Optimizing hydrogen yield in sorption-enhanced steam methane reforming: A novel framework integrating chemical reaction model, ensemble learning method, and whale optimization algorithm

High hydrogen production is essential in sorption-enhanced steam methane reforming (SE-SMR). This study presents an innovative optimization framework to optimize the H2 yield of SE-SMR through a coupled reaction model, prediction surrogate model, and whale optimization algorithm. Firstly, a computational fluid dynamics chemical reaction model of SE-SMR is constructed. A database is established to consider the H2 yield of SE-SMR with temperature, pressure, velocity, and steam to carbon ratio. Then, a surrogate model is developed on the basis of eXtreme Gradient Boosting (XGBoost) to predict the H2 yield under various operating parameters. Finally, the surrogate model is exploited to calculate the fitness function of the whale optimization algorithm to construct an optimization framework for maximizing the H2 yield. The results show that the XGBoost ensemble surrogate model can predict the H2 yield of SE-SMR within 4 seconds with high accuracy. The optimization framework can optimize the maximum H2 yield and the corresponding operating parameters within 1 minute. The optimization results are validated by applying the model and the percentage error of only 0.79%. Moreover, under optimal operating conditions, methane is almost completely converted to hydrogen, and the dry hydrogen mole fraction could reach 95.96%. The presented optimization framework can guide the prediction of H2 yield and the optimization of operating parameters in SE-SMR.