The network for test data yielded the R2 values of 0.999 and the mean % errors for test data are smaller than 3.5% for the performance and 8% for the emissions.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study, the performance and exhaust emission values in a four-stroke, four-cylinder turbocharged Diesel engine fueled with ethanol-diesel fuel blends (10% and 15% in volume) were investigated by using Artificial Neural Network (ANN) modeling.
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Key Findings & Data:The outputs are the engine performance values (engine torque, power, brake mean effective pressure, and specific fuel consumption) and exhaust emissions (SO2, CO2, NOx, and smoke level (N%)) which were measured in the experiments.The back-propagation learning algorithm with three different variants, a single layer, and logistic sigmoid transfer function (log-sig) was used in the network.
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Conclusion & Impact:The network for test data yielded the R2 values of 0.999 and the mean % errors for test data are smaller than 3.5% for the performance and 8% for the emissions.
In this study, the performance and exhaust emission values in a four-stroke, four-cylinder turbocharged Diesel engine fueled with ethanol-diesel fuel blends (10% and 15% in volume) were investigated by using Artificial Neural Network (ANN) modeling. The actual data derived from engine test measurements was applied in model training, cross-validation, and testing. To train the network, fuel injection pressures, throttle positions, engine speed, and ethanol fuel blend ratios were used as input layer in the network. The outputs are the engine performance values (engine torque, power, brake mean effective pressure, and specific fuel consumption) and exhaust emissions (SO2, CO2, NOx, and smoke level (N%)) which were measured in the experiments.The back-propagation learning algorithm with three different variants, a single layer, and logistic sigmoid transfer function (log-sig) was used in the network. By using the weights of the network, formulations were given for each output. The network for test data yielded the R2 values of 0.999 and the mean % errors for test data are smaller than 3.5% for the performance and 8% for the emissions.
Arcaklioğlu, E., Can, Ö., Öztürk, E. (2022). Artificial neural network based determination of the performance and emissions of a Diesel engine using ethanol-diesel fuel blends. International Journal of Automotive Science and Technology, Cilt: 5 - Sayı: 1, 43–51. https://ajindex.com/articles/540457
Can, Ö. Çelikten. İ and Usta, N. (2004). Effects of etha-nol addition on performance and emissions of a turbo-charged indirect injection Diesel engine running at dif-ferent injection pressures. Energy Conversion and Man-agement, 45, 2429-2440
Can, O. Celikten. İ and Usta. N. (2005). Effects of etha-nol blended diesel fuel on exhaust emissions From a Diesel engine, Journal of Pamukkale University Engi-neering College-Journal of Engineering Sciences, 3, 11, 325-334
Balat, M. and Balat, H. (2009) Recent trends in global production and utilization of bio-ethanol fuel, Applied Energy, 86, 11, 2273-2282
Jinlin, Han. Somers, L. M. T. Cracknell, R. Joedicke, A. Wardle, R. Mohan, R. V. R. (2009) Experimental inves-tigation of ethanol/diesel dual-fuel combustion in a heavy-duty diesel engine, Fuel, 275, 117867
Rakopoulos, C. D. Rakopoulos, D. C. Kosmadakis, G. M. Papagiannakis, R. G. (2019). Experimental compara-tive assessment of butanol or ethanol diesel-fuel extend-ers impact on combustion features, cyclic irregularity, and regulated emissions balance in heavy-duty diesel engine, Energy, 174, 1145-1157
Liu, H. Ma, G. Hu, B. Zheng, Z. Yao, M. (2018). Effects of port injection of hydrous ethanol on combustion and emission characteristics in dual-fuel reactivity controlled compression ignition (RCCI) mode, Energy, 145, 592-602
Torres-Jimenez, E. Jerma, M. S. Gregorc, A. Lisec, I. Dorado, M. P. Kegl, B. (2011). Physical and chemical properties of ethanol–diesel fuel blends, Fuel, 90, 2, 795-802
Singh, G. N. and Bharj, R. S. (2019). Study of physical-chemical properties for 2nd generation ethanol-blended diesel fuel in India, Sustainable Chemistry and Pharma-cy, 12, 100130
Kwanchareon, P. Luengnaruemitchai, A. Jai-In, S. (2007) Solubility of a diesel– biodiesel–ethanol blend, its fuel properties, and its emission characteristics from diesel engine. Fuel, 86, 7–8, 1053–61
Jin, C. Pang, X. Zhang, X. Wu, S. Ma, M. Xiang, Y. Ma, J. Ji, J. Wang, G. Liu, H. (2019). Effects of C3–C5 al-cohols on solubility of alcohols/diesel blends, Fuel, 236, 65-74
Lapuerta, M. Armas, O. García-Contreras, R. (2007). Stability of diesel–bioethanol blends for use in diesel engines. Fuel, 86, (10–11), 1351–1357
Reyes, Y. Aranda, D. A. G. Santander, L. A. M. Cavado, A. Belchior, C. R. P. (2009). Action principles of cosol-vent additives in ethanol diesel blends: stability studies. Energy Fuels, 23, 5, 2731–2735