Performance Evaluation of Tower-Type Solar-Driven Dry Reforming of Methane Reactor by Developing a MCRT-CFD-BPNN Model

Solar-driven <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> reforming of methane into fuel presents a promising solution for addressing both global warming and energy shortages. However, performance evaluation of the reactor is critical to the concentrated reforming system. Here, a comprehensive model combining a threedimensional optical-thermal model and a BP neural network model is developed to evaluate the performance of a reactor. After that, predictive calculations of the reactor performance under various operating conditions are conducted. The results indicate that the predicted values closely match the simulated values, confirming the reliability of the BP prediction model. Finally, the model is optimized using a multi-objective genetic algorithm, achieving the optimal reactor performance. This work provides reliable models for tower-type solar-driven greenhouse gas conversion technologies.