INCA MPC4Distillation: A Novel Industrial Solution for Cost-Efficient Non-Linear Distillation Column Modeling and Control

Abstract Distillation columns are one of the first unit processes to which MPC (Model-based Predictive Control) was successfully applied in an industrial context. The methodology that is used to execute such APC (Advanced Process Control) projects has not fundamentally changed in the decades that have passed since its inception. Linear black-box models are still the most commonly used type of models in such applications. However, obtaining black-box models by performing dedicated plant tests is often prohibitively time-consuming (and hence costly), which limits the cost-efficient application of APC. On the other hand, in several applications non-linear models offer significant benefits when combined with a high-performance non-linear APC controller. INCA MPC4Distillation employs a novel modeling approach, by combining physical knowledge and historical data in order to identify dynamic models that can be used for APC. This new technology results in accurate non-linear models and does not rely on dedicated step tests, which results in increased benefits to the customer and at a lower cost than existing methods. Experience shows that INCA MPC4Distillation models can typically be obtained in a matter of days, as opposed to several weeks when one would use the classical modeling approaches. The results are illustrated using results obtained on a high-purity binary distillation column.