Modelling of compression ignition engine by soft computing techniques (ANFIS-NSGA-II and RSM) to enhance the performance characteristics for leachate blends with nano-additives

Abstract

Integrating nanoparticles in waste oil-derived biodiesel can revolutionize its performance in internal combustion engines, making it a promising fuel for the future. Nanoparticles act as combustion catalysts, enhancing combustion efficiency, reducing emissions, and improving fuel economy. This study employed a comprehensive approach, incorporating both quantitative and qualitative analyses, to investigate the influence of selected input parameters on the performance and exhaust characteristics of biodiesel engines. The focus of this study is on the potential of using oils extracted from food waste that ended up in landfills. The study's results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system, Response surface methodology-Genetic algorithm, and Non sorting genetic algorithm. The analysis takes into account engine load, blend percentage, nano-additive concentration, and injection pressure, and the desired responses are the thermal efficiency and specific energy consumption of the brakes, as well as the concentrations of carbon monoxide, unburned hydrocarbon, and oxides of nitrogen. Root-mean-square error and the coefficient of determination were used to assess the predictive power of the model. Comparatively to Artificial Intelligence and the Response Surface Methodology-Genetic Algorithm model, the results provided by NSGA-II are superior. This is because it achieved a pareto optimum front of 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.020243% for Brake Thermal Efficiency, Brake Specific Energy Consumption, Oxides of nitrogen, Unburnt Hydro Carbon, and Carbon monoxide. Combining the precision of ANFIS's prediction with the efficiency of NSGA-optimization II's gives a reliable and thorough evaluation of the engine's settings. The qualitative assessment considered practical aspects and engineering constraints, ensuring the feasibility of applying the parameters in real-world engine applications.

Exergy-energy, sustainability, and emissions assessment of Guizotia abyssinica (L.) fuel blends with metallic nano additives

“…The validation procedure takes into account dimensionless quantities such as energy efficiency, exergy efficiency, and sustainability index. The value was estimated via the artificial neural network (ANN) computational method 28 , 41 . The obtained experimental value is later compared to the outputs generated by the ANN, as shown in Fig.…”

“…( 20 ) 26 . The job in question is accompanied by a certain level of uncertainty, as indicated by the % value provided 27 , 28 . The prevailing degree of uncertainty is around 2.247%.…”