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Integration of Design and Control: A Robust Control Approach Using MPC
Author(s) -
Chawankul N.,
Sandoval La Ricardez,
Budman H.,
Douglas P. L.
Publication year - 2007
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.5450850406
Subject(s) - control theory (sociology) , model predictive control , process (computing) , mathematical optimization , fractionating column , computer science , process control , robust control , stability (learning theory) , control (management) , control system , engineering , distillation , mathematics , artificial intelligence , chemistry , organic chemistry , machine learning , electrical engineering , operating system
This paper presents a new method to integrate process control with process design. The process design is based on steady‐state costs, .i.e., capital and operating costs. Control is incorporated into the design in terms of a variability cost. This term is calculated based on the non‐linear process model, represented here as a nominal linear model supplemented with model parameter uncertainty. Robust control tools are then used within the approach to assess closed‐loop robust stability and to calculate closed‐loop variability. The integrated method results in a non‐linear constrained optimization problem with an objective function that consists of the sum of the steady costs and the variability cost. Optimization using the traditional sequential approach and the new integrated method was applied to design a multi‐component distillation column using a Model Predictive Control (MPC) algorithm. The optimization results show that the integrated method can lead to significant cost savings when compared to the traditional sequential approach. In addition, an RGA analysis was performed to study the effects of process interactions on the optimization results.