Type 3 Fuzzy-Based Performance Evaluation of Biodiesel Blends with Orange Peel Oil Additives in a Diesel Engine

Abstract

The global demand for energy is increasing, combined with an ongoing environmental concern about the application of fossil fuel has resulted in a quest to explore sound alternatives source of fuels. Biodiesel is a renewable fatty acid methyl ester that can be produced from biological feedstocks and represents an attractive alternative to conventional diesel, however optimizing blend formulations across several competing performance metrics continues to limit internal combustion engine (ICME) biodiesel utilization. This article develops a pioneering Type 3 fuzzy set based multi-criteria decision-making framework, that experimentally applies Neutrosophic membership in conjunction with weighted aggregation for systematic analysis and ranking of biodiesel blend formulations enhanced with orange peel oil (OPO) additives.

Single-cylinder diesel engine performance tests were performed at four load conditions  and  using over twenty different fuel formulations including neat diesel, biodiesel blends (i.e., B10–B100), ethanol-enriched blends [8], butanol-enriched blends [9], viscosity-modified blends [10] and novel OPO additive blended fuels at varying concentrations as detailed above. This study measured and evaluated four major performance parameters: e.g. Total Fuel Consumption (TFC), Specific Fuel Consumption (SFC), Brake Thermal Efficiency (BTE) and Brake Specific Energy Consumption, BSEC based on the concept of Engine monitoring. Type 3 fuzzy analysis estimated Neutrosophic membership values for each attribute, which were matched up with assigned weights to yield an overall ranking of the attributes' performance using weighted average aggregation.

The results of which indicate that BSEC was determined as the most limiting performance characteristic through fuzzy analysis, and the optimum blend was B10OPO10E10  biodiesel containing  orange peel oil, ethanol), with a corresponding maximum load BTE  of  and max-load BSEC (MJ/kWh) of  higher than neat diesel. Comparison against Type 1 and Type 2 fuzzy approaches confirms a superior ranking quality of the Type 3 framework in an environment with measurement uncertainty. Statistical validation confirms significant differences exist among blend formulations via one-way ANOVA. Fuzzy ranking is stable under perturbation in weights according to the sensitivity analysis. Collectively, these results provide a robust mathematical basis for the Type 3 fuzzy framework for fuel optimization problems as well as useful guidance on how to implement it effectively in practice.