Economic analysis and TOPSIS approach to optimize the CI engine characteristics using span 80 mixed carbon nanotubes emulsified Sapindus trifoliatus (soapnut) biodiesel by artificial neural network prediction model

Abstract

The rise in fuel use within the vehicle sector leads to a corresponding escalation in energy demand. To achieve optimal operating conditions, it is necessary to reduce energy usage. For that, the primary objective of this study is to utilize an artificial neural network (ANN) and the optimization approach as a technique for order performance by similarity to the ideal solution (TOPSIS) to forecast the optimal features of a compression ignition (CI) engine. The experiment was performed with a four-stroke mono-cylinder compression ignition (CI) engine under different load situations. The study considers six operational parameters, including brake thermal efficiency (BTE), specific fuel consumption (SFC), as well as emission characteristics including carbon monoxide (CO), hydrocarbon (HC), Nitrogen oxides (NOx), and smoke. From the ANN results, the correlation coefficients for BTE, SFC NOx, smoke, HC, and CO were 0.9712, 0.9862, 0.964, 0.941, 0.998, and 0.978, respectively. The SNBD25 + 30 mg/L SP80 + 30 mg/L CNT blend exhibited the maximum closeness coefficient under various load conditions. The maximum closeness coefficient obtained from the TOPSIS optimisation approach under full load conditions is 0.982386. From the result of the ANN prediction technique, for TOPSIS optimised blend SNBD25 + 30 mg/L SP80 + 30 mg/L CNT a 13.84% increase in BTE, 11.21% decrease in SFC, 16.67% decrease in CO, 13.87% decrease in NOx, 19.23% decrease in HC, and 32.53% decrease in smoke. Based on the outcomes obtained from the ANN and TOPSIS methodologies, it can be concluded that the SNBD25 + 30 mg/L SP80 + 30 mg/L CNT blend exhibits superior performance in terms of reduced NOx emissions and enhanced efficiency, surpassing the other blends under consideration. The incorporation of carbon nanotubes (CNTs) into a system leads to an increase in the carbon-to‑oxygen ratio, resulting in a reduction in the generation of nitrogen oxides (NOx). The cost of a mixture consisting of SNBD25 + 30 mg/L SP80 + 30 mg/L blend is 20% lower than the cost of diesel on a per-litre basis.