Machine Learning-Guided Optimization of Biofuel Blends for Enhanced Engine Efficiency and Emission Reduction
Keywords:
Palm biodiesel, Engine Thermal Efficiency, Machine Learning Model, Multi Objective Genetic Algorithm [MOGA], Pareto Optimal solutions, Efficient Optimization
Abstract
India heavily relies on imported foreign crude oil, prompting the need for effective solutions to reduce this dependency. One such solution is the blending of Biodiesel, Palm oil, and Ethanol with original diesel. The crucial concern lies in determining the optimal blending ratio that maximizes Engine Efficiency while maintaining reasonable levels of Oil Consumption and NOx emission. To address this, experimental data are collected from the paper [1] which systematically blends biodiesel. Experimental data involved three input parameters [Load, Palm Biodiesel, Ethanol] and three output parameters [Motor Brake Thermal Efficiency (BTE), Brake Specific Fuel Consumption (BSFC), and Nitrogen Oxides (NOx)], with 40 different runs. The prediction was accomplished using 26 Machine Learning Models, including Gaussian Process Regression, Support Vector regression, ANN, Tree and Linear Regression and others. Among the 26 models considered in the analysis, three models emerged as the top performers. The Stepwise Linear Regression Model [SLRM] yielded the highest Brake Thermal Efficiency (BTE), the Fine Tree Regression Model [FTRM] achieved the lowest Brake Specific Energy Consumption [BSFC], and the Matern 5/2 Gaussian Process Regression Model [MGPRM] demonstrated the lowest Nitrogen Oxide (NOx) emission. These models displayed a range of Root Mean Square Error (RMSE) and R-squared(validation) values: 0.02077–0.02333 and 0.99 for SLRM, 0.03789–0.03907 and 0.98 for FTRM & 0.02184–0.02296 and 0.99 for MGPRM. Moving forward, a multi-objective optimization approach has been undertaken to simultaneously maximize BTE while minimizing both BSFC and NOx emissions. To accomplish this, a Multi Objective Genetic Algorithm [MOGA] is employed to identify the Pareto Optimal Solution. The optimization process [MOGA] resulted in a series of 18 Pareto Optimal Solutions. These solutions provide insights on the appropriate blend ratios of Load, Palm Biodiesel and Ethanol in order to maximize Engine Thermal Efficiency while minimizing Fuel Consumption and NOx emissions.
References
[1] Ghadikolaei MA, Cheung CS, Yung KF. Study of combustion, performance and emissions of diesel engine fueled with diesel/biodiesel/alcohol blends having the same oxygen concentration. Energy 2018;157:258–69. https://doi.org/10.1016/j.energy.2018.05.164.
[2] Agarwal AK, Dhar A. Experimental investigations of performance, emission and combustion characteristics of Karanja oil blends fuelled DICI engine. Renewable Energy 2013;52:283–91. https://doi.org/10.1016/j.renene.2012.10.015.
[3] Agarwal AK, Gupta JG, Dhar A. Potential and challenges for large-scale application of biodiesel in automotive sector. Prog Energy Combust Sci 2017;61:113–49. https://doi.org/10.1016/j.pecs.2017.03.002.
[4] Dey S, Deb M, Das PK. Chemical Characterization and Tribological Behavior of Kitchen Chimney Dump Lard (KCDL) as a Bio-lubricant. Revue Des Composites et Des Materiaux Avances 2019;29. 10.18280/rcma.290303.
[5] Aydin H, Ilkiliç C. Effect of ethanol blending with biodiesel on engine performance and exhaust emissions in a CI engine. Appl Therm Eng 2010;30:1199–204. https://doi.org/10.1016/j.applthermaleng.2010.01.037.
[7] Dey S, Reang NM, Das PK, Deb M. A comprehensive study on prospects of economy, environment, and efficiency of palm oil biodiesel as a renewable fuel. J Cleaner Prod 2020. https://doi.org/10.1016/j.jclepro.2020.124981.
[8] Guido C, Beatrice C, Napolitano P. Application of bioethanol / RME / diesel blend in a Euro5 automotive diesel engine: potentiality of closed loop combustion control technology. Appl Energy 2013;102:13–23. https://doi.org/10.1016/j.apenergy.2012.08.051.
[9] Gulum M, Bilgin A. An experimental optimization research of methyl and ethyl esters production from safflower oil. Environ Climate Technol 2018;22:132–48. https://doi.org/10.2478/rtuect-2018-0009.
[10] Mohiddin MN, Saleh AA, Reddy ANR, Hamdan S. A study on chicken fat as an alternative feedstock: Biodiesel production, fuel characterisation, and diesel engine performance analysis. Int J Automotive and Mech Eng 2018;15:5535–46.
10.15282/ijame.15.3.2018.10.0425.
[11] Ali OM, Mamat R, Abdullah NR, Abdullah AA. Analysis of blended fuel properties and engine performance with palm biodiesel-diesel blended fuel. Renewable Energy 2015;86:59–67. https://doi.org/10.1016/j.renene.2015.07.103.
[12] Zaharin MSM, Abdullah NR, Najafi G, Sharudin H, Yusaf T. Effects of physicochemical properties of biodiesel fuel blends with alcohol on diesel engine performance and exhaust emissions: a review. Renew Sustain Energy Rev 2017;79: 475–93. https://doi.org/10.1016/j.rser.2017.05.035.
[13] Di Y, Cheung CS, Huang Z. Comparison of the effect of biodiesel-diesel and ethanol- diesel on the gaseous emission of a direct-injection diesel engine. Atmos Environ 2009;43:2721–30. https://doi.org/10.1016/j.atmosenv.2009.02.050.
[14] Ren Y, Huang ZH, Jiang DM, Li W, Liu B, Wang XB. Effects of the addition of ethanol and cetane number improver on the combustion and emission characteristics of a compression ignition engine. Proc Inst Mech Eng, Part D: J Automobile Eng 2008;222:1077–87. https://doi.org/10.1243/09544070JAUTO516.
[15] EL-Seesy AI, Kayatas Z, Takayama R, He Z, Kandasamy S, Kosaka H. Combustion and emission characteristics of RCEM and common rail diesel engine working with diesel fuel and ethanol/hydrous ethanol injected in the intake and exhaust port: Assessment and comparison. Energy Conversion and Management 2020;205: 112453. 10.1016/j.enconman.2019.112453.
[16] Dey S, Reang NM, Deb M, Das PK. Study on performance-emission trade-off and multi-objective optimization of diesel-ethanol-palm biodiesel in a single cylinder CI engine: a Taguchi-fuzzy approach ARTICLE HISTORY. Energy Sources Part A 2020;1–21. https://doi.org/10.1080/15567036.2020.1767234.
[17] Yesilyurt MK, Aydin M. Experimental investigation on the performance, combustion and exhaust emission characteristics of a compression-ignition engine fueled with cottonseed oil biodiesel/diethyl ether/diesel fuel blends. Energy Convers Manage 2020;205:112355. https://doi.org/10.1016/j.enconman.2019.112355.
[18] Qi DH, Chen H, Geng LM, Bian YZ. Effect of diethyl ether and ethanol additives on the combustion and emission characteristics of biodiesel-diesel blended fuel engine. Renewable Energy 2011;36:1252–8. https://doi.org/10.1016/j.renene.2010.09.021
[19] Tosun E, Aydin K, Bilgili M. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alexandria Engineering Journal 2016;55:3081–9. https://doi.org/10.1016/j.aej.2016.08.011
[20] Atmanli A, Yüksel B, Ileri E, Deniz Karaoglan A. Response surface methodology based optimization of diesel-n-butanol -cotton oil ternary blend ratios to improve engine performance and exhaust emission characteristics. Energy Convers Manage
2015;90:383–94. https://doi.org/10.1016/j.enconman.2014.11.029
[21] Sakthivel R, Ramesh K, Joseph John Marshal S, Sadasivuni KK. Prediction of performance and emission characteristics of diesel engine fuelled with waste biomass pyrolysis oil using response surface methodology. Renewable Energy 2019;136:91–103. 10.1016/J.RENENE.2018.12.109.
[22] Krishnamoorthi M, Malayalamurthi R, Mohamed Shameer P. RSM based optimization of performance and emission characteristics of DI compression ignition engine fuelled with diesel/aegle marmelos oil/diethyl ether blends at varying compression ratio, injection pressure and injection timing. Fuel 2018;221: 283–97. https://doi.org/10.1016/j.fuel.2018.02.070
[23] Fang W, Kittelson DB, Northrop WF. Optimization of reactivity-controlled compression ignition combustion fueled with diesel and hydrous ethanol using response surface methodology. Fuel 2015;160:446–57. https://doi.org/10.1016/j.fuel.2015.07.055
[24] Hirkude JB, Padalkar AS. Performance optimization of CI engine fuelled with waste fried oil methyl ester-diesel blend using response surface methodology. Fuel 2014; 119:266–73. https://doi.org/10.1016/j.fuel.2013.11.039.
[25] Ileri E, Karaoglan AD, Atmanli A. Response surface methodology based prediction of engine performance and exhaust emissions of a diesel engine fuelled with canola oil methyl ester. J Renewable Sustainable Energy 2013;5:033132. https://doi.org/ 10.1063/1.4811801.
[26] Shivakumar, Srinivasa Pai P, Shrinivasa Rao BR. Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings. Applied Energy 2011;88:2344–54. 10.1016/j.apenergy.2010.12.030.
[27] Taghavifar HH, Taghavifar HH, Mardani A, Mohebbi A, Khalilarya S, Jafarmadar S. Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine. J Cleaner Prod 2016;112:1729–39. https://doi.org/10.1016/j.jclepro.2015.03.035
[28] Prasada Rao K, Victor Babu T, Anuradha G, Appa Rao BV. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN). Egypt J Pet 2017;26:593–600. https://doi.org/10.1016/j.ejpe.2016.08.006
[29] Javed S, Satyanarayana Murthy YV V, Baig RU, Prasada Rao D. Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends. J Natural Gas Sci Eng 2015;26:549–57. doi: 10.1016/j.jngse.2015.06.041.
[30] Uslu S, Celik MB. Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether. Eng Sci Technol Int J 2018;21:1194–201. https://doi.org/10.1016/j.jestch.2018.08.017
[31] Aryasomayajula Venkata Satya Lakshmi SB, Subramania Pillai N, Khadhar Mohamed MSB, Narayanan A. Biodiesel production from rubber seed oil using calcined eggshells impregnated with Al2O3 as heterogeneous catalyst: a comparative study of RSM and ANN optimization. Brazilian J Chem Eng 2020;37: 351–68. https://doi.org/10.1007/s43153-020-00027-9
[32] Hariharan N, Senthil V, Krishnamoorthi M, Karthic SV. Application of artificial neural network and response surface methodology for predicting and optimizing dual-fuel CI engine characteristics using hydrogen and bio fuel with water injection. Fuel 2020;270:117576. https://doi.org/10.1016/j.fuel.2020.117576
[33] Hammoudi A, Moussaceb K, Belebchouche C, Dahmoune F. Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Constr Build Mater 2019;209:425–36. https://doi.org/10.1016/j.conbuildmat.2019.03.119
[34] Samuel OD, Okwu MO. Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production. Energy Sources Part A 2019;41:1049–61. https://doi.org/10.1080/15567036.2018.1539138
[35] Astray G, Gullon ´ B, Labidi J, Gullon ´ P. Comparison between developed models using response surface methodology (RSM) and artificial neural networks (ANNs) with the purpose to optimize oligosaccharide mixtures production from sugar beet pulp. Ind Crops Prod 2016;92:290–9. https://doi.org/10.1016/j.indcrop.2016.08.011
[36] Ayoola AA, Hymore FK, Omonhinmin CA, Babalola PO, Fayomi OSI, Olawole OC, et al. Response surface methodology and artificial neural network analysis of crude palm kernel oil biodiesel production. Chem Data Collect 2020;28:100478. https://doi.org/10.1016/j.cdc.2020.100478
[37] Chouaibi M, Daoued K Ben, Riguane K, Rouissi T, Ferrari G. Production of bioethanol from pumpkin peel wastes: comparison between response surface methodology (RSM) and artificial neural networks (ANN). Industrial Crops and Products 2020;155:112822. 10.1016/j.indcrop.2020.112822.
[38] Uslu S. Optimization of diesel engine operating parameters fueled with palm oil- diesel blend: Comparative evaluation between response surface methodology (RSM) and artificial neural network (ANN). Fuel 2020;276:117990. https://doi.org/10.1016/j.fuel.2020.117990
[39]. Dey, Suman, Narath Moni Reang, Pankaj Kumar Das, and Madhujit Deb. "Comparative study using RSM and ANN modelling for performance-emission prediction of CI engine fuelled with bio-diesohol blends: A fuzzy optimization approach." Fuel 292 (2021): 120356. https://doi.org/10.1016/j.fuel.2021.120356
[40]. Zheng, M., Wang, F., Hu, X., Miao, Y., Cao, H., & Tang, M. (2022). A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models. Axioms, 11(11). https://doi.org/10.3390/axioms11110607
[41]. Wang, Z., Zhang, X., Zhang, Z. K., & Sheng, D. (2022). Credit portfolio optimization: A multi-objective genetic algorithm approach. Borsa Istanbul Review, 22(1). https://doi.org/10.1016/j.bir.2021.01.004
[42]. Giordano, P. C., Pereyra, V., Beccaria, A. J., Vero, S., & Goicoechea, H. C. (2020). Application of pareto-optimal front as an option to desirability function for the optimization of a microbiological process. Microchemical Journal, 155. https://doi.org/10.1016/j.microc.2020.104682
[43]. Yu, W., Li, B., Jia, H., Zhang, M., & Wang, D. (2015). Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, 88. https://doi.org/10.1016/j.enbuild.2014.11.063
[2] Agarwal AK, Dhar A. Experimental investigations of performance, emission and combustion characteristics of Karanja oil blends fuelled DICI engine. Renewable Energy 2013;52:283–91. https://doi.org/10.1016/j.renene.2012.10.015.
[3] Agarwal AK, Gupta JG, Dhar A. Potential and challenges for large-scale application of biodiesel in automotive sector. Prog Energy Combust Sci 2017;61:113–49. https://doi.org/10.1016/j.pecs.2017.03.002.
[4] Dey S, Deb M, Das PK. Chemical Characterization and Tribological Behavior of Kitchen Chimney Dump Lard (KCDL) as a Bio-lubricant. Revue Des Composites et Des Materiaux Avances 2019;29. 10.18280/rcma.290303.
[5] Aydin H, Ilkiliç C. Effect of ethanol blending with biodiesel on engine performance and exhaust emissions in a CI engine. Appl Therm Eng 2010;30:1199–204. https://doi.org/10.1016/j.applthermaleng.2010.01.037.
[7] Dey S, Reang NM, Das PK, Deb M. A comprehensive study on prospects of economy, environment, and efficiency of palm oil biodiesel as a renewable fuel. J Cleaner Prod 2020. https://doi.org/10.1016/j.jclepro.2020.124981.
[8] Guido C, Beatrice C, Napolitano P. Application of bioethanol / RME / diesel blend in a Euro5 automotive diesel engine: potentiality of closed loop combustion control technology. Appl Energy 2013;102:13–23. https://doi.org/10.1016/j.apenergy.2012.08.051.
[9] Gulum M, Bilgin A. An experimental optimization research of methyl and ethyl esters production from safflower oil. Environ Climate Technol 2018;22:132–48. https://doi.org/10.2478/rtuect-2018-0009.
[10] Mohiddin MN, Saleh AA, Reddy ANR, Hamdan S. A study on chicken fat as an alternative feedstock: Biodiesel production, fuel characterisation, and diesel engine performance analysis. Int J Automotive and Mech Eng 2018;15:5535–46.
10.15282/ijame.15.3.2018.10.0425.
[11] Ali OM, Mamat R, Abdullah NR, Abdullah AA. Analysis of blended fuel properties and engine performance with palm biodiesel-diesel blended fuel. Renewable Energy 2015;86:59–67. https://doi.org/10.1016/j.renene.2015.07.103.
[12] Zaharin MSM, Abdullah NR, Najafi G, Sharudin H, Yusaf T. Effects of physicochemical properties of biodiesel fuel blends with alcohol on diesel engine performance and exhaust emissions: a review. Renew Sustain Energy Rev 2017;79: 475–93. https://doi.org/10.1016/j.rser.2017.05.035.
[13] Di Y, Cheung CS, Huang Z. Comparison of the effect of biodiesel-diesel and ethanol- diesel on the gaseous emission of a direct-injection diesel engine. Atmos Environ 2009;43:2721–30. https://doi.org/10.1016/j.atmosenv.2009.02.050.
[14] Ren Y, Huang ZH, Jiang DM, Li W, Liu B, Wang XB. Effects of the addition of ethanol and cetane number improver on the combustion and emission characteristics of a compression ignition engine. Proc Inst Mech Eng, Part D: J Automobile Eng 2008;222:1077–87. https://doi.org/10.1243/09544070JAUTO516.
[15] EL-Seesy AI, Kayatas Z, Takayama R, He Z, Kandasamy S, Kosaka H. Combustion and emission characteristics of RCEM and common rail diesel engine working with diesel fuel and ethanol/hydrous ethanol injected in the intake and exhaust port: Assessment and comparison. Energy Conversion and Management 2020;205: 112453. 10.1016/j.enconman.2019.112453.
[16] Dey S, Reang NM, Deb M, Das PK. Study on performance-emission trade-off and multi-objective optimization of diesel-ethanol-palm biodiesel in a single cylinder CI engine: a Taguchi-fuzzy approach ARTICLE HISTORY. Energy Sources Part A 2020;1–21. https://doi.org/10.1080/15567036.2020.1767234.
[17] Yesilyurt MK, Aydin M. Experimental investigation on the performance, combustion and exhaust emission characteristics of a compression-ignition engine fueled with cottonseed oil biodiesel/diethyl ether/diesel fuel blends. Energy Convers Manage 2020;205:112355. https://doi.org/10.1016/j.enconman.2019.112355.
[18] Qi DH, Chen H, Geng LM, Bian YZ. Effect of diethyl ether and ethanol additives on the combustion and emission characteristics of biodiesel-diesel blended fuel engine. Renewable Energy 2011;36:1252–8. https://doi.org/10.1016/j.renene.2010.09.021
[19] Tosun E, Aydin K, Bilgili M. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alexandria Engineering Journal 2016;55:3081–9. https://doi.org/10.1016/j.aej.2016.08.011
[20] Atmanli A, Yüksel B, Ileri E, Deniz Karaoglan A. Response surface methodology based optimization of diesel-n-butanol -cotton oil ternary blend ratios to improve engine performance and exhaust emission characteristics. Energy Convers Manage
2015;90:383–94. https://doi.org/10.1016/j.enconman.2014.11.029
[21] Sakthivel R, Ramesh K, Joseph John Marshal S, Sadasivuni KK. Prediction of performance and emission characteristics of diesel engine fuelled with waste biomass pyrolysis oil using response surface methodology. Renewable Energy 2019;136:91–103. 10.1016/J.RENENE.2018.12.109.
[22] Krishnamoorthi M, Malayalamurthi R, Mohamed Shameer P. RSM based optimization of performance and emission characteristics of DI compression ignition engine fuelled with diesel/aegle marmelos oil/diethyl ether blends at varying compression ratio, injection pressure and injection timing. Fuel 2018;221: 283–97. https://doi.org/10.1016/j.fuel.2018.02.070
[23] Fang W, Kittelson DB, Northrop WF. Optimization of reactivity-controlled compression ignition combustion fueled with diesel and hydrous ethanol using response surface methodology. Fuel 2015;160:446–57. https://doi.org/10.1016/j.fuel.2015.07.055
[24] Hirkude JB, Padalkar AS. Performance optimization of CI engine fuelled with waste fried oil methyl ester-diesel blend using response surface methodology. Fuel 2014; 119:266–73. https://doi.org/10.1016/j.fuel.2013.11.039.
[25] Ileri E, Karaoglan AD, Atmanli A. Response surface methodology based prediction of engine performance and exhaust emissions of a diesel engine fuelled with canola oil methyl ester. J Renewable Sustainable Energy 2013;5:033132. https://doi.org/ 10.1063/1.4811801.
[26] Shivakumar, Srinivasa Pai P, Shrinivasa Rao BR. Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings. Applied Energy 2011;88:2344–54. 10.1016/j.apenergy.2010.12.030.
[27] Taghavifar HH, Taghavifar HH, Mardani A, Mohebbi A, Khalilarya S, Jafarmadar S. Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine. J Cleaner Prod 2016;112:1729–39. https://doi.org/10.1016/j.jclepro.2015.03.035
[28] Prasada Rao K, Victor Babu T, Anuradha G, Appa Rao BV. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN). Egypt J Pet 2017;26:593–600. https://doi.org/10.1016/j.ejpe.2016.08.006
[29] Javed S, Satyanarayana Murthy YV V, Baig RU, Prasada Rao D. Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends. J Natural Gas Sci Eng 2015;26:549–57. doi: 10.1016/j.jngse.2015.06.041.
[30] Uslu S, Celik MB. Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether. Eng Sci Technol Int J 2018;21:1194–201. https://doi.org/10.1016/j.jestch.2018.08.017
[31] Aryasomayajula Venkata Satya Lakshmi SB, Subramania Pillai N, Khadhar Mohamed MSB, Narayanan A. Biodiesel production from rubber seed oil using calcined eggshells impregnated with Al2O3 as heterogeneous catalyst: a comparative study of RSM and ANN optimization. Brazilian J Chem Eng 2020;37: 351–68. https://doi.org/10.1007/s43153-020-00027-9
[32] Hariharan N, Senthil V, Krishnamoorthi M, Karthic SV. Application of artificial neural network and response surface methodology for predicting and optimizing dual-fuel CI engine characteristics using hydrogen and bio fuel with water injection. Fuel 2020;270:117576. https://doi.org/10.1016/j.fuel.2020.117576
[33] Hammoudi A, Moussaceb K, Belebchouche C, Dahmoune F. Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Constr Build Mater 2019;209:425–36. https://doi.org/10.1016/j.conbuildmat.2019.03.119
[34] Samuel OD, Okwu MO. Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production. Energy Sources Part A 2019;41:1049–61. https://doi.org/10.1080/15567036.2018.1539138
[35] Astray G, Gullon ´ B, Labidi J, Gullon ´ P. Comparison between developed models using response surface methodology (RSM) and artificial neural networks (ANNs) with the purpose to optimize oligosaccharide mixtures production from sugar beet pulp. Ind Crops Prod 2016;92:290–9. https://doi.org/10.1016/j.indcrop.2016.08.011
[36] Ayoola AA, Hymore FK, Omonhinmin CA, Babalola PO, Fayomi OSI, Olawole OC, et al. Response surface methodology and artificial neural network analysis of crude palm kernel oil biodiesel production. Chem Data Collect 2020;28:100478. https://doi.org/10.1016/j.cdc.2020.100478
[37] Chouaibi M, Daoued K Ben, Riguane K, Rouissi T, Ferrari G. Production of bioethanol from pumpkin peel wastes: comparison between response surface methodology (RSM) and artificial neural networks (ANN). Industrial Crops and Products 2020;155:112822. 10.1016/j.indcrop.2020.112822.
[38] Uslu S. Optimization of diesel engine operating parameters fueled with palm oil- diesel blend: Comparative evaluation between response surface methodology (RSM) and artificial neural network (ANN). Fuel 2020;276:117990. https://doi.org/10.1016/j.fuel.2020.117990
[39]. Dey, Suman, Narath Moni Reang, Pankaj Kumar Das, and Madhujit Deb. "Comparative study using RSM and ANN modelling for performance-emission prediction of CI engine fuelled with bio-diesohol blends: A fuzzy optimization approach." Fuel 292 (2021): 120356. https://doi.org/10.1016/j.fuel.2021.120356
[40]. Zheng, M., Wang, F., Hu, X., Miao, Y., Cao, H., & Tang, M. (2022). A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models. Axioms, 11(11). https://doi.org/10.3390/axioms11110607
[41]. Wang, Z., Zhang, X., Zhang, Z. K., & Sheng, D. (2022). Credit portfolio optimization: A multi-objective genetic algorithm approach. Borsa Istanbul Review, 22(1). https://doi.org/10.1016/j.bir.2021.01.004
[42]. Giordano, P. C., Pereyra, V., Beccaria, A. J., Vero, S., & Goicoechea, H. C. (2020). Application of pareto-optimal front as an option to desirability function for the optimization of a microbiological process. Microchemical Journal, 155. https://doi.org/10.1016/j.microc.2020.104682
[43]. Yu, W., Li, B., Jia, H., Zhang, M., & Wang, D. (2015). Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, 88. https://doi.org/10.1016/j.enbuild.2014.11.063
Published
2024-07-30
How to Cite
[1]
LAHIRI, S.K., DAS, P., BISWAS, A., SARDAR, S., MODAK, B., AHMED, F. and PAL, A. 2024. Machine Learning-Guided Optimization of Biofuel Blends for Enhanced Engine Efficiency and Emission Reduction. IIChE-CHEMCON. (Jul. 2024). DOI:https://doi.org/10.36375/prepare_u.iiche.a408.
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