Rapid prediction, optimization and design of solar membrane reactor by data-driven surrogate model

Abstract

Membrane reactor is a process intensification technology that enhances traditional reactions. This study investigates the optimal operating conditions and structural designs of an insert-enhanced solar methane-to-hydrogen Pd-based membrane reactor (SMMR) for more efficient conversion-reaction and separation-purification. Applying response surface methodology, a data-driven surrogate model is fitted to SMMR for rapid performance prediction, which can combine algorithms to optimize operating conditions and structures. The results indicate that operating conditions center around matching reaction kinetics to strengthen reaction, while structural parameters focus on weakening concentration polarization to enhance separation. Methane feed flow and steam-to-methane ratio are almost always the dominant factors, especially in balancing methane conversion and fuel efficiency. Moreover, SMMR under high-pressure more relies on optimized inserts’ transport enhancement to improve overall reaction-separation performance. The optimized results by single- and multi-objective optimization are similar, where the 4-helical insert delivers a comprehensive excellent methane conversion of 0.95, fuel efficiency of 0.86 and hydrogen recovery of 0.95 at 400 °C inlet temperature and 8 bar pressure. Overall, the optimization of SMMR surrogate model consumes less than 0.2 % of the single simulation CPU time and the average error of the results is about 3 %, showing high efficiency and prediction accuracy.