An artificial intelligence approach study for assessing hydrogen energy materials for energy saving in building. (March 2023) - Ma, Kun; Xu, Lingyu; Abed, Azher M.; Elkamchouchi, Dalia H.; Amine Khadimallah, Mohamed; Ali, H. Elhosiny; Algarni, H.; Assilzadeh, Hamid
Highlights: H2 thermal and storage rate were ultimately predicted. Artificial Neural Network (ANN) approach is used. Spiral-shaped thermal collector with water was superior. Constructed ANN model could be utilized to estimate the performance of H2 storage. Abstract: The main energy demand of the globe is provided by fossil fuels, which are nonrenewable and can no longer be utilized once depleted specifically at buildings. Hydrogen as the highest environmentally friendly fuel, is a renewable and clean fuel with a potential to be an energy carrier for the next generation. It also has the capacity to replace the current fossil fuel-based energy infrastructure and refinery products for building energy consumptions. This is seen and projected as a remedy for the aforementioned issues, such as global warming and environmental deterioration. The most significant elements to consider while establishing hydrogen infrastructure are environmental conditions. In this study, by the use of an Artificial Neural Network (ANN) approach in MATLAB, H2 thermal and storage rate were ultimately predicted. The outcome of the spiral-shaped thermal collector with water was superior to that of the other hydrogen generation methods. The findings predicted by ANN approaches demonstrate an outstanding correlation with the experimental outcomes. Consequently, it is recommended that the constructed ANN model might be utilized to estimate the performance of the H2 storage system in future research.