Optimization of performance and emission outputs of a CI engine powered with waste fat biodiesel: A detailed RSM, fuzzy multi-objective and MCDM application

Decreasing the influence of diesel engines on the environment by mitigating harmful exhaust emissions is a real-life target to reach a cleaner atmosphere. Accordingly, the present work aims to reduce emissions while pre-serving enhanced performance in the diesel engine. From this point of view, the biodiesel from chicken fats was produced with the following of esterification as transesterification processes, then its mixtures with conventional diesel fuel were comprehensively investigated. A diesel engine was experimentally tested at varying engine speeds and loads. Both response surface methodology (RSM) and fuzzy multi-objective modeling techniques were used to predict the engine performance, and exhaust pollutants of diesel engine fueled with chicken biodiesel blends. Central composite RSM was used for the experimental design. Different responses were modeled mathematically via highly statistically significant models. A nonlinear fuzzy-multi-objective optimization model was also constructed and optimally solved in the paper. The multi-objective optimized results show that the blending ratio of 24.42%, engine load of 64.1%, and engine speed of 2616.6 rpm were the optimum operating conditions for the different performance and emission concentrations. These results were validated experimen-tally and the relative error was within & PLUSMN;6.67%. Sensitivity analysis was handled for the discussion of the model performance under the different importance of the performance and emission criteria. The model is capable to satisfy the decision maker's needs and gives the corresponding operating conditions. In the results, it is well-noticed that RSM, fuzzy multi-objective, and multi-criteria decision-making (MCDM) supports are good tools to both predict, and optimize the engine behaviors.