Optimal control, online

Surveyor is suitable for parallel process development and optimization with online sampling and integrated HPLC analysis. It employs ten reaetion vessels (working volume 15-45 mL), with individually controlled reaction temperatures from —40° to -l-150°C, with the ability to reflux. Reagent addition, reaction parameter control, sampling, and HPLC injection are controlled by built-in software. 

Because the control calculations are based on optimizing control system performance, MPC can be readily integrated with online optimization strategies to optimize plant performance. 

Therefore, the controller is a linear time-invariant controller, and no online optimization is needed. Linear control theory, for which there is a vast literature, can equivalently be used in the analysis or design of unconstrained MPC (Garcia and Morari, 1982). A similar result can be obtained for several MPC variants, as long as the objective function in Eq. (4). remains a quadratic function of Uoptfe+ -iife and the process model in Eq. (22) remains linear in Uoptfe+f-ife. Incidentally, notice that the appearance of the measured process output y[ ] in Eq. (22) introduces the measurement information needed for MPC to be a feedback controller. This is in the spirit of classical hnear optimal control theory, in which the controlled... 

Online Optimizing Control of a Reactive Simulated Moving Bed Process 9.4.1... 

As discussed above, the task of the controller is to optimize the performance of the process over a certain horizon in the future, the prediction horizon. Specifications of product purities, equipment limitations and the dynamic process model (a full hybrid model of the process, including the switching of the ports and a general rate model of all columns) appear as constraints. The control algorithm solves the following nonlinear optimization problem online ... 

The state estimation technique can also be incorporated into the design of optimal batch polymerization control system. For example, a batch reaction time is divided into several control intervals, and the optimal control trajectory is updated online using the molecular weight estimates generated by a model/state state estimator. Of course, if batch reaction time is short, such feedback control of polymer properties would be practically difficult to implement. Nevertheless, the online stochastic estimation techniques and the model predictive control techniques offer promising new directions for the improved control of batch polymerization reactors. 

Additional process information could be derived from continuous online redox reaction monitoring. Reaction data such as the amount of sodium bisulfite used from batch to batch would indicate varying amounts of unreacted bromine that provided an indication of incomplete reaction or the prevalence of other undesired side reactions. The amount and quality of the data derived from the online redox provided an excellent opportunity for process optimization, control, and real validation. 

In principle, there are three possible remedies for this problem. First, the plant can be operated with a safety margin - the most common but not the most economic approach. Second, some critical speciflcations can be controlled online, by feeding back the measured values to a controller, especially the product purities as the most critical parameters. This feedback can also be realized manually, which requires the continuous presence of skilled operators. The most advanced approach is to establish the optimality of the operation continuously and automatically, based on more or less sophisticated plant models and the available measurements. 

In the same spirit, Alamir, Ibrahim, and Corriou (2006) proposed an online optimizing controller that switches between different performance criteria. 

Ktipper, A. and Engell, S. (2008) Engineering of Online Optimizing Control -A Case Study Reactive SMB Chromatography. Proceedings of the 17th IFAC World Congress, Seoul, pp. 964—969. 

Wilfrid Marquis-Favre, Omar Mouhib, Bogdan Chereji, Daniel Thomasset, Jerome Pousin, and Martine Picq. Bond graph formulation of an optimal control problem for linear time-invariant systems. Journal of the Franklin Institute, 345(4) 349-373, 2008. Available online 17 November 2007. 

The off-line optimization results when applied in practice often become suboptimal due to ever-existing process disturbances and changes in process dynamics (e.g., when capacity is increased). Online optimal control can circumvent this problem and ensure optimum process operation aU the time. 

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