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

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.

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Publication Details

Journal
Scientific Reports
Published
2025-09-29
DOI
https://doi.org/10.1038/s41598-025-15700-7
Primary Topic
Advanced Combustion Engine Technologies
Type
article
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article

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

M. Javed Idrisi, Mohd Zaheen Khan, Zeinebou Yahya, Haidar Howari et al.
Scientific Reports
Advanced Combustion Engine Technologies
article

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

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

Abstract

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.

Scientific ReportsVol. 15(1)
Dr. A.P.J. Abdul Kalam Technical University (IN), Qassim University (SA), Majmaah University (SA), Mizan Tepi University (ET), Manav Rachna International Institute of Research and Studies (IN)
Openalex Percentile: Top 77%
Advanced Combustion Engine Technologies
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