<![CDATA[This study investigates the use of deep learning techniques to forecast ignition delays in hydrogen combustion systems, with a focus on optimizing hydrogen combustion processes in industrial applications such as stationary power generation and the automotive industry. The work utilizes experimental data from a rapid compression machine (RCM) and a shock tube. Two large datasets were created through 0-D simulations and experimental measurements, covering a wide range of conditions. The study involves the development of two artificial neural network (ANN) models, one for RCM and another for shock tube data, each with distinct architectures. The ANN models were trained, tested, and evaluated using thoughtfully divided datasets. The results demonstrate the effectiveness of the developed ANN models in predicting ignition delays with remarkable accuracy. Comparative analyses with 0-D simulations and experimental measurements reveal that the ANN models predict ignition delays "1000 times faster" than traditional simulation methods. This speed improvement is crucial for real-time industrial applications, allowing engineers to quickly optimize combustion parameters, adjust engine settings, and make operational decisions in a fraction of the time. The study highlights the potential of these ANN models to optimize hydrogen combustion processes, improving combustion efficiency, reducing operational costs, and enhancing resource utilization in industrial settings. This progress can play a significant role in optimizing hydrogen-powered internal combustion engines by increasing fuel efficiency, reducing emissions, and enhancing overall engine performance. In the automotive and power generation sectors, the quick predictive abilities of ANN models can support more effective energy production, decrease operational expenses, and lessen environmental harm.]]>
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