Using CRITIC-TOPSIS and python to examine the effect of 1-Hepatnol on the performance and emission characteristics of CRDI CI engine with split injection.

Recently, biofuels with higher alcohol content have become a promising alternative to diesel fuel. These fuels are appealing because they are sustainable, renewable, and possess attractive fuel properties. This study uses a split injection strategy to analyze the performance and emissions of a CRDI diesel engine fueled by 1-heptanol. The work involved testing different fuel blends, ranging from 10 % to 30 %, while maintaining a constant engine speed of 1500 rpm and varying the operating load between 0 kg and 12 kg in 4 kg increments. During the second stage, the CRITIC-TOPSIS method determines the objective weights and rankings of various criteria and alternatives. A Python approach based on machine learning was used to ensure the CRITIC-TOPSIS results were accurate. Seven criteria were evaluated to maximize BTE while minimizing BSFC, NOx, smoke opacity, HC, CO, and CO(2). The experimental results showed a slight drop of 2.98 % in BTE and an increase of about 13.33 % in BSFC. NOx and smoke opacity were reduced by 7.13%-4.53 %, while there was a 12.12 % increase in HC, 6.45 % higher CO, and a 5.5 % increase in CO(2) at full load. Adding 1-heptanol to diesel and using a split injection strategy significantly reduced NOx and smoke opacity. The final ranking and best blend are determined using CRITIC-TOPSIS and Python algorithms to estimate performance and emissions criteria. At a load of 4 kg, D100 ranks first with a relative closeness value of 0.642, while at a pack of 8 kg, the blend HP20D80 ranks first with a relative closeness value of 0.633. According to the rankings, the HP20D80 blend is the best option for achieving optimal performance and reduced emissions in CRDI diesel engines. A research paper has presented a unique approach to multiple criteria decision-making (MCDM) validated using a Python algorithm. This method can assist decision-makers in making better-informed choices when faced with MCDM problems that involve various criteria and alternatives.