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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Veröffentlicht in:Environment, development and sustainability Jg. 27; H. 9; S. 22877 - 22903
Hauptverfasser: Tiwari, Chandrabhushan, Dwivedi, Gaurav, Verma, Tikendra Nath
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.09.2025
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1573-2975
1387-585X
1573-2975
DOI:10.1007/s10668-024-05362-2