Prediction of CI Engine Emissions Fueled with Multiwalled Carbon Nanotube-Doped Waste Cooking Oil Biodiesel using Multilayer Neural Network

Nanocatalysts play a significant role to improve the thermal and physical properties of biodiesel. In the present work, the multiwalled carbon nanotubes (MWCNTs) as an additive with the fraction of 30, 40, and 50 ppm are dispersed with the different biodiesel–diesel blends of 10%, 30%, and 50% of waste cooking oil (WCO)-based biodiesel (B10, B30, B50) for the prediction of four-stroke compression ignition (CI) engine emissions using multilayer neural network (MLNN) model. An MLNN model uses a backpropagation algorithm to map input and output parameters. The input parameters to MLNN are load, blends, and MWCNTs in ppm. On the other hand, the output parameters are HC, CO, and NOx. The results for the optimum topological structure of 3-10-3 denoted mean square error (MSE) equal to 0.095 that are capable of predicting the emissions for different operating conditions. Thereafter, the developed MLNN model is tested on an experimental setup consisting of a single-cylinder four-stroke CI engine and emission analyzer. The emission characteristics predicted by MLNN are called to be nearly experimental measurements with reasonable accuracy as it depicts the good “R” values as 0.95, 0.96, and 0.976 for HC, CO, and NOx, respectively, and also gives the reasonable average relative error values as 0.83%, 1.01%, and 1.05%, for HC, CO, and NOx, respectively. Further, the developed model is suitable for predicting emissions of CI engines, thus minimizing the cost, time, and labor effort.