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<p>Adopting sustainable and cleaner fuels has become imperative in light of the rapid depletion of petroleum reserves and escalating vehicular emissions. Biodiesel produced from agricultural and food-waste oils presents a promising alternative; however, its application in contemporary CRDI diesel engines is hindered by intrinsic drawbacks such as elevated viscosity, diminished volatility, and lower energy density. This study explores the synergistic potential of silicon dioxide (SiO₂) nanoparticles as energy catalytic additives to enhance the performance, combustion behavior, and emission profile of an Agri &amp; Food-Waste Mixed Biodiesel (MME20) blend. Nanofuel formulations MME 20, MME20+SIO40, MME20+SIO80, and MME20+SIO120 were developed using CTAB-assisted ultrasonication for superior stability and uniformity. Experimental assessments were conducted on a single-cylinder CRDI diesel engine (3.7 kW, 1500 rpm, 18:1 compression ratio). The addition of SiO₂ nanoparticles led to a reduction in brake specific fuel consumption (BSFC) from 0.355 to 0.31 kg/kWh and an improvement in brake thermal efficiency (BTE) from 24.2% to 25.8% compared to neat biodiesel. Combustion analysis indicated elevated peak in-cylinder pressure (72 bar) and heat release rate (63 J/°CA), which are attributed to enhanced fuel atomization, micro-explosion phenomena, and catalytic oxidation mechanisms induced by SiO₂. Emission studies revealed marked decreases in hydrocarbon (20%), carbon monoxide (~18%), and smoke opacity (~25%), with a slight increase in nitrogen oxides (10%) likely resulting from higher in-cylinder flame temperatures. The findings underscore the effectiveness of SiO₂ nanoparticles in ameliorating the limitations of biodiesel, enabling improved engine performance, superior combustion, and cleaner exhaust emissions in CRDI diesel engines. Alongside the experimental evaluation, a comprehensive Artificial Neural Network (ANN) model was developed using MATLAB to predict engine performance, combustion, and emission parameters. The ANN was configured as a feedforward backpropagation network with input neurons representing engine operational variables and SiO₂ nanoparticle concentrations. The model was trained and validated using experimental data from varying nanoparticle blend levels (0, 40, 80, and 120 ppm) under different engine loads. The ANN predicted key outputs including brake thermal efficiency, brake-specific fuel consumption, hydrocarbon emissions, carbon monoxide emissions, nitric oxide emissions, and smoke opacity with high accuracy, achieving regression coefficients (R2R2) exceeding 0.98 and low mean squared error values. This predictive modeling complements the experimental results by enabling rapid parameter estimation and optimization without exhaustive engine testing, demonstrating the ANN's potential as an effective tool in biodiesel fuel engine studies.</p>

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Effect of Metal-Based SiO2 Nanoparticles Blended Concentration on Performance, Combustion and Emission Characteristics of CRDI Diesel Engine Running on Agri & Food Waste Biodiesel and ANN Prediction Using MATLAB

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Deepankumar S,
  • Barun Haldar,
  • Vishal Shukla,
  • Sivapragasam Alagesan

摘要

Adopting sustainable and cleaner fuels has become imperative in light of the rapid depletion of petroleum reserves and escalating vehicular emissions. Biodiesel produced from agricultural and food-waste oils presents a promising alternative; however, its application in contemporary CRDI diesel engines is hindered by intrinsic drawbacks such as elevated viscosity, diminished volatility, and lower energy density. This study explores the synergistic potential of silicon dioxide (SiO₂) nanoparticles as energy catalytic additives to enhance the performance, combustion behavior, and emission profile of an Agri & Food-Waste Mixed Biodiesel (MME20) blend. Nanofuel formulations MME 20, MME20+SIO40, MME20+SIO80, and MME20+SIO120 were developed using CTAB-assisted ultrasonication for superior stability and uniformity. Experimental assessments were conducted on a single-cylinder CRDI diesel engine (3.7 kW, 1500 rpm, 18:1 compression ratio). The addition of SiO₂ nanoparticles led to a reduction in brake specific fuel consumption (BSFC) from 0.355 to 0.31 kg/kWh and an improvement in brake thermal efficiency (BTE) from 24.2% to 25.8% compared to neat biodiesel. Combustion analysis indicated elevated peak in-cylinder pressure (72 bar) and heat release rate (63 J/°CA), which are attributed to enhanced fuel atomization, micro-explosion phenomena, and catalytic oxidation mechanisms induced by SiO₂. Emission studies revealed marked decreases in hydrocarbon (20%), carbon monoxide (~18%), and smoke opacity (~25%), with a slight increase in nitrogen oxides (10%) likely resulting from higher in-cylinder flame temperatures. The findings underscore the effectiveness of SiO₂ nanoparticles in ameliorating the limitations of biodiesel, enabling improved engine performance, superior combustion, and cleaner exhaust emissions in CRDI diesel engines. Alongside the experimental evaluation, a comprehensive Artificial Neural Network (ANN) model was developed using MATLAB to predict engine performance, combustion, and emission parameters. The ANN was configured as a feedforward backpropagation network with input neurons representing engine operational variables and SiO₂ nanoparticle concentrations. The model was trained and validated using experimental data from varying nanoparticle blend levels (0, 40, 80, and 120 ppm) under different engine loads. The ANN predicted key outputs including brake thermal efficiency, brake-specific fuel consumption, hydrocarbon emissions, carbon monoxide emissions, nitric oxide emissions, and smoke opacity with high accuracy, achieving regression coefficients (R2R2) exceeding 0.98 and low mean squared error values. This predictive modeling complements the experimental results by enabling rapid parameter estimation and optimization without exhaustive engine testing, demonstrating the ANN's potential as an effective tool in biodiesel fuel engine studies.