Implementation of Soft Computing Techniques in Predicting and Optimizing the Operating Parameters of Compression Ignition Diesel Engines: State-of-the-Art Review, Challenges, and Future Outlook

Abstract

Abstract Fossil fuels being the primary source of energy to global industrialization and rapid development are being consumed at an alarming rate, thus creating a dire need to search for alternative fuels and optimize the internal combustion (IC) engine performance parameters. Traditional methods of testing and optimizing the performances of IC engines are complex, time consuming, and expensive. This has led the researchers to shift their focus to faster and inexpensive techniques like soft computing (SC), which predict the optimum performance with a substantial accuracy. The SC techniques commonly used are artificial neural network (ANN), fuzzy logic, adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm (GA), particle swarm optimization (PSO), and hybrid techniques like ANN-GA, ANN-PSO, and others. The data of engine parameters predicted with these models have been found to be in very close indices with the experimented values making them a reliable predicting tool. The ANN, fuzzy logic, and ANFIS models have been found to have a correlation coefficient (R) above 0.9 suggesting a good level of agreement between experimented and predicted values of several engine-out parameters. In the present review article, the application of various SC techniques in the prediction and the optimization of output parameters of compression ignition (CI) diesel engines are thoroughly reviewed along with their future prospects and challenges. This review work highlights the implication of these SC techniques in CI diesel engines run on both conventional fuel as well as biodiesels.