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<p>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&#xa0;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&#xa0;g/kW-hr, and minimal emissions of 1.5% CO, 108.13 ppm HC, and 1211.8 ppm NO<sub>x</sub>, with an overall desirability of 0.820, indicating a highly favorable combination of performance and emissions characteristics.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月7日

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  • Farooq Shaik,
  • D. Vinay Kumar,
  • N. Channa Keshava Naik,
  • G. Radha Krishna,
  • T. M. Yunus Khan,
  • Abdul Saddique Shaik,
  • Abdulrajak Buradi,
  • Addisu Frinjo Emma

摘要

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.