Comparative analysis of the performance and emissions of a multi-cylinder diesel engine using biodiesel from underutilized feedstocks

In this work, an RSM-based DOE approach was adopted to investigate and perform a comparative study on the performance and emission characteristics of a multi-cylinder transportation-type diesel engine running at variable speeds using 20% biodiesel blends of <i>Manilkara zapota</i> (MZME), <i>Bauhinia variegata</i> (BVME), Karanja (KME), and <i>Simarouba</i> (SME). The powerful desirability-based optimization technique was used to optimize system performance while reducing emissions, thereby meeting the demands of sustainable energy targets and stricter environmental regulations. To further validate the effectiveness of RSM, the results were compared with the performance of several advanced machine learning (ML) algorithms, including linear regression, decision tree, and random forest. It was found that the average reduction in BP and BTE for the blends of MZME, BVME, KME and SME was approximately in the range of 3-6%, 7-11%, 5-9% and 8-12%, respectively, whereas the increase in BSFC was 2%, 4%, 3% and 5%, respectively, compared with the diesel fuel. The average reduction in the emissions of HC and CO was in the range of 30%, 15-22%, 20-27% and 7-15% for MZME, BVME, KME and SME blends, respectively, and for NO _<i>x</i> emissions, the average increase was found to be 6%, 17%, 10% and 20%, respectively, compared with the diesel fuel. The random forest model demonstrated a lower MAPE than the other models, confirming its superiority in predictive modeling, with respect to both generalization and accuracy balance. The outcomes also demonstrated that both RSM and ML were highly effective modelling tools, offering accurate predictions of biodiesel performance and emission behaviour. The integrated experimental and predictive approach provided a robust framework for optimizing biodiesel formulations by identifying the optimal biodiesel blend ratio.