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Multi-objective Genetic Algorithms for the Integrated Design of Chemical Processes Using Advanced Control Techniques

In this work, the integrated design of a sulphitation tower using model-based predictive control (MPC) techniques is performed. An innovative controllability analysis is introduced in the integrated framework considering state and output controllability metrics. The MPC optimization is speeded out using a Restricted Enumeration Technique. The problem is formulated as a multi-objective optimization of the economical and control indices, which is solved using a real-coded genetic algorithm. A comparison is made between the simultaneous design and control methodology and the results of the automatic tuning of the MPC for the most economical plant design.