Bayesian Regularization Neural Network-Based Machine Learning Approach on Optimization of CRDI-Split Injection with Waste Cooking Oil Biodiesel to Improve Diesel Engine Performance

Babu Dharmalingam, Santhoshkumar Annamalai, Sukunya Areeya, Kittipong Rattanaporn, Keerthi Katam, Pau-Loke Show and Malinee Sriariyanun ()
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Babu Dharmalingam: Biorefinery and Process Automation Engineering Center, Department of Chemical and Process Engineering, TGGS, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Santhoshkumar Annamalai: Mechanical Engineering, Kongu Engineering College, Perundurai 638060, India
Sukunya Areeya: Biorefinery and Process Automation Engineering Center, Department of Chemical and Process Engineering, TGGS, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Kittipong Rattanaporn: Department of Biotechnology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand
Keerthi Katam: Department of Civil Engineering, Ecole Centrale School of Engineering, Mahindra University, Telangana 500043, India
Pau-Loke Show: Department of Chemical Engineering, Khalifa University, Shakhbout Bin Sultan St. Zone 1, Abu Dhabi P.O. Box. 127788, United Arab Emirates
Malinee Sriariyanun: Biorefinery and Process Automation Engineering Center, Department of Chemical and Process Engineering, TGGS, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand

Energies, 2023, vol. 16, issue 6, 1-19

Abstract: The present study utilized response surface methodology (RSM) and Bayesian neural network (BNN) to predict the characteristics of a diesel engine powered by a blend of biodiesel and diesel fuel. The biodiesel was produced from waste cooking oil using a biocatalyst synthesized from vegetable waste through the wet impregnation technique. A multilevel central composite design was utilized to predict engine characteristics, including brake thermal efficiency (BTE), nitric oxide (NO), unburned hydrocarbons (UBHC), smoke emissions, heat release rate (HRR), and cylinder peak pressure (CGPP). BNN and the logistic–sigmoid activation function were used to train the experimental data in the artificial neural network (ANN) model, and the errors and correlations of the predicted models were calculated. The study revealed that the biocatalyst was capable of producing a maximum yield of 93% at 55 °C under specific reaction conditions, namely a reaction time of 120 min, a stirrer speed of 900 rpm, a catalyst loading of 7 wt.%, and a molar ratio of 1:9. Further, the ANN model was found to exhibit comparably lower prediction errors (0.001–0.0024), lower MAPE errors (3.14–4.6%), and a strong correlation (0.984–0.998) compared to the RSM model. B100-80%-20% was discovered to be the best formulation for emission property, while B100-90%-10% was the best mix for engine performance and combustion at 100% load. In conclusion, this study found that utilizing the synthesized biocatalyst led to attaining a maximum biodiesel yield. Furthermore, the study recommends using ANN and RSM techniques for accurately predicting the characteristics of a diesel engine.

Keywords: central composite design; Bayesian regularization neural network; split injection strategy; mixed waste cooking oil methyl ester; common rail direct injection diesel engine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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