ABSTRACT
After the COVID-19 pandemic has spread throughout the world, many research institutions and industrial organizations are putting great efforts into producing environmentally friendly solutions for the transportation sector. This paper presents a newly developed combined solid oxide fuel cell system with a turbofan engine that can use five alternative fuels, such as dimethyl ether, methanol, hydrogen, methane, and ethanol, with different blending ratios to form five fuel blends. The proposed system is studied in this paper using exergoenvironmental analysis (which is known as environmental impact assessment by exergy) in order to quantify and evaluate the environmental impact. The combined turbofan has an exergetic efficiency of 82%, with total fuel and product exergy rates of 905 and 743 MW, respectively. The total environmental impact caused by emissions and exergy destructions has a range of 4000 to 9000 Pt/h for all the fuel blends. The specific exergoenvironmental impact values of electricity production vary from about 3 to 8 mPt/MJ for solid oxide fuel cells and 10 to 25 mPt/MJ for the three turbines. The exergoenvironmental impact of the thrust force is a minimum of 34 Pt/(h.kN) for the RF1 fuel and a maximum of 87 Pt/(h.kN) for the RF4 fuel. © 2022
ABSTRACT
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%. © 2022 Elsevier Ltd