Hybrid experimental and artificial neural network modeling for cold start emissions prediction in PCM-assisted diesel engines

This study presents a hybrid experimental–computational approach integrating phase change materials (PCMs) and artificial neural network (ANN) modeling to predict cold-start emissions in diesel engines. During cold starts, insufficient fuel vaporization causes incomplete combustion and significantly increases CO, HC, and NO emissions. To mitigate this problem, a PCM-assisted thermal energy storage (TES) system was designed to preheat the intake air, utilizing latent heat stored in the PCM. Experiments were performed on a two-cylinder, water-cooled, direct-injection diesel engine under various PCM initial temperatures (6–60 °C). Measured parameters included PCM temperature, intake air temperature, and exhaust emissions (CO, CO₂, HC, NO). Building upon these data, a multilayer perceptron ANN model was developed with four inputs (initial PCM temperature, time, PCM temperature, and intake air temperature) and four outputs (CO, HC, CO₂, NO). The network architecture comprised six hidden layers and 500 neurons, using sigmoid and tanh activation functions. The model achieved high predictive accuracy with coefficients of determination (R²) of 0.913, 0.984, 0.959, and 0.926 for CO, CO₂, HC, and NO, respectively, and correspondingly low RMSE values. These results confirm that the ANN successfully captured the nonlinear dependencies between thermal conditions and emission behavior. The proposed hybrid methodology reduces the need for extensive experimental testing while maintaining high prediction reliability. Consequently, this study demonstrates the potential of PCM-assisted intake air heating combined with ANN-based prediction for efficient cold-start management, reduced emissions, and the intelligent thermal optimization of diesel engines.