Eric Visser, FRANCOIS Gregory, WELZ Carsten, SRINIVASAN Bala, BONVIN Dominique
Dynamic optimization has been widely studied in literature. The available studies often involve implementing a profile that has been determined off-line on the basis of a model. However, this approach may not lead to optimality when there is uncertainty on raw materials, model parameters, or when there are some disturbances. Industry typically copes with uncertainty by introducing conservatism that guarantees constraint satisfaction. Such an approach is normally very conservative and performance can be quite poor. The core idea of measurement-based optimization is to use process measurements to compensate for the effect of uncertainty.
Among measurement-based optimization methods, the indirect ones are those where the model is refined periodically and used for reoptimization. This method is computationally expensive and the model-refinement suffers from the lack of persistency of excitation. Thus, the direct measurement-based optimization approach is investigated here, where an optimized reference is tracked.
The main contribution of this project is to demonstrate that the necessary conditions of optimality can be tracked in the presence of uncertainty. The optimal solution consists of two parts, (i) the constraints and (ii) the sensitivities, i.e, the gradient of the cost with respect to the input. Furthermore, in dynamic optimization, there are two types of constraints and sensitivity conditions: i) the path conditions that are related to quantities during the batch and ii) the terminal conditions that are related to quantities at the end of the batch. Various methodologies for tracking the path and terminal conditions using appropriate measurements are considered in subsequent projects.
Related publications
2003.04
Bonvin D. and B. Srinivasan. Optimal Operation of Batch Processes via the Tracking of Active Constraints. ISA Transactions, 2003, 42, 123-134
2003.02
Srinivasan B., D. Bonvin , E. Visser and S. Palanki. Dynamic Optimization of Batch Processes: II. Role of Measurements in Handling Uncertainty. Comput. Chem. Engng., 2003, 27, 27-44
2003.01
Srinivasan B., S. Palanki and D. Bonvin. Dynamic Optimization of Batch Processes: I. Characterization of the Nominal Solution. Comput. Chem. Engng., 2003, 27, 1-26
2000.11
Visser E., B. Srinivasan, S. Palanki and D. Bonvin. A Feedback-Based Implementation Scheme for Batch Process Optimization. Journal of Process Control, 10, 399-410
1999.27
Visser E., B. Srinivasan, S. Palanki and D. Bonvin. On-Line Optimization of Batch Processes by Tracking States and Costates. IFAC World Congress, Beijing, China (July 1999), N, 31-36
1997.09
Srinivasan B., E. Visser and D. Bonvin. Optimization-Based Control with Imposed Feedback Structures. IFAC Symposium ADCHEM, Banff, Canada, pp. 635-640 (June 1997).