International Journal of Science, Engineering and TechnologyAuthors: Nand Kishore Chopara, Om Prakash Sondhiya
Abstract: Reactivity Controlled Compression Ignition (RCCI) combustion represents a highly promising low-temperature combustion strategy capable of simultaneously breaking the trade-off between nitrogen oxides (NOx) and soot emissions while maintaining high brake thermal efficiency. However, achieving ultra-low emissions and optimal performance across dynamic operating envelopes requires precise multi-variable coordination of low-reactivity and high-reactivity fuel blends. This study presents a comprehensive experimental investigation and parallel artificial intelligence (AI)-based multi-objective optimization of an ethanol-hydrogen dual-fuel RCCI engine setup. Port injection of an ethanol-water superheated vapor matrix serves as the low-reactivity fuel charge, dynamically supplemented by on-demand, exhaust-heated catalytic steam reforming of bioethanol to provide a high-velocity hydrogen-rich syngas stream. A baseline high-reactivity pilot fuel triggers the multi-stage auto-ignition process. The operational space is mapped using a three-factor, three-level Central Composite Design (CCD) framework spanning variations in reforming temperature (500°C–800°C), steam-to-ethanol molar ratios (2.0–6.0), and syngas volumetric fractions (0%–25%). To overcome classical multi-variable optimization limits, statistical Response Surface Methodology (RSM) is coupled with a back-propagation Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) trained via the Levenberg-Marquardt routine, achieving superior predictive correlation (R² > 0.994). Multi-dimensional Computational Fluid Dynamics (CFD) transit models developed in ANSYS Fluent elucidate in-cylinder turbulence, chemical species concentrations, and localized thermal profiles. The optimized AI-guided operating paradigm demonstrates a substantial 14.9% increase in maximum Brake Thermal Efficiency (BTE) under part-load frameworks, alongside dramatic drops of 65%–80% in carbon monoxide (CO) and 45%–60% in unburned hydrocarbon (UHC) profiles, realizing a practical, closed-loop technical pathway toward high-efficiency, zero-carbon scalable powertrain solutions.
DOI: http://doi.org/10.5281/zenodo.20742501
