Closed-Loop Process Identification

Proportional Test In this test, a set of step increases of magnitude M is performed. The response of the system should be the same during each step increase interval. The ideal response is shown in Fig. 6.6 (right). 

Finally, the prediction error model assumes that the parameter values do not change with respect to time, that is, they are time invariant. A quick and simple test of the invariance of the model is to split the data into two parts and cross validate the models using the other data set. If both models perform successfully, then the parameters are probably time invariant, at least over the time interval considered. 

The general closed-loop system is shown in Fig. 6.7, for which the transfer function can be written as 

6 Modelling Dynamic Processes Using System Identification Methods 

The primary issue with closed-loop identification is how to identify the desired process model Gp given the two competing equations. 

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The third and final method for closed-loop process identification is called the joint input-output identification method, which uses all three signals, y and in order to identify a model of the system in a two-step procedure. In the first step, a model between r, and m, is fit to give... 

The PBL reactor considered in the present study is a typical batch process and the open-loop test is inadequate to identify the process. We employed a closed-loop subspace identification method. This method identifies the linear state-space model using high order ARX model. To apply the linear system identification method to the PBL reactor, we first divide a single batch into several sections according to the injection time of initiators, changes of the reactant temperature and changes of the setpoint profile, etc. Each section is assumed to be linear. The initial state values for each section should be computed in advance. The linear state models obtained for each section were evaluated through numerical simulations. 

One final point about closed-loop process control. Economic considerations dictate that to derive optimum benefits, processes must invariably be operated in the vicinity of constraints. A good control system must drive the process toward these constraints without actually violating them. In a polymerization reactor, the initiator feed rate may be manipulated to control monomer conversion or MW however, at times when the heat of polymerization exceeds the heat transfer capacity of the kettle, the initiator feed rate must be constrained in the interest of thermal stability. In some instances, there may be constraints on the controlled variables as well. Identification of constraints for optimized operation is an important consideration in control systems design. Operation in the vicinity of constraints poses problems because the process behavior in this region becomes increasingly nonlinear. 

The primary objective is to perform system identification, that is, obtain a plant model, especially that of the process, in order to design a controller. Two different situations can be considered open-loop system identification and closed-loop system identification. 

The second method is called direct identification, where the fact that the process is running in closed loop is ignored. In this type of identification, both the process and error structures must be simultaneously estimated. Thus, either a Box-Jenkins or a general prediction error model should be fit. Since this is one of the more common approaches to closed-loop system identification, it is necessary to examine the properties of this approach. It will be assumed that the prediction error method will be used. 

Indirect identification of closed-loop processes requires that only the input and output signals be available. 

Hazard identification and risk management Is the closed-loop process that identifies and evaluates hazards and prioritizes the corresponding risks so that that can be adequately managed. 

An automated pilot-scale 1-litre experimental polymer reactor system with facilities for on-line measurement of flow rate, temperature and density has been set up by Chien and Penlidis (1994a, b). These authors describe a set of open-loop process identification experiments and closed-loop control experiments performed on this system where monomer conversion is controlled in the presence of reactive impurities using the initiator flow rate as the manipulated variable. 

An HTS does not imply that a hazard is just passively stored in a database and then forgotten. Hazard tracking is a dynamic process in which the SSP takes positive steps to eliminate or mitigate the hazard and record aU actions. Hazards are tracked from inception (identification) to closure, with focus on reporting and acceptance of the final residual hazard-mishap risk. Hazard tracking should be a closed-loop process, meaning that the review and mitigation process is repeated iteratively, until final closure of the hazard is achieved. 

The cote system safety process can therefore be reduced to Hazard Identification -> Hazard Risk Assessment -> Hazard Risk Control -> Hazard Risk Verifica-tion-> Hazard Identification... (Ericson 2005). This is a closed-loop process where Hazards ate identified and tracked until acceptable closure action is implemented and verified. 

In the present study, we propose a tuning method for PID controllers and apply the method to control the PBL process in LG chemicals Co. located in Yeochun. In the tuning method proposed in the present work, we first find the approximated process model after each batch by a closed-loop Identification method using operating data and then compute optimum tuning parameters of PID controllers based on GA (Genetic Algorithm) method. 

The identification of plant models has traditionally been done in the open-loop mode. The desire to minimize the production of the off-spec product during an open-loop identification test and to avoid the unstable open-loop dynamics of certain systems has increased the need to develop methodologies suitable for the system identification. Open-loop identification techniques are not directly applicable to closed-loop data due to correlation between process input (i.e., controller output) and unmeasured disturbances. Based on Prediction Error Method (PEM), several closed-loop identification methods have been presented Direct, Indirect, Joint Input-Output, and Two-Step Methods. 

Does not require identification and measurement of any disturbance for corrective action Does not require an explicit process model Is possible to design controller to be robust to process/model errors Control action not taken until the effect of the disturbance has been felt by the system Is unsatisfactory for processes with large time constants and frequent disturbances May cause instability in the closed-loop response... 

Other recent developments in the field of adaptive control of interest to the processing industries include the use of pattern recognition in lieu of explicit models (Bristol (66)), parameter estimation with closed-loop operating data (67), model algorithmic control (68), and dynamic matrix control (69). It is clear that discrete-time adaptive control (vs. continuous time systems) offers many exciting possibilities for new theoretical and practical contributions to system identification and control. 

The process has to operate in a new regime where an accurate process model is not available, yet the cost of off-line identification experiments for the development of such a model is prohibitively high, thus making closed-loop identification necessary. 

The main challenge of closed-loop identification is that feedback control leads to quiescent process behavior and poor conditions for process identification, because the process is not excited (see, for example, Radenkovic and Ydstie, 1995, and references therein). Traditional methods for excitation of a process (SOderstrom et al, 1975 Fu and Sastry, 1991 Van Der... 

Y Cheng, W Karjala, and DM Himmelblan. Resolving problems in closed loop nonlinear process identification nsing IRN. Comput. Chem. Engg., 20(10) 1159-1176, 1996. 

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