Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM

Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM

Abstract The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. Furthermore,...

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Bibliographic Details
Main Authors: Farooq Shaik, D. Vinay Kumar, N. Channa Keshava Naik, G. Radha Krishna, T. M. Yunus Khan, Abdul Saddique Shaik, Abdulrajak Buradi, Addisu Frinjo Emma
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Performance
Emissions
Equivalent GEM blends
ANN
RSM
Prediction
Online Access:https://doi.org/10.1038/s41598-025-88486-3
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Summary:Abstract The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. Furthermore, Response Surface Methodology (RSM) was utilized to optimize the engine’s performance, identifying the optimal operating conditions of 2992.9 rpm engine speed and an E20-equivalent GEM blend. Under these conditions, the engine exhibited a brake thermal efficiency (B_The) of 34.63%, a brake specific fuel consumption (BSFC) of 243.7 g/kW-hr, and minimal emissions of 1.5% CO, 108.13 ppm HC, and 1211.8 ppm NOx, with an overall desirability of 0.820, indicating a highly favorable combination of performance and emissions characteristics.
ISSN:2045-2322

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