MPC Model Assessment of Highly Coupled Systems

Systems with strong interactions among the variables are frequent in the chemical industry, and the use of model predictive control (MPC) is a standard tool in these scenarios. However, model assessment in this case is more complex when compared with fairly coupled systems, since the interactions make the system more sensitive to the model uncertainties. It means that, if the coupling is high, a small modeling error in a single variable could be spread to the entire system. As a result, all the controlled variables (CVs) of the MPC will have their performance deteriorated and the root of the model problem will not be evident. This paper presents a method of model assessment for highly coupled systems. This is an extension of the method proposed by Botelho et al.(1) for model-plant mismatch evaluation in MPC applications, based on the use of the diagonal elements of the output sensitivity matrix. One of its advantages is that the method does not require previous knowledge regarding the systems coupling level. The effectiveness of the proposed method is illustrated by two case studies: a high-purity distillation column and the Shell heavy oil fractionator.