Experimental Investigation and neural network modelling of diesel engine using hexanol blended ternary waste cooking oil biodiesel with moderate preheating

The depleting fossil fuel reserves, rising air pollution, technology transformation threat, and most recently, global economic slowdown by the COVID-19 pandemic, led the internal combustion engine-based automotive industries in a critical condition. The development of improved biofuels to meet stringent emission norms is a promising solution. Higher alcohols possess the fuel properties better than lower alcohols to blend with diesel and biodiesel. The miscibility and higher viscosity is the issue. Preheating can help the vaporization and atomization of fuel. The present study investigates the engine characteristics of moderately preheated ternary fuel using 20 to 40% blends of 1-hexanol, waste cooking oil biodiesel, and diesel. The study found that moderately preheated ternary fuel blends showed a drop in brake-specific fuel consumption, HC, CO, and smoke emissions with improvement in peak cylinder pressure, heat release rate, and brake thermal efficiency. A multi-layer neural network model is developed to prognosticate the engine characteristics. Backpropagation algorithm-based neural network with single hidden layers using Levenberg–Marquardt training function gave the best results. The mean square error of the network was 0.00028517 and the correlation coefficient was 0.99944, 0.99945, and 0.99923 for training, validation, and testing respectively. The mean absolute percentage error was found below 4%..