Exploring the performance and emission characteristics of a dual fuel CI engine using microalgae biodiesel and diesel blend: a machine learning approach using ANN and response surface methodology

Alternative fuels in internal combustion engines have gained significant attention to environmental sustainability and energy security. The study employs a machine-learning (ML) approach, integrating artificial neural networks (ANN) and response surface method (RSM), to analyze the engine characteri...

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Vydáno v:	Environment, development and sustainability Ročník 27; číslo 9; s. 22877 - 22903
Hlavní autoři:	Tiwari, Chandrabhushan, Dwivedi, Gaurav, Verma, Tikendra Nath
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.09.2025 Springer Nature B.V
Témata:	Alternative energy Alternative fuels Artificial intelligence Artificial neural networks Biodiesel fuels Biofuels Carbon dioxide Compression Compression ratio Current Progress on Advanced Technologies for Biofuel Production and Utilisation Diesel Diesel fuels Earth and Environmental Science Ecology Economic Geology Economic Growth Energy security Environment Environmental Economics Environmental Management Experimental data Internal combustion engines Investigations Learning algorithms Machine learning Neural networks Optimization Parameters Regression coefficients Response surface methodology Responses Sustainable Development Emission ANN Blend Biodiesel RSM Optimization
ISSN:	1573-2975, 1387-585X, 1573-2975
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Shrnutí:	Alternative fuels in internal combustion engines have gained significant attention to environmental sustainability and energy security. The study employs a machine-learning (ML) approach, integrating artificial neural networks (ANN) and response surface method (RSM), to analyze the engine characteristics. The experimental data used to train the ANN and RSM model was obtained by employing different combinations of input parameters obtained by the Design of the experiment tool. Four input parameters load 25–100% ((1.3, 2.6, 3.9, and 5.2 kW) loading condition, speed (1200, 1500, and 1800 RPM), compression ratio (17.5 and 18.5), and biodiesel–diesel blends (Diesel, SM 20 , SM 40 , SM 60 , SM 80 and SM 100 ) were used. The results show predictability for ANN with training and test regression coefficients (R 2 ) of 0.975 and 0.948 whereas RSM with R 2 of 0.992. Optimized results for RSM and ANN, BTE (29.4% and 29.1%), BSFC (0.0.3201 and 0.334 kg/kWh), IMEP (2.83 and 2.69 bar), and CO 2 (922.72 and 940.87 g/kwh), NOx (964 and 937 ppm). When compared with experimental data, the error was about 5%. It can be inferred that RSM and ANN may be used to model processes with high predictability and that optimization can be carried out using various techniques depending on the process or problem.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1573-2975 1387-585X 1573-2975
DOI:	10.1007/s10668-024-05362-2