A nonlinear regression based multi-objective optimization of parameters based on experimental data from an IC engine fueled with biodiesel blends

This study reports the results of an experimental investigation of the performance of an IC engine fueled with a Karanja biodiesel blends, followed by multi-objective optimization with respect to engine emissions and fuel economy, in order to determine the optimum biodiesel blend and injection timings complying with Bharat Stage II emission norms. Nonlinear regression has been used to regress the experimentally obtained data to predict the brake thermal efficiency, NOx, HC and smoke emissions based on injection timing, blend ratio and power output. To acquire the data, experimental studies have been conducted on a single cylinder, constant speed (1500 rpm), direct injection diesel engine under variable load conditions and injection timings for neat diesel and various Karanja biodiesel blends (5%, 10%, 15%, 20%, 50% and 100%). Retarding the injection timing for neat Karanja biodiesel resulted in an improved efficiency and lower HC emissions. A tradeoff relationship between the NOx and smoke emissions is observed, which makes it difficult to determine the optimum blend ratio. The functional relationship developed between the correlating variables using nonlinear regression is able to predict the performance and emission characteristics with a correlation coefficient (R) in the range of 0.95–0.99 and very low root mean square errors. The outputs obtained using these functions are used to evaluate the multi-objective function of optimization process in the 0–20% blend range. The overall optimum is found to be 13% biodiesel-diesel blend with an injection timing of 24°bTDC, when equal weightage is given to emissions and efficiency.

► In this study experiments on blends of Karanja biodiesel with diesel are carried out on a compression ignition diesel engine.
►  Detailed parametric studies conducted to investigate thermal performance and emission characteristics as a function of blend ratio, injection timing and brake power.
► Multi-objective optimization framework set up to reduce emissions without compromising on efficiency.
► Artificial neural networks used to aid the optimization.
► There exists an optimum combination of parameters at which substantial reduction in emissions is achieved with a marginal drop in brake thermal efficiency.