Hydrogen/Diesel Dual-Fuel Engines Modeling Using Temporal Kolmogorov-Arnold Networks

Hydrogen/diesel dual-fuel (HDDF) engines are being investigated as a promising solution for reducing carbon dioxide and providing a transition to zero-carbon fuels. However, modeling these engines presents challenges due to the complex interactions between the two fuels. Computational fluid dynamics modeling is often inaccurate across various operating conditions, while conventional data-driven approaches frequently face issues related to a lack of interpretability. Furthermore, based on the complexity of the system, there is a shortage of accurate physics-based modeling. This study explores temporal Kolmogorov-Arnold networks (T-KANs) as an alternative to conventional machine learning (ML) models. T-KANs offer a structured framework for function approximation that can efficiently learn the underlying dependencies with historical data while ensuring interpretability. A T-KAN model is developed and trained using experimental HDDF engine data, demonstrating its ability to predict performance metrics, including indicated mean effective pressure and nitrogen oxides. A comparative analysis of seven different models of well-known ML methods highlights the advantages of T-KANs in terms of accuracy, generalization, and computational efficiency. Given its computational efficiency and a coefficient of determination of 0.956, the T-KAN network with a 10-lookback is suitable for a model-based controller. This configuration effectively utilizes historical data while operating independently of sensor feedback.