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<p>The research aims to investigate the impact of GO and ZnO nanoparticles in a 20% mahua biodiesel blend (B20) on direct injection diesel engine performance. Mahua oil, selected for its non-edible source and local availability, provides a sustainable fuel option. To improve combustion and reduce emissions, nanoparticles are added. Artificial neural networks (ANNs) and response surface methodology (RSM) are then used to predict and optimize engine operating parameters, leveraging their ability to model intricate relationships and analyze experimental data, ultimately aiming to create a more efficient and environmentally friendly fuel system. At a concentration of 75&#xa0;ppm, GO and ZnO nanoparticles were taken into consideration. Additionally, a dispersant (TWEEN 80) and surfactant (CTAB) were mixed respectively at a ratio of 1:1. Using a spectrophotometer, stability analysis was carried out on different nanofuel samples, and a study based on experiments was done on a diesel engine. The output factors examined are BSFC, BTE, NHRR, CP, UHC, CO, NO<sub>x</sub>, and smoke Opacity. These metrics were based on performance, combustion, and emission characteristics. Input parameters such as fuel samples, injection pressure, and engine load were considered. The injection pressure varied at 200, 225, and 250&#xa0;bar, whereas the load was considered to be 5%, 50%, 75%, and 100%, respectively. When compared to other fuel samples, dispersant included GO and ZnO nanoparticles in B20 shown optimal results. The B20 + GO 75&#xa0;ppm + TWEEN 80 75&#xa0;ppm combination has shown a 5.293% decrease in BSFC and 5.067% improvement in BTE at 250 bars. Furthermore, significant improvements were observed in key combustion parameters, CP increased by 3.13%, and NHRR increased by a substantial 43.50%. CO, UHC, NO<sub>x</sub>, and smoke opacity were all reduced by around 11.07%, 37.63%, 27.77%, and 38.55% respectively. With <i>R</i><sup>2</sup> values consistently between 0.93 and 0.99, the ANN and RSM predictions demonstrate a perfect fit to the data, confirming their high accuracy and reliability.</p>

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Optimizing performance, combustion and emission characteristics of mahua biodiesel included GO and ZnO nanoparticles: an ANN-RSM approach

  • 期刊论文

  • 发表日期：

    2025年5月21日

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  • Srinivasa Reddy Pala,
  • Mangu Venkata Krishna Mohan,
  • Varaha Siva Prasad Vanthala

摘要

The research aims to investigate the impact of GO and ZnO nanoparticles in a 20% mahua biodiesel blend (B20) on direct injection diesel engine performance. Mahua oil, selected for its non-edible source and local availability, provides a sustainable fuel option. To improve combustion and reduce emissions, nanoparticles are added. Artificial neural networks (ANNs) and response surface methodology (RSM) are then used to predict and optimize engine operating parameters, leveraging their ability to model intricate relationships and analyze experimental data, ultimately aiming to create a more efficient and environmentally friendly fuel system. At a concentration of 75 ppm, GO and ZnO nanoparticles were taken into consideration. Additionally, a dispersant (TWEEN 80) and surfactant (CTAB) were mixed respectively at a ratio of 1:1. Using a spectrophotometer, stability analysis was carried out on different nanofuel samples, and a study based on experiments was done on a diesel engine. The output factors examined are BSFC, BTE, NHRR, CP, UHC, CO, NOx, and smoke Opacity. These metrics were based on performance, combustion, and emission characteristics. Input parameters such as fuel samples, injection pressure, and engine load were considered. The injection pressure varied at 200, 225, and 250 bar, whereas the load was considered to be 5%, 50%, 75%, and 100%, respectively. When compared to other fuel samples, dispersant included GO and ZnO nanoparticles in B20 shown optimal results. The B20 + GO 75 ppm + TWEEN 80 75 ppm combination has shown a 5.293% decrease in BSFC and 5.067% improvement in BTE at 250 bars. Furthermore, significant improvements were observed in key combustion parameters, CP increased by 3.13%, and NHRR increased by a substantial 43.50%. CO, UHC, NOx, and smoke opacity were all reduced by around 11.07%, 37.63%, 27.77%, and 38.55% respectively. With R2 values consistently between 0.93 and 0.99, the ANN and RSM predictions demonstrate a perfect fit to the data, confirming their high accuracy and reliability.