Experimental and artificial neural network (ANN) study on the impact of three different metal oxide nanoparticle combustion enhancers enriched on mahua biodiesel-diesel fuelled CI engine

Abstract The prediction of CI engine parameters has acquired significant attention and is regarded as a crucial tool for engine research and diagnosis studies. This contribution compares two different approaches for diesel engine viz. experimental and artificial neural networks (ANNs) predictions of performance and emission outputs. The base fuel M30 blend consist of mahua biodiesel 30 (% v) and 70 (% v) of diesel. The M30 blend and 50 ppm concentration of NPs (CeO 2 , CuO, and TiO 2 ) were chosen for basic experimentation. The findings indicate an increase in BTE by 9.1%, peak CP by 11.3%, and HRR by 10.2%, with a decrease in BSFC by 13.7%, CO by 30.4%, HC by 30.1%, smoke by 34.7%, and NOx by 7.1%, resulting from the addition of 50 ppm CeO 2 NP to D70M30 blend. The widely used backpropagation technique for ANN is implemented in multilayered feedforward design. To forecast the engine characteristics of a CI engine, a network structures including two inputs and one output is used. The given ANN model examined D100, M30, M30CeNP50, M30CuNP50, and M30TNP50 blends, using engine load and biodiesel with nanoparticle blend as the two input factors. A data-driven ANN model was created to forecast the optimised engine characteristics. The lowest and highest value of correlation coefficient (R 2 ) and mean square errors (MSE), mean relative error (MRE) were found to for peak CP, HRR, BTE, BSFC, CO, CO 2 , HC, NOx and smoke. Using ANN one can choose right blend ratio among the variety of fuels blends for an appropriate requirement without much experimentation.