Improving the combustion process by determining the optimum percentage of liquefied petroleum gas (LPG) via response surface methodology (RSM) in a spark ignition (SI) engine running on gasoline-LPG blends

Şimşek, Süleyman; Uslu, Samet; Şimşek, Hatice; Uslu, Gonca

doi:10.1016/j.fuproc.2021.106947

Cited by 50 publications

(2 citation statements)

References 38 publications

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“…From the observation, the experimental and model values agree with each other, where the difference is only less than ±10%. This acceptance level is also shared by other researchers [39], [40]. This is true for all the performance indicators selected (brake torque, brake power, BSFC, and BTE).…”

Section: Comparison Between Performance Experimental Value and Model ...supporting

confidence: 54%

Design of Experiment to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance

Daud

Hamidi²,

Mamat³

2022

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To minimise diesel exhaust emissions, a few methods are commonly used. Engine modifications, combustion optimisation, and exhaust system treatment components are among them. Fuel additives, such as zinc oxide, titanium oxide, aluminium oxide, and cerium oxide, are amongst the most effective methods to increase performance and reduce emissions. Even while positive performance and emission reduction outcomes have been demonstrated, there are worries concerning health toxicity effects. Carbon nanoparticles have been accepted as a fuel additive since they pose little risk to human health. A few studies have been undertaken to investigate the consequences of employing graphene nanoplatelets as fuel additives, thanks to advancements in graphene research. The findings of the study seemed encouraging. However, despite detecting the additive effects of graphene on performance, no more study has been undertaken to forecast the effects on engine performance. The objective of this study was to predict the effects of graphene nanoplatelets as an additive for diesel engines. The performance parameters of the trial were torque, power, BSFC, and BTE. Speed, load, and blend concentration are all considered in this model. Response surface methods and contour plotting with Minitab software were used to generate the prediction model. The results show that the prediction model is within 10% of the experimental data.

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Section: Comparison Between Performance Experimental Value and Model ...supporting

confidence: 54%

Design of Experiment to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance

Daud

Hamidi²,

Mamat³

2022

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“…These results can be presented as evidence that the modeling was successful. 43,44 In addition, it can be said that high-accuracy estimation of experimental results can be made using regression equations.…”

Section: Resultsmentioning

confidence: 99%

Modeling of a port fuel injection spark-ignition engine with different compression ratios using methanol blends with the response surface methodology

Yeşilyurt

Uslu

Yaman

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this study, the response surface methodology was applied to verify the optimum compression ratio, methanol percentage, and engine load in order to obtain the best levels of engine response that will occur when using methanol (0, 10, and 20% by vol.) in a spark-ignition engine under different compression ratio (6.0:1, 8.0:1, and 10.0:1) and engine load (8, 10, and 12 kg) conditions. A response surface methodology aided by analysis of variance was created using the three-factor and three-level central composite full design with the results of the experiment. With the created model, optimum methanol percentage, compression ratio, and engine load levels corresponding to the finest brake thermal efficiency, brake-specific fuel consumption, carbon monoxide, carbon dioxide, hydrocarbon, and nitrogen oxide emission levels were determined. According to the optimization results, the optimum methanol percentage, compression ratio, and engine load were found to be 10.5%, 6.0:1, and 12 kg, respectively. Hydrocarbon, nitrogen oxide, carbon monoxide, carbon dioxide, brake thermal efficiency , and brake-specific fuel consumption corresponding to optimum operating conditions were determined as 63.568 ppm, 840.643 ppm, 0.365%, 14.059%, 28.199%, and 0.286 kg/kWh, respectively. To test the reliability of the response surface methodology results, experiments with optimal methanol, compression ratio, and engine load were carried out and compared with the response surface methodology findings. As a result, it can be said that the response surface methodology is a successful application for the optimization of a spark-ignition engine using methanol as an alternative fuel with different engine parameters.

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A review of combustion properties, performance, and emission characteristics of diesel engine fueled with Al2O3 nanoparticle-containing biodiesel

Sharifianjazi

Esmaeilkhanian

Karimi

et al. 2023

Clean Techn Environ Policy

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Improving the combustion process by determining the optimum percentage of liquefied petroleum gas (LPG) via response surface methodology (RSM) in a spark ignition (SI) engine running on gasoline-LPG blends

Cited by 50 publications

References 38 publications

Design of Experiment to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance

Design of Experiment to Predict the Effects of Graphene Nanoplatelets Addition to Diesel Engine Performance

Modeling of a port fuel injection spark-ignition engine with different compression ratios using methanol blends with the response surface methodology

A review of combustion properties, performance, and emission characteristics of diesel engine fueled with Al2O3 nanoparticle-containing biodiesel

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