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Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine

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  • Rajkumar, Sundararajan
  • Das, Arnab
  • Thangaraja, Jeyaseelan

Abstract

Biodiesel usage is practically restricted as a blended supplement with fossil diesel. In the current study, the authors have attempted to arrive at the optimal biodiesel blend concentrations for an automotive engine. Here, the artificial neural network and genetic algorithm are coupled with phenomenological combustion modelling for the efficient operation of biodiesel blends. The engine experiments are conducted with diesel and diesel-biodiesel blends namely jatropha, and karanja consisting of 120 data points each. This set of data are used for the ANN development and validation. A multi-layer perceptron network is trained by the experimental data for predicting the engine parameters. The Nash Sutcliffe coefficient of efficiency values for the ANN predicted parameters are close to 1, indicating a close prediction. The ANN model predicted the engine output parameters with low values of mean square error, mean square relative error, mean absolute percentage error and standard error of prediction. Optimum values of biodiesel blend fraction, engine speed, brake mean effective pressure, injection pressure and timing are obtained using a multi-objective genetic algorithm. The optimised blend concentration is found to be ∼20% and ∼40% for satisfying the different objectives concerning the overall engine characteristics. Finally, the outputs for the optimised parameters are compared to the validated multi-zone model predictions within the maximum error of ∼3% and 7.9% for performance and emission parameters respectively.

Suggested Citation

  • Rajkumar, Sundararajan & Das, Arnab & Thangaraja, Jeyaseelan, 2022. "Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s036054422102137x
    DOI: 10.1016/j.energy.2021.121889
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    1. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    2. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    3. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    4. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    5. Jaliliantabar, Farzad & Ghobadian, Barat & Najafi, Gholamhassan & Mamat, Rizalman & Carlucci, Antonio Paolo, 2019. "Multi-objective NSGA-II optimization of a compression ignition engine parameters using biodiesel fuel and exhaust gas recirculation," Energy, Elsevier, vol. 187(C).
    6. Motlagh, Tara Yazdani & Azadani, Leila N. & Yazdani, Kaveh, 2020. "Multi-objective optimization of diesel injection parameters in a natural gas/diesel reactivity controlled compression ignition engine," Applied Energy, Elsevier, vol. 279(C).
    7. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(C).
    8. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    9. Channapattana, S.V. & Pawar, Abhay A. & Kamble, Prashant G., 2017. "Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model," Applied Energy, Elsevier, vol. 187(C), pages 84-95.
    10. Silitonga, A.S. & Masjuki, H.H. & Ong, Hwai Chyuan & Sebayang, A.H. & Dharma, S. & Kusumo, F. & Siswantoro, J. & Milano, Jassinnee & Daud, Khairil & Mahlia, T.M.I. & Chen, Wei-Hsin & Sugiyanto, Bamban, 2018. "Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine," Energy, Elsevier, vol. 159(C), pages 1075-1087.
    11. Thangaraja, J. & Anand, K. & Mehta, Pramod S., 2016. "Biodiesel NOx penalty and control measures - a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 1-24.
    12. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "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, Elsevier, vol. 88(7), pages 2344-2354, July.
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    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.
    2. Jeyaseelan, Thangaraja & El Samad, Tala & Rajkumar, Sundararajan & Chatterjee, Abhay & Al-Zaili, Jafar, 2023. "A techno-economic assessment of waste oil biodiesel blends for automotive applications in urban areas: Case of India," Energy, Elsevier, vol. 271(C).
    3. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).
    4. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).
    5. Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    6. Sujin, P. & Selva Roji, S. Sheeju & Kings, Ajith J. & Miriam, L.R. Monisha, 2024. "Optimization of engine performance, emission and combustion parameters by using mixed nonedible oil biodiesel with nano-additives using hybrid techniques," Energy, Elsevier, vol. 305(C).

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