Processes control models

BALZHISER, SAMUELS, AND Eliassen Chemical Engineering Thermodynamics BEQUETTE Process Control Modeling, Design and Simulation BEQUETTE Process Dynamics... 

Dynamic models, fitting to experimental data, 20 689-691 Dynamic process control models, 20 687-688... 

Any on-line process control model used for computer-aided manufacturing of high-performance composite laminates must include a thorough treatment of void stability and growth as well as resin transport. These two key components, along with a heat transfer model and additional chemorheological information on kinetics and material properties, should permit optimized production of void-free, controlled-thickness parts. A number of advances have been made toward this goal. 

In traditional process control, models are often used to predict the deviation of the controlled variable from the desired state, the process error. This assumes that one knows the desired state. In complex batch processes, the desired state of the process is also dependent on history and changing dynamically. Further, most process models have to predict the outcome of an entire cycle to determine if the product will be good, so predictions are not available in real time, even for a slow process like the autoclave cure however, partial models have been used as virtual sensors to expand on the information available from sensors [38]. Saliba et al. used a kinetic model to predict the degree of cure as a function of time and temperature in a mold and used that predicted degree of cure to time pressure application and determine the completion of cure. Others [39] have used the predictions of models together with the measured progress of the process to predict future trends and even project process outcomes. 

Control System Development Model-based design space development offers an ideal segue between process and control development. Quite literally, a model-based design space would provide the template for development of feedforward process control models. Moreover, development of a process design space using a model-based framework would facilitate control system validation and identification of science-based, in-process, and release specifications. 

J.B. Balchen, B. Lie, and I. Solberg. Internal decoupling in nonlinear process control. Modeling Identification and Control, 9 137-148, 1988. 

W. Bequette, Process Control Modeling, Design, and Simulation, Prentice-Hall, 2003. 

First order series/parallel chemical reactions and process control models are usually represented by a linear system of coupled ordinary differential equations (ODEs). Single first order equations can be integrated by classical methods (Rice and Do, 1995). However, solving more than two coupled ODEs by hand is difficult and often involves tedious algebra. In this chapter, we describe how one can arrive at the analytical solution for linear first order ODEs using Maple, the matrix exponential, and Laplace transformations. 

Keywords dependability evaluation, fault injection, process control, model predictive control. 

There are a number of modeling approaches that can be used with process control systems. Whereas mathematical models based on the chemistry and physics of the system represent one alternative, the typical process control model utilizes an empirical input/output relationship, the so-called black-box model. These models are found by experimental tests of the process. Mathematical models of the control system may include not only the process but also the controller, the final control element, and other electronic components such as measurement devices and transducers. Once these component models have been determined, one can proceed to analyze the overall system dynamics, the effect of different controllers in the operating process configuration, and the stability of the system, as well as obtain other usefid information. 

Astrom, K.J. and Wittenmark, B. 1994. Adaptive Control, 2nd ed. Addison-Wesley, Reading, MA. Bequette, B.W. 2003. Process Control Modeling, Design and Simulation. Prentice-HaU, Upper Saddle River, NJ. 

Most models have been developed and tested only under steady state conditions In recent years some attention has been devoted to applying two-phase models for process control and simulation of unsteady conditions (e g 37,87,88,97-99) More unsteady state modelling is required, especially since process control models used for fluidized beds are often crude. Further work is also needed to allow the Influence of fluctuations in properties and exchange rates to be modelled successfully ... 

With this continuous real-time data stream on the polymer properties and reaction kinetics, it is anticipated that ACOMP will allow for immediate benefits with tighter operator control. Evenmally, the goal will be to use the continuous stream of process data on polymer properties and reaction kinetics yielded by ACOMP to create a complete feedback control closed loop system. This will be achieved by developing low-error process control models using ACOMP and general process data. This union of ACOMP with process models... 

It was determined in this case that both in-cavity (behind ejector pin) pressure transducers and temperature sensors can be utilized to monitor micro-cellular injection molding and should be able to be developed as a basis of a process control model. For the pressure transducer, the gate location appeared to track part weight and dimensions more closely than at the end of fill, across the range of process changes. 

If a reasonably accurate dynamic model of the process is available, it is advantageous to base the controller design on the process model. A wide variety of model-based design strategies are available for designing PID controllers. In this section, we consider two important model-based design methods that are especially useful in process control. Model-based techniques can also be used to design feedforward controllers (Chapter 15) and advanced control systems (Chapters 16,17, and 20). 

The concept Process Control Settings represents the parameterization of Process Control Model. By separating it into two concepts the more stable knowledge is represented by Process Control Model instances while the product-specific parameterization is performed through Process Control Settings instances. 

Depending on the purpose of the control model and the individual needs of the used control software component, occurrences of Process Control Model are... 

Seborg, D. E. A perspective on advanced strategies for process control. Model. Ident. Control, 1984,15(3) 179-89. 

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