Control of a Batch Reactor Using a Multivariate Statistical Controller Design

Abstract

Batch processes play an important role in the chemical, pharmaceutical, and agricultural industries. However, the main characteristics of batch processes, flexibility, finite duration, and nonlinear behavior are associated with both their success and their incompatibility with the usual techniques for control. The batch reactor studied indirectly controls the extent of reaction by using reactor pressure to control temperature which is as an inferential measurement of extent of reaction. The manipulated variable is the flow of the heating or cooling medium through a split range valve. However, just controlling to a reactor temperature profile will not necessarily reduce product nonuniformity and batch-to-batch variability. In this paper, it is proposed to control the reactor via control actions calculated in a reduced representation space of a principal component representation, the score space. This space is determined by the interrelationships among the process variables using Multi-way Principal Component Analysis (MPCA); the control action that will regulate the scores to the nominal score trajectories are calculated. The development of the MPCA representation of the process and the associated controller based on score space data are presented. The performance of this controller formulation in the face of a typical heat transfer disturbance is illustrated.