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

Author

Listed:
  • 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
    Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China
    Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India
    Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham Malaysia, Semenyih 43500, Malaysia)

  • 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)

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.

Suggested Citation

  • Babu Dharmalingam & Santhoshkumar Annamalai & Sukunya Areeya & Kittipong Rattanaporn & Keerthi Katam & Pau-Loke Show & Malinee Sriariyanun, 2023. "Bayesian Regularization Neural Network-Based Machine Learning Approach on Optimization of CRDI-Split Injection with Waste Cooking Oil Biodiesel to Improve Diesel Engine Performance," Energies, MDPI, vol. 16(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2805-:d:1100462
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    References listed on IDEAS

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    1. Thangarasu, Vinoth & M, Angkayarkan Vinayakaselvi & Ramanathan, Anand, 2021. "Artificial neural network approach for parametric investigation of biodiesel synthesis using biocatalyst and engine characteristics of diesel engine fuelled with Aegle Marmelos Correa biodiesel," Energy, Elsevier, vol. 230(C).
    2. Jain, Ayush & Singh, Akhilendra Pratap & Agarwal, Avinash Kumar, 2017. "Effect of split fuel injection and EGR on NOx and PM emission reduction in a low temperature combustion (LTC) mode diesel engine," Energy, Elsevier, vol. 122(C), pages 249-264.
    3. Babu, D. & Karvembu, R. & Anand, R., 2018. "Impact of split injection strategy on combustion, performance and emissions characteristics of biodiesel fuelled common rail direct injection assisted diesel engine," Energy, Elsevier, vol. 165(PB), pages 577-592.
    4. Eldiehy, Khalifa S.H. & Gohain, Minakshi & Daimary, Niran & Borah, Doljit & Mandal, Manabendra & Deka, Dhanapati, 2022. "Radish (Raphanus sativus L.) leaves: A novel source for a highly efficient heterogeneous base catalyst for biodiesel production using waste soybean cooking oil and Scenedesmus obliquus oil," Renewable Energy, Elsevier, vol. 191(C), pages 888-901.
    5. Yusuff, Adeyinka S. & Bhonsle, Aman K. & Bangwal, Dinesh P. & Atray, Neeraj, 2021. "Development of a barium-modified zeolite catalyst for biodiesel production from waste frying oil: Process optimization by design of experiment," Renewable Energy, Elsevier, vol. 177(C), pages 1253-1264.
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    Cited by:

    1. Wenbo Ai & Haeng Muk Cho, 2024. "Predictive Models for Biodiesel Performance and Emission Characteristics in Diesel Engines: A Review," Energies, MDPI, vol. 17(19), pages 1-25, September.

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