Implementation of adaptive optimal operation for a semi-batch reaction system

12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering

2015

The kinetic identification of chemical reaction systems often represents a time-consuming and complex task. This contribution presents an approach that uses rate estimation and feedback linearization to implement effective control without the use of a kinetic model. The reaction rates are estimated by numerical differentiation of reaction variants that are computed from measurements. The approach is illustrated in simulation through the temperature control of a continuous stirred-tank reactor.

Section: Simulated Examplementioning

confidence: 99%

Control of Reaction Systems via Rate Estimation and Feedback Linearization

Rodrigues

Billeter

12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering

2015

“…An adaptation approach that has been used extensively in the literature is the indirect (or model-based) adaptation scheme (Rawlings et al 1989, Ruppen et al 1998. This consists of refining the model (i.e., re-identifying the model parameters with fresh data) and using the refined model to recompute the reference trajectories.…”

Section: Measurement-based Optimizationmentioning

confidence: 99%

Optimal operation of batch processes via the tracking of active constraints

Srinivasan

2003

ISA Transactions

This paper presents a new measurement-based optimization framework for batch processes, whereby optimal operation is achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus, the optimal input trajectories can be adjusted using measurements without the use of a model of the process. When only batchend measurements are available, the proposed method leads itself to an efficient batch-to-batch optimization scheme. The approach is illustrated via the simulation of a semi-batch reactor under uncertainty.

“…The reaction system considered is described below and, for a more detailed description of the process and model, see Ruppen et al (1998):…”

Section: Examplementioning

confidence: 99%

Run-to-run optimization via control of generalized constraints

Srinivasan

Primus

Control Engineering Practice

et al. 2001

Self Cite

Run-to-run optimization methodologies exploit the repetitive nature of batch processes to determine the optimal operating policy in the presence of uncertainty. In this paper, a parsimonious parameterization of the inputs is used and the decision variables of the parameterization are updated on a run-to-run basis using a feedback control scheme which tracks signals that are invariant under uncertainty. In this run-to-run framework, terminal constraints of the optimization problem and cost sensitivities constitute the invariant signals. The methodology is adapted to improve the cost function from batch to batch without constraint violation.