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<p>The rising global energy demand strongly associates over global warming, and prompt environmental changes have determined researchers to investigate and identify alternative fuels as sustainable and eco-friendly solutions. The aim of the current study is to investigate the performance, and emission characteristics of an engine using water diesel emulsified fuel (WDEF) adopting SiO<sub>2</sub> nanoparticle. The fuel blend D94W5S1-Si50, comprising of 94% diesel, 5% water, 1% SPAN 80, and 50 ppm silicon nanoparticles (SiNPs) is selected for test fuel and explored the simultaneous effects of different input parameters such as injection pressure, injection timing, and engine load on engine performance and emission attributes without engine modification or experiencing added costs. The novelty of this work lies in the integrated use of Taguchi L9 design, ANFIS modeling, and MOPDO algorithm to optimize diesel engine performance and emissions using a SiO<sub>2</sub>-based WDEF blend. This combined experimental–numerical approach for multi-parameter analysis and optimization has not been previously reported, offering a comprehensive and intelligent strategy for engine-fuel interaction studies. The Taguchi design approach L9 array was applied and subsequently an adaptive neuro fuzzy inference system (ANFIS) model has been established for studying and prediction of WDEF engine’s performance and emission characteristics. Moreover, the current study employs soft computing approaches, ANFIS and multi-objective prairie dog optimization algorithm (MOPDO) for investigating and optimizing the performance and emission attributes for corresponding input variables of diesel engine. The results showed that maximum Brake thermal efficiency (BTE), minimum NOx and smoke are found to be at 50% load, 200&#xa0;bar injection pressure &amp; 21<sup>0</sup>bTDC, 25% load, 180&#xa0;bar injection pressure &amp; 23<sup>0</sup>bTDC, and 25% load, 180&#xa0;bar injection pressure &amp; 23<sup>0</sup>bTDC respectively. This result is also confirmed by the developed ANFIS model.</p>

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Optimization of SiO2 based water–diesel emulsified fuel for engine performance and emission characteristics using soft computing approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月28日

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  • Rahul Goyal,
  • Vimal Kumar Pathak,
  • Chandrakant Sonawane

摘要

The rising global energy demand strongly associates over global warming, and prompt environmental changes have determined researchers to investigate and identify alternative fuels as sustainable and eco-friendly solutions. The aim of the current study is to investigate the performance, and emission characteristics of an engine using water diesel emulsified fuel (WDEF) adopting SiO2 nanoparticle. The fuel blend D94W5S1-Si50, comprising of 94% diesel, 5% water, 1% SPAN 80, and 50 ppm silicon nanoparticles (SiNPs) is selected for test fuel and explored the simultaneous effects of different input parameters such as injection pressure, injection timing, and engine load on engine performance and emission attributes without engine modification or experiencing added costs. The novelty of this work lies in the integrated use of Taguchi L9 design, ANFIS modeling, and MOPDO algorithm to optimize diesel engine performance and emissions using a SiO2-based WDEF blend. This combined experimental–numerical approach for multi-parameter analysis and optimization has not been previously reported, offering a comprehensive and intelligent strategy for engine-fuel interaction studies. The Taguchi design approach L9 array was applied and subsequently an adaptive neuro fuzzy inference system (ANFIS) model has been established for studying and prediction of WDEF engine’s performance and emission characteristics. Moreover, the current study employs soft computing approaches, ANFIS and multi-objective prairie dog optimization algorithm (MOPDO) for investigating and optimizing the performance and emission attributes for corresponding input variables of diesel engine. The results showed that maximum Brake thermal efficiency (BTE), minimum NOx and smoke are found to be at 50% load, 200 bar injection pressure & 210bTDC, 25% load, 180 bar injection pressure & 230bTDC, and 25% load, 180 bar injection pressure & 230bTDC respectively. This result is also confirmed by the developed ANFIS model.