Training Neural Networks for Pressure Swing Adsorption Processes

Abstract

A three-layer neural network is used to analyze the relationship between input and output variables for pressure swing adsorption (PSA) processes. The network is trained using a modified version of the back-propagation (BP) algorithm. The modification consists of minimizing the error corresponding to a reordered set of inputs. The inputs are arranged according to the errors (e.g., largest to smallest) they generate at the output of the network. The BP training algorithm is then implemented on the input-output measurement which gave the largest error. This operation is repeated during the entire training process. This paper demonstrates the effectiveness of reordering after single and multiple passes (epochs) through the training set. The method is illustrated by application to a PSA cycle for separation of carbon monoxide and hydrogen. In another application, a PSA cycle for hydrogen production from natural gas is discussed. In these two applications, a single epoch using reordering is discussed. A third example pertains to nitrogen production from air by PSA/vacuum swing adsorption (VSA) cycles. In this case, multiple epochs have been considered.