Constrained Latent Variable Model Predictive Control for trajectory tracking and economic optimization in batch processes

J. L. GODOY; A. H. GONZÁLEZ; J. E. NORMEY-RICO

JOURNAL OF PROCESS CONTROL

ELSEVIER SCI LTD

Lugar: Amsterdam; Año: 2016 vol. 45 p. 1 - 11

0959-1524

A constrained latent variable model predictivecontrol (LV-MPC) technique is proposed for trajectory tracking and economicoptimization in batch processes. The controller allows the incorporation ofconstraints on the process variables and is designed on the basis of multi-wayprincipal component analysis (MPCA) of a batch data array rearranged by meansof a regularized batch-wise unfolding. The main advantages ofLV-MPC over other MPC techniquesare: (i) requirements for the dataset are rather modest(only around 10-20 batch runs are necessary), (ii) nonlinearprocesses can efficiently be handled algebraicallythrough MPCA models, and (iii) the tuning procedure is simple. The LV-MPC for trackingis tested through a benchmark process used in previous LV-MPC formulations. Theextension to economic LV-MPC includes an economic cost and it is based on modeland trajectory updating from batch to batch to drive the process to theeconomic optimal region. A data-driven model validity indicator is used toensure the prediction?s validity while the economic cost drives the process toregions with higher profit. This technique is validated through simulations ina case study.