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All Journal Automotive Experiences Journal of Social Research
Nasruddin Nasruddin
Universitas Indonesia, Indonesia
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Improving The Use of Biogas/Biohydrogen in Dual Fuel Engines Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Hamza Khamis Kombo; Ridho Irwansyah; Nasruddin Nasruddin
Journal of Social Research Vol. 4 No. 11 (2025): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v4i10.2833

Abstract

Growing energy demand and the need to reduce the emission of greenhouse gases have created greater interest in alternative fuels such as diesel substitutes, with biodiesel, biogas, and bio-hydrogen being rated as the viable alternatives. Biodiesel improves combustion and reduces CO and HC emissions, biogas is economically viable utilization but its efficiency is impacted by the loss resulting from the presence of CO?, and bio-hydrogen supports the development of flame, thermal efficiency, and reduces carbon-based emissions. However, issues with abnormal combustion, reduced efficiency, and high levels of NOx with high levels of substitution necessitate optimization of the parameters. In this study, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to evaluated and optimize the effect of load, compression ratio, ignition pressure, and gas flow rates on engine performance and emission. RSM outputs reported load, ignition pressure, and bio-hydrogen to have strong effects on BTE, BSFC, CO, and NOx with a maximum of 40.55% BTE, 303.48 g/kWh BSFC, 2.35 g/kWh CO, and 869.78 ppm NOx. ANN models reported a good predictive capability with R² > 0.99 and were better at predicting emission trends compared to RSM. The integration of RSM and ANN offers a highly effective tool for optimizing dual-fuel diesel engines to attain improved efficiency, improved fuel utilization, and reduced emissions for green energy use.
Effect of Graphene Oxide Addition on Spark Ignition Engine Performance and Cycle-to-cycle Variation with Gasoline-ethanol Fuel Askar Adika Agama; Ahmad Syihan Auzani; Alfian Ferdiansyah Madsuha; Hendra Hermawan; Ade Kurniawan; Mokhtar Mokhtar; Aswin Aswin; Nasruddin Nasruddin; Yulianto Sulistyo Nugroho; Harinaldi Harinaldi
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14237

Abstract

A fuel blend of gasoline and ethanol increases octane, meets air quality standards, and satisfies renewable fuel mandates, but the blend does not always result in perfect bonding, causing fuel separation and increasing cyclic variation. To overcome these limitations, up to 60 ppm graphene oxide (GO) nanoparticles were added into an 80:20 gasoline-ethanol blend (E20) and tested for the first time on a spark-ignition (SI) engine. The engine performance was evaluated by measuring cyclic variation, combustion stability and pressure, torque and power, specific fuel consumption, and CO2 emission. The acquired data were then statistically treated by using a coefficient of variation (COV) and then evaluated with Response Surface Methodology (RSM) in order to demonstrate a strong ability to accurately predict the optimization. Results show that the addition of GO nanoparticles into the E20 reduced the COV by up to 19.54% at an engine speed of 8000 rpm when compared to E20 alone, while the torque and power both increased by 5% at 5500 rpm. The specific fuel consumption of the GO-E20 blend was up to 15% higher than that of E20, with a decrease in CO emission but an increase in CO2 emission. Generally, the E20GO blend positively impacts the SI engine’s cyclic stability and performance, but its potential adverse effects on the environment and health must be carefully considered.
Co-Authors Ade Kurniawan Ahmad Syihan Auzani Alfian Ferdiansyah Madsuha Askar Adika Agama Aswin Aswin Hamza Khamis Kombo Harinaldi Harinaldi Hendra Hermawan Mokhtar Mokhtar Ridho Irwansyah Yulianto Sulistyo Nugroho