Excessive exhaust backpressure (EBP) in modern diesel engines disrupts gas exchange, increases residual gas fraction (RGF), and reduces combustion efficiency. Traditional experimental approaches, including simulations and bench testing, are often time-consuming and costly, which has driven growing interest in artificial neural networks (ANNs) for accurately modelling complex engine behavior. This research introduces an ANN model designed to predict the impact of EBP on the performance and emissions of a diesel engine across varying compression ratio (CR) of 12, 14, 16, and 18 and engine load (25%, 50%, 75%, and 100%) conditions. The ANN model was developed and optimised using genetic algorithms (GA) and particle swarm optimisation (PSO). It was then trained using data from an experimentally validated one-dimensional computational fluid dynamics (1D-CFD) model developed through GT-Power GT-ISE v2024, simulating engine responses under variation CR, load, and EBP conditions. The optimised ANN architecture, featuring an optimal (3-14-10) configuration, was trained using the Levenberg–Marquardt back propagation algorithm. The performance of the model was assessed using statistical criteria, including the coefficient of determination (R2), root mean square error (RMSE), and k-fold cross-validation, by comparing its predictions with both experimental and simulated data. Results indicate that the optimised ANN model outperformed the baseline ANN and other machine learning (ML) models, attaining an R2 of 0.991 and an RMSE of 0.011. It reliably predicts engine performance and emissions under varying EBP conditions while offering insights for engine control, optimisation, diagnostics, and thermodynamic mechanisms. The overall prediction error ranged from 1.911% to 2.972%, confirming the model’s robustness in capturing performance and emission outcomes.
Khanyi et al. (2025) studied this question.