Premium
Two‐layer model predictive control for chemical process model with integrating controlled variables
Author(s) -
Yang Yuanqing,
Ding Baocang
Publication year - 2020
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23600
Subject(s) - model predictive control , control theory (sociology) , offset (computer science) , process (computing) , consistency (knowledge bases) , control engineering , steady state (chemistry) , computer science , engineering , control (management) , artificial intelligence , chemistry , programming language , operating system
This paper proposes an overall solution to the two‐layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open‐loop prediction module, the steady‐state target calculation (SSTC) module, and the dynamic control module. Based on the real‐time output measurements and past inputs, the open‐loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi‐priority ranks. The dynamic control module receives the steady‐state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady‐state targets are adjusted dynamically after the occurrence of disturbances, and offset‐free control is achieved.