Decentralized plantwide control strategy for large-scale processes. Case study: Pulp mill benchmark problem

The plantwide control (PWC) complexity increases for highly-integrated and large-scale chemical processes. This work presents a novel framework for decentralized PWC which includes: (i) the selection of the controlled variables (CVs), (ii) the pairing between the manipulated variables (MVs) and the CVs, and (iii) the determination of the controller algorithms as well as their tuning parameters for closed-loop operation. The proposal is to solve the steps (i) and (ii) simultaneously, driving the selection of the most effective PWC structure from a Pareto optimal set. Here, algorithms based only on steady-state information are considered to give a systematic procedure which tries to minimize the use of heuristic considerations. Genetic algorithms (GA) and the Hungarian algorithm (HA) are used here because they provide a good trade-off between computational effort and acceptable results. The proposed methodology is completely tested in a pulp mill benchmark and compared with a previous one.