Model Predictive Control of an Exothermic Batch Reactor using Near Infrared (NIR) Spectroscopic Measurements as Feedbac

Batch and semi-batch processes provide needed flexibility for multi-product plants, especially when products change frequently and production quantities are small. However, challenges occur when trying to implement reliable control systems in batch processes due to some unavoidable inherent characteristics such as the presence of time-varying and nonlinear batch process dynamics and a host of unmeasured disturbances. The most typical control strategy employed in batch process operations does not use utilise online measurements of variables directly related to the product quality and as such is bound to produce off specification products even when the specified control objective has been met. Work done in this thesis is concerned with the design of a supervisory control scheme that takes into consideration the online status of the quality variable of interest from the beginning to the end of the batch process.A novel control methodology is proposed which combines the speed and flexibility of Near-Infrared (NIR) spectroscopic measurements as quality feedback variables within a multiple model predictive control (MPC) framework. In particular the multivariate NIR spectral data is pre-processed for feedback using a statistical model based on Independent Component Analysis (ICA). The proposed controller is tested on a benchmark simulated batch reactor using several case studies and is demonstrated to bring significant improvement in control performance when contrasted with other inferential and direct quality controllers.