Application of Artificial Neural Networks Coupled with Sequential Pseudo-Uniform Design to Optimization of Membrane Reactors for Hydrogen Production

Abstract

Fuel cells with on board reforming require compact and lightweight components. A membrane reactor (MR) that combines hydrogen permeable membranes with a methanol steam reformer promises considerable weight and space savings. Its dense metal membranes produce high purity hydrogen over a wide range of pressure and load. For a real application of MR, there is much incentive to determine optimal operating conditions of a membrane reactor without resorting to the time consuming knowledge-based modeling work. In this work, a Pd membrane reactor (PMR) for carrying out the methanol steam reforming was simulated and adopted as the test process for verification of the applicability of the proposed optimization method. The artificial neural networks (ANN) with back propagation algorithms coupled with the sequential pseudo-uniform design (SPUD) was applied and demonstrated successfully to the modeling of the PMR system using limited but adequate experimental data. The optimum operating conditions determined from the identified ANN model were applied precisely.