec.1262259Keywords:
Artificial Neural Network, Diesel, Exergy and Energy Analyses, Genetic Algorithm, OptimizationAbstract
In this research, a hybrid artificial neural network (ANN) optimized by a genetic algorithm (GA) was used to estimate energy and exergy analyses parameters. This article presents an approach for estimating energy and exergy analyses parameters with optimized ANN model based on GA (GA-ANN) for different ternary blends consisting of diesel, biodiesel and bioethanol in a single-cylinder, water-cooled diesel engine. The data used in the experiments performed at twelve different engine speeds between 1000 and 3000 rpm with 200 rpm intervals for five different fuel mixtures consisting of fuel mixtures prepared by blends biodiesel, diesel and 5% bioethanol in different volumes constitute the input data of the models. Using these input data, engine torque (ET), amount of fuel consumed depending on fuels and speed (AFC), carbon monoxide emission values (CO), carbon dioxide emission values (CO2), hydrocarbon emission values (HC), nitrogen oxides emission values (NOx), the amount of air consumed (AAC), exhaust gas temperatures (EGT) and engine coolant temperatures (ECT) were estimated with the GA-ANN. In examining the results obtained were examined, it was proved that diesel, biodiesel and bioethanol blends were effective in predicting all the results mentioned in engine studies performed at 200 rpm intervals in the 1000-3000 rpm range. A standard ANN model used in the literature was also proposed to measure the prediction performance of GA-ANN model. The predictive results of both models were compared using various performance indices. As a result, it was revealed that the proposed GA-ANN model reached higher accuracy in estimating the exergy and energy analyses parameters of the diesel engine compared to the standard ANN technique.Downloads
References
Esan, A.O., et al., A concise review on alternative route of biodiesel production via interesterification of different feedstocks. International Journal of Energy Research, 2021. 45(9): p. 12614-12637.
Dhawan, M.S., S.C. Barton, and G.D. Yadav, Interesterification of triglycerides with methyl acetate for the co-production biodiesel and triacetin using hydrotalcite as a heterogenous base catalyst. Catalysis Today, 2021. 375: p. 101-111.
Liu, S.-H., Y.-C. Lin, and K.-H. Hsu, Emissions of regulated pollutants and PAHs from waste-cooking-oil biodiesel-fuelled heavy-duty diesel engine with catalyzer. Aerosol and Air Quality Research, 2012. 12(2): p. 218-227.
Agarwal, A., Biofuels applications as fuels for internal combustion engines, progress in energy and combustion science. Journal Energy and Fuels, 2006. 8: p. 1-38.
Dey, S., et al., A comprehensive study on prospects of economy, environment, and efficiency of palm oil biodiesel as a renewable fuel. Journal of Cleaner Production, 2021. 286: p. 124981.
Aydin, H. and C. Ilkılıc, Effect of ethanol blending with biodiesel on engine performance and exhaust emissions in a CI engine. Applied Thermal Engineering, 2010. 30(10): p. 1199-1204.
Banapurmath, N. and P. Tewari, Performance, combustion, and emissions characteristics of a single-cylinder compression ignition engine operated on ethanol—biodiesel blended fuels. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 2010. 224(4): p. 533-543.
Oğuz, H., I. Sarıtas, and H.E. Baydan, Prediction of diesel engine performance using biofuels with artificial neural network. Expert Systems with Applications, 2010. 37(9): p. 6579-6586.
Tang, Q., et al., Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics. Bioresource Technology, 2021. 339: p. 125581.
Paul, A., et al., Artificial neural network-based prediction of performances-exhaust emissions of diesohol piloted dual fuel diesel engine under varying compressed natural gas flowrates. Journal of Energy Resources Technology, 2018. 140(11).
Arcaklioğlu, E. and İ. Çelıkten, A diesel engine's performance and exhaust emissions. Applied energy, 2005. 80(1): p. 11-22.
Gürgen, S., B. Ünver, and İ. Altın, Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network. Renewable Energy, 2018. 117: p. 538-544.
Aydın, M., S. Uslu, and M.B. Çelik, Performance and emission prediction of a compression ignition engine fueled with biodiesel-diesel blends: A combined application of ANN and RSM based optimization. Fuel, 2020. 269: p. 117472.
Morris, J.D., S.S. Daood, and W. Nimmo, Machine learning prediction and analysis of commercial wood fuel blends used in a typical biomass power station. Fuel, 2022. 316: p. 123364.
Zheng, Y., et al., Prediction of viscosity of biodiesel blends using various artificial model and comparison with empirical correlations. Renewable Energy, 2020. 153: p. 1296-1306.
Uslu, S. and M.B. Celik, Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether. Engineering science and technology, an international journal, 2018. 21(6): p. 1194-1201.
Van Hung, T., et al., Prediction of emission characteristics of a diesel engine using experimental and artificial neural networks. Applied Nanoscience, 2021: p. 1-10.
Grahovac, J., et al., Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks. Renewable Energy, 2016. 85: p. 953-958.
Yilmaz, N., et al., Predicting the engine performance and exhaust emissions of a diesel engine fueled with hazelnut oil methyl ester: the performance comparison of response surface methodology and LSSVM. Journal of Energy Resources Technology, 2016. 138(5).
Yasar, A., B. Sayin Kul, and M. Ciniviz, Modelling the Performance of Fuzzy Expert System for Prediction of Combustion, Engine Performance and Exhaust Emission Parameters of a SI Engine Fueled with Waste Bread Bioethanol-Gasoline Blends. Journal of Energy Resources Technology, 2022: p. 1-28.
Sayin Kul, B. and A. Kahraman, Energy and exergy analyses of a diesel engine fuelled with biodiesel-diesel blends containing 5% bioethanol. Entropy, 2016. 18(11): p. 387.
Sayın, B., An experimental study on energy and exergy analysis for a diesel engine using bio-fuel. 2014, Master's thesis, Department of Mechanical Engineering, Selcuk University, Konya.
Zhao, S., W. Xu, and L. Chen, The modeling and products prediction for biomass oxidative pyrolysis based on PSO-ANN method: An artificial intelligence algorithm approach. Fuel, 2022. 312: p. 122966.
Fan, M., et al., Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO. Scientific reports, 2017. 7(1): p. 1-14.
Ghaedi, M., et al., A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015. 137: p. 1004-1015.
Moghaddam, M.A., R. Golmezergi, and F. Kolahan, Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN–PSO approach. Measurement, 2016. 92: p. 279-287.
Socha, K. and C. Blum, An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural computing and applications, 2007. 16(3): p. 235-247.
Castillo, P.A., et al. SA-Prop: Optimization of multilayer perceptron parameters using simulated annealing. in International Work-Conference on Artificial Neural Networks. 1999. Springer.
De Campos, L.M.L., R.C.L. de Oliveira, and M. Roisenberg, Optimization of neural networks through grammatical evolution and a genetic algorithm. Expert Systems with Applications, 2016. 56: p. 368-384.
Azimi, Y., S.H. Khoshrou, and M. Osanloo, Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network. Measurement, 2019. 147: p. 106874.
Kalathingal, M.S.H., S. Basak, and J. Mitra, Artificial neural network modeling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves. Journal of Food Process Engineering, 2020. 43(1): p. e13128.
Asteris, P.G. and V.G. Mokos, Concrete compressive strength using artificial neural networks. Neural Computing and Applications, 2020. 32(15): p. 11807-11826.
Chen, M., et al., Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 2019. 21(4): p. 3039-3071.
Hagan, M.T., H.B. Demuth, and M. Beale, Neural network design. 1997: PWS Publishing Co.
Mohamad, E.T., et al., An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Computing and Applications, 2017. 28(1): p. 393-406.
Sparks, D.L., R. Hernandez, and L.A. Estévez, Evaluation of density-based models for the solubility of solids in supercritical carbon dioxide and formulation of a new model. Chemical Engineering Science, 2008. 63(17): p. 4292-4301.
Shenfield, A., D. Day, and A. Ayesh, Intelligent intrusion detection systems using artificial neural networks. Ict Express, 2018. 4(2): p. 95-99.
Cui, K. and X. Jing, Research on prediction model of geotechnical parameters based on BP neural network. Neural Computing and Applications, 2019. 31(12): p. 8205-8215.
Rocke, D., Genetic Algorithms+ Data Structures= Evolution programs (3rd. Journal of the American Statistical Association, 2000. 95(449): p. 347.
Garud, K.S., S. Jayaraj, and M.Y. Lee, A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models. International Journal of Energy Research, 2021. 45(1): p. 6-35.
Lambora, A., K. Gupta, and K. Chopra. Genetic algorithm-A literature review. in 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon). 2019. IEEE.
Azadeh, A., et al., Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Applied mathematics and computation, 2007. 186(2): p. 1731-1741.
Kaelo, P. and M. Ali, Integrated crossover rules in real coded genetic algorithms. European Journal of Operational Research, 2007. 176(1): p. 60-76.
Tsoulos, I.G., Modifications of real code genetic algorithm for global optimization. Applied Mathematics and Computation, 2008. 203(2): p. 598-607.
Katoch, S., S.S. Chauhan, and V. Kumar, A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 2021. 80(5): p. 8091-8126.
Mirjalili, S., Genetic algorithm, in Evolutionary algorithms and neural networks. 2019, Springer. p. 43-55.
Kumar, M., et al., Genetic algorithm: Review and application. Available at SSRN 3529843, 2010.
Sexton, R.S., R.E. Dorsey, and J.D. Johnson, Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. European Journal of Operational Research, 1999. 114(3): p. 589-601.
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