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<p>The world is currently grappling with a severe fuel crisis, driving the urgent search for sustainable and renewable alternatives. Biodiesel stands out as a viable green option due to its biodegradability and lower emissions. However, its global adoption remains limited, primarily due to high conversion costs and low yield. This study investigates the use of sulfonated graphene (SGR) as a catalyst to enhance biodiesel production efficiency. A Petter-AV1 single-cylinder diesel engine was used to evaluate performance (BTE, BSFC) and emissions (NOx, UBHC) through 14 experimental trials, with total uncertainty below 5%, confirming test reliability. Given the complex interactions among these parameters, stemming from nonlinear combustion behavior and physicochemical dependencies, a hybrid optimization method is applied, integrating Pearson-based priority analysis with k-means machine learning clustering. AHP–k-means is specifically selected due to its strength in addressing the multi-dimensional complexity of biodiesel properties. Its precision in prioritizing influencing factors and clustering performance-emission outcomes makes it ideal for optimizing biodiesel blends in diesel engine setup. Sulfonated graphene effectively enhances the transesterification process, achieving a high biodiesel yield of 94%. Nanoparticle concentration had the most significant effect, showing strong positive correlation with BTE (r = 0.6247) and strong negative correlation with BSFC (r = − 0.5802) and UBHC (r = − 0.6634), though it increased NOx (r = 0.6168). Among the input parameters, nanoparticle concentration held the highest priority (48%), followed by blend percentage (27%). The optimal trial (Trial 13) featured 40% biodiesel blend, 20&#xa0;ppm NPC, 100% load, and a toroidal piston head, resulting in BTE of 42.30%, BSFC of 0.34&#xa0;kJ/kWh, NOx at 620.18&#xa0;ppm, and UBHC at 39.60&#xa0;ppm. These findings highlight the promising role of SGR in improving biodiesel yield and its potential application in converting wastewater treatment plants into sustainable fuels.</p>

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An experimental approach varying piston geometry for optimization of diesel engine performance and emissions using prioritized clustering approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Saad Alshammari,
  • Mohd Zaheen Khan,
  • Zeinebou Yahya,
  • Aiyeshah Alhodaib,
  • Haidar Howari,
  • M. Javed Idrisi,
  • Worku Tenna

摘要

The world is currently grappling with a severe fuel crisis, driving the urgent search for sustainable and renewable alternatives. Biodiesel stands out as a viable green option due to its biodegradability and lower emissions. However, its global adoption remains limited, primarily due to high conversion costs and low yield. This study investigates the use of sulfonated graphene (SGR) as a catalyst to enhance biodiesel production efficiency. A Petter-AV1 single-cylinder diesel engine was used to evaluate performance (BTE, BSFC) and emissions (NOx, UBHC) through 14 experimental trials, with total uncertainty below 5%, confirming test reliability. Given the complex interactions among these parameters, stemming from nonlinear combustion behavior and physicochemical dependencies, a hybrid optimization method is applied, integrating Pearson-based priority analysis with k-means machine learning clustering. AHP–k-means is specifically selected due to its strength in addressing the multi-dimensional complexity of biodiesel properties. Its precision in prioritizing influencing factors and clustering performance-emission outcomes makes it ideal for optimizing biodiesel blends in diesel engine setup. Sulfonated graphene effectively enhances the transesterification process, achieving a high biodiesel yield of 94%. Nanoparticle concentration had the most significant effect, showing strong positive correlation with BTE (r = 0.6247) and strong negative correlation with BSFC (r = − 0.5802) and UBHC (r = − 0.6634), though it increased NOx (r = 0.6168). Among the input parameters, nanoparticle concentration held the highest priority (48%), followed by blend percentage (27%). The optimal trial (Trial 13) featured 40% biodiesel blend, 20 ppm NPC, 100% load, and a toroidal piston head, resulting in BTE of 42.30%, BSFC of 0.34 kJ/kWh, NOx at 620.18 ppm, and UBHC at 39.60 ppm. These findings highlight the promising role of SGR in improving biodiesel yield and its potential application in converting wastewater treatment plants into sustainable fuels.