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Prediction OF CI engine performance, emission and combustion parameters using fish oil as a biodiesel by fuzzy-GA

Author

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  • Sakthivel, G.
  • Sivaraja, C.M.
  • Ikua, Bernard W.

Abstract

This paper describes the application of Fuzzy Logic, Genetic Algorithm (GA) and Fuzzy Analytic Hierarchical Process-Technique for Order Preference by Similarity to Ideal Solution (FAHP-TOPSIS) for the selection of optimum blend from the various alternative blends of fish oil and diesel. A single cylinder, constant speed and direct injection diesel engine with a rated output of 4.4 kW is used for exploratory analysis at different load conditions. Ten parameterssuch as maximization of brake thermal efficiency, rate of pressure rise and minimization of NOx,CO2, CO, HC, smoke, exhaust gas temperature, ignition delay and combustion delay are considered. From the experimental data, multi input multi output (MIMO) fuzzy models are developed using triangular membership functionto predict the engine parameters. The developed model has good correlation with the observed data with noticeably low errors. Then, for each parameter, optimum blend has to be identified for all the loads using genetic algorithm which is a multi-objective optimization process. The engine performance is accurately predicted for the identified best load and blend from the developed fuzzy model. Hence in the proposed model, fuzzy logic is integrated with GAto find the best blend. Finally, from the best blend of each parameter, the exact biodiesel proportion of about 17% for no load, 17% for 25% load, 18% for 50% load, 17% for 75% load and 20% for 100% load are found out usingTOPSIS which will best suit the engine performance and reduction of emissions.

Suggested Citation

  • Sakthivel, G. & Sivaraja, C.M. & Ikua, Bernard W., 2019. "Prediction OF CI engine performance, emission and combustion parameters using fish oil as a biodiesel by fuzzy-GA," Energy, Elsevier, vol. 166(C), pages 287-306.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:287-306
    DOI: 10.1016/j.energy.2018.10.023
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    3. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
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