Sequential sub-problem programming strategies for data reconciliation and parameter estimation with multiple data sets

Data reconciliation and parameter estimation (DRPE) is a key problem in real-time optimization. The dimensionality of the DRPE problem increases directly with the number of data sets, and the number of degrees of freedom in DRPE is very large. Therefore, solving a DRPE problem is very difficult. Sequential sub-problem programming strategies for data reconciliation and parameter estimation with multiple data sets are proposed in this paper. Based on the characteristics of a DRPE optimization problem, we construct a series of sub-problems depending on objective and model parameters. The solutions of each sub-problem are a good initial guess of the optimum of the next sub-problem. By solving the series of sub-problems, the optimum of the DRPE optimization problem can be derived. The proposed sequential sub-problem programming strategies are used in the industrial purified terephthalic acid (PTA) oxidation process system. The effectiveness of the proposed strategies is demonstrated by the results of numerical experiments.