Performance and Emission Analysis an RCCI engine fueled with Gasoline-n Butanol and Biodiesel-Diesel Blends

Abstract

Conventional compression ignition engines are widely used due to their high thermal efficiency and reliability; however, they remain major contributors to nitrogen oxides (NOx), particulate matter, and carbon dioxide (CO2), which contribute to air pollution and global climate change. Growing concerns over fossil fuel depletion and environmental degradation intensified the search for cleaner, renewable, and more sustainable fuel alternatives. Although reactivity-controlled compression ignition (RCCI) combustion has emerged as a promising strategy for improving engine efficiency and reducing emissions, several research gaps persist. These include challenges in achieving stable combustion at higher loads, limited fuel flexibility, and insufficient exploration of oxygenated biodiesel-alcohol fuel combinations. Moreover, there is a scarcity of optimization based studies for determining optimal blends and operating conditions in RCCI combustion mode. In response to these issues, the present study integrates Response surface methodology (RSM) and Artificial neural networks (ANN) to optimize biodiesel production and its application in a RCCI engine, using biodiesel-diesel blend as a high reactivity fuel and a gasoline-n-butanol blend as a low reactivity fuel. Biodiesel was produced from cottonseed oil through the transesterification process using methanol and a KOH catalyst, with RSM optimization identifying optimal conditions that yielded of 94.80% biodiesel. The fuel’s chemical composition was validated through GC-MS analysis. The RCCI engine experiments were carried out on a modified single-cylinder CI engine at varying engine speeds and of gasoline- n-butanol blend ratios. The results demonstrated that the B20+G50n-b50 blend produced the maximum cylinder pressure of 92.25 bar at 2800 rpm, significantly outperforming the baseline fuel’s 57.24 bar. This blend also delivered the maximum brake power of 4.35 kW. In contrast, the lowest NOx (150 ppm) and CO2 (3.7 vol%) emissions were achieved using the B20+G25n-b75 blend at the same speed, while HC and CO emissions were minimized with the B20+G75n-b25 blend. The RSM and ANOVA analysis confirmed strong predictive capability for performance and emissions, with R2 values exceeding 97% for all key parameters. The optimal operating condition was identified to be 2523 rpm with a 51.78% n butanol ratio. Overall, this study demonstrates the effectiveness of RCCI combustion in reducing emissions and enhancing performance, offering a viable pathway toward cleaner and more sustainable engine technologies.