Prediction of Properties, Engine Performance and Emissions of Compression Ignition Engines Fuelled with Waste Cooking Oil Methyl Ester - A Review of Numerical Approaches

Abstract

Given the challenges of renewability, affordability, and sustainability that ultra-low sulfur diesel (ULSD) fuel and first-generation biodiesel currently face, a niche clearly exists for biodiesel sourced from waste cooking oil. Exploring these potentials requires insights into a range of properties as well as performance and emission characteristics. Real-time experimental determination of fatty acid methyl ester (FAME) properties, engine performance and emissions of compression ignition (CI) engines fuelled with unblended FAME is technical, time-consuming, expensive, and requires sophisticated laboratory infrastructure, unlike numerical prediction techniques. Emerging trends and consensus indicate that utilization of numerical simulation and prediction techniques are cost-effective, less laborious, innovative, and flexible. Although FAME properties, engine performance, and emissions have been accurately predicted via various numerical techniques in the recent past, important gaps such as using numerical approaches to determine the optimal FAME mix still exist. The objective of this study was to review the numerical techniques employed in predicting FAME properties, engine performance and emissions of CI engine and investigate whether an optimal FAME candidate can be unearthed through these numerical prediction techniques. This review reports that the outcomes of these numerical predictions agree with experimental data and fall within acceptable average relative deviation but a precise approach to determining an optimal FAME candidate was not achieved. The use of numerical tools like MATLAB and computational fluid dynamics (CFD) in amalgamation with some new measurement techniques including high-speed cameras to accurately predict FAME properties, engine performance, emission characteristics and gain access to the activities in the combustion chambers, were highlighted. These high capacity modeling tools, high-speed cameras, and techniques can be used to accurately forecast an optimal FAME mix to improve engine performance, meet emission benchmarks and advanced engine research, if well harmonized.