Surrogate Modeling & Optimization of a Nonlinear Batch Reactor by Polynomial Chaos Expansion

Abstract

The paper presents a computationally efficient approach to develop a nonlinear data driven input/output model between the finite-time control trajectories and the quality index at the end of the batch. Polynomial chaos expansion (PCE) was applied to produce the approximate representation of the full process model of a nonlinear batch reactor with the reaction scheme .A-->^k1B--> ^k2C A surrogate model was developed to estimate the dependence of intermediate product (B) concentration at the end of the batch on the temperature trajectory applied during the reaction. The surrogate model was then validated for its performance. Later, the surrogate model was used to determine the optimal temperature profile needed to maximize the concentration of intermediate product at the end of the batch. The validation and optimization results prove that the experimental data based PCE can provide a very good approximation of the desired outputs, providing a generally applicable approach for rapid design, control and optimization of batch reactor systems.

Chemical Engineering Research Bulletin 21(2020) 121-126