An improved capacitance–resistance model for analysing hydrogen production with geothermal energy utilisation

The integration of geothermal and hydrogen energy through advanced predictive modelling not only enhances energy production efficiency but also contributes significantly to the global transition towards renewable energy sources. This study commences with the establishment of a geothermal hydrogen production model using Aspen Plus, incorporating physical constraints, derived from the Capacitance–Resistance Model (CRM) model. Refinements to Long Short-Term Memory (LSTM) neural networks based on historical data and CRM constraints tailor them for geothermal reforming-based hydrogen production. Production analysis indicates that the hybrid CRM–LSTM model adeptly predicts heat and hydrogen production, improving system performance. The approach demonstrates superior accuracy, with hydrogen production predictions deviating by less than 2%. The comparison highlights the hybrid model's advantage in handling nonlinear characteristics but it also shows the hybrid model requires longer training times. Sensitivity analysis reveals significant implications for investment decisions, with CRM predicting a 26-year cost recovery period under standard conditions, potentially underestimating actual outcomes by over eight months. Such discrepancies underscore the importance of accurate predictive models in guiding investment decisions for sustainable energy projects. This model contributes to achieving sustainable development goals by integrating geothermal and hydrogen energy, advancing the transition towards renewable and environmentally friendly energy sources.