Model-based control

Caccavale et al., Control and Monitoring of Chemical Batch Reactors, Advances in Industrial Control, 

Tin temperature of the fluid entering the jacket [K] At sampling time [s] 

Y gain setting the parameter estimate update rate 

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An ethylene plant contains more than 300 equipment items. Traditionally, operators were trained at the site alongside experienced co-workers. With the advent of modem computers, the plant operation can be simulated on a real-time basis, and the results displayed on monitors (107). Computers are used in a modem plant to control the entire operation, eg, they are used to control the heaters and the recovery section (108). A weU-controUed plant is much more profitable than a poorly controlled plant. For the heaters, a model-based control system is gaining importance (109). Instead of simply controlling the coil outlet temperature (COT), severity is actually controlled. The measurement of severity (either or C H /CH ratio) requires on-line effluent... 

The Smith predictor is a model-based control strategy that involves a more complicated block diagram than that for a conventional feedback controller, although a PID controller is still central to the control strategy (see Fig. 8-37). The key concept is based on better coordination of the timing of manipulated variable action. The loop configuration takes into account the facd that the current controlled variable measurement is not a result of the current manipulated variable action, but the value taken 0 time units earlier. Time-delay compensation can yield excellent performance however, if the process model parameters change (especially the time delay), the Smith predictor performance will deteriorate and is not recommended unless other precautions are taken. 

Introduction The model-based contfol strategy that has been most widely applied in the process industries is model predictive control (MFC). It is a general method that is especially well-suited for difficult multiinput, multioutput (MIMO) control problems where there are significant interactions between the manipulated inputs and the controlled outputs. Unlike other model-based control strategies, MFC can easily accommodate inequahty constraints on input and output variables such as upper and lower limits or rate-of-change limits. 

In many cases, plants simply live with these problems. However, use of modern model-based control schemes in conjunction with improved methods for on-line moisture and particle size analysis can help overcome these effects [Ennis (ed.), Powder Tech., 82 (1995) Zhang et al., Control of Paiticulate Processes TV (1995)]. 

Mathews and Rawlings (1998) successfully applied model-based control using solids hold-up and liquid density measurements to control the filtrability of a photochemical product. Togkalidou etal. (2001) report results of a factorial design approach to investigate relative effects of operating conditions on the filtration resistance of slurry produced in a semi-continuous batch crystallizer using various empirical chemometric methods. This method is proposed as an alternative approach to the development of first principle mathematical models of crystallization for application to non-ideal crystals shapes such as needles found in many pharmaceutical crystals. 

Memo. No. 1140. Massachusetts Institute of Technology, Cambridge, MA, 1989. Psichogios, D. C., and Ungar, L. H., Direct and indirect model based control using artificial neural networks, Ind. Eng. Chem. Res. 30, 2564 (1991). 

The design procedures depend heavily on the dynamic model of the process to be controlled. In more advanced model-based control systems, the action taken by the controller actually depends on the model. Under circumstances where we do not have a precise model, we perform our analysis with approximate models. This is the basis of a field called "system identification and parameter estimation." Physical insight that we may acquire in the act of model building is invaluable in problem solving. 

Just like feedforward control (or any other model-based control), we only have perfect compensation if... 

Recall Eq. (10-46) that Hj and H2 are defined entirely by the four plant functions Gy. This is another example of model-based control. With the definitions of Hj and H2 given in (10-46), the calculations are best performed with a computer. 

BROSILOW AND Joseph Techniques of Model-Based Control... 

In the last chapter we used Laplace-domain techniques to study the dynamics and stability of simple closedloop control systems. In this chapter we want to apply these same methods to more complex systems cascade control, feedforward control, openloop unstable processes, and processes with inverse response. Finally we will discuss an alternative way to look at controller design that is called model-based control. 

One final comment should be made about model-based control before we leave the subject. These model-based controllers depend quite strongly on the validity of the model. If we have a poor model or if the plant parameters change, the performance of a model-based controller is usually seriously affected. Model-based controllers are less robust than the more conventional PI controllers. This lack of robustness can be a problem in the single-input-single-output (SISO) loops that we have been examining. It is an even more serious problem in multi-variable systems, as we will find out in Chaps. 16 and 17. 

One of the most interesting and unique approaches to the design of sampled-data controllers is called minimal-prototype design. It is one of the earliest examples of model-based controllers. 

There is a special type of controller, called a Smith predictor or deadtime compensator, that can be applied in either continuous or discrete form. It is basically a special type of model-based controller, in the same family as IMC. Figure 20.6a gives a sketch of a conventional feedback control system. Let s break up the total openloop process into the portion without any deadtime G j,(s) nd deadtime e... 

C. Brosilow and 1. Babu, Techniques of Model-Based Control, Prentice HaU PTR, Upper Saddle River, 2002. 

When thermodynamics or physics relates secondary measurements to product quality, it is easy to use secondary measurements to infer the effects of process disturbances upon product quality. When such a relation does not exist, however, one needs a solid knowledge of process operation to infer product quality from secondary measurements. This knowledge can be codified as a process model relating secondary to primary measurements. These strategies are within the domain of model-based control Dynamic Matrix Control (DMC), Model Algorithmic Control (MAC), Internal Model Control (IMC), and Model Predictive Control (MPC—perhaps the broadest of model-based control strategies). 

The details of the control strategy have received much less attention. The theoretical (159) and experimental (160) analyses of the transfer function for CZ growth are notable exceptions. Algorithms for model-based control are just being developed. 

The method proposed for improving the batch operation can be divided into two phases on-line modification of the reactor temperature trajectory and on-line tracking of the desired temperature trajectory. The first phase involves determining an optimal temperature set point profile by solving the on-line dynamic optimization problem and will be described in this section. The other phase involves designing a nonlinear model-based controller to track the obtained temperature set point and will be presented in the next section. 

Since both the on-line dynamic optimization and the model-based control strategy rely on process models, the knowledge of current states and/or model parameters is required. However, in most industrial processes, state variables are not all measurable and some parameters are not known exactly. As a consequence, there is a need for estimating these states and parameters. In this work, two Extended Kalman Filters (EKF) are implemented. The first one is applied to predict the reactant concentration, which will be used for on-line dynamic optimization, from its delayed measurement. The other one is applied to estimate the unknown heat of reaction, which will be used for model-based controller, from the frequently available measurements of temperature. 

Z.H. Liu, S. Macchietto, Model based control of a multipurpose batch reactor - an experimental study, Comp. Chem. Eng. 19(Suppl) (1995) s477-s482. 

J.X. Shen, M.S. Chiu, Q.G. Wang, A comparative study of model-based control techniques for batch crystallization process, J. Chem. Eng. Jpn. 32 (4) (1999) 456 164. 

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