Modeling and optimization of a styrene monomer reactor system using a hybrid neural network model

Abstract

The operation of a current styrene monomer plant requires large quantities of energy to heat and cool its processing streams. Especially, operating a styrene monomer reactor system under proper conditions is very important because this reactor system occupies a large portion of the total operating cost using a large amount of expensive high-pressure steam. The optimization of the operating conditions of this reactor system can therefore be used to significantly reduce the total cost. To predict the dehydrogenation reactor conditions and take into account the effects of catalyst deactivation, we propose in this paper an alternative hybrid model of the reactor that is composed of a mathematical model and a neural network model. The mathematical model is a first principle model that predicts the compositions and the temperature and pressure profiles from the reaction mechanism and reactor geometry. The catalyst deactivation factor used in the mathematical model is calculated with the neural network model. Actual plant data were used in this study to test the hybrid model. Using this reactor model, we were able to solve the optimization problem for this plant. The objective of the optimization was to maximize the performance of the dehydrogenation reactor. A trajectory optimization method is proposed in this study that reduces the calculation required for the optimization. In this method, the trajectory of each operating variable is optimized while the other operating variables are held constant at their average values. Empirical equations are then obtained from the optimal trajectories, and the parameters of the empirical equations for all operation trajectories are optimized simultaneously. We found that the optimal profit was greater than that currently obtained by the plant.