Dynamic process control

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

The fascinating thermodynamics of block copolymers that results from microphase separation are the subject of the parts 2.2,2.3, and 2.4 of Chapter 2. Part 2.4 is concerned with the complex kinetic processes that accompany phase transitions, and the dynamic processes controlled by the structure of the block copolymer melt. 

This chapter is concerned with the mathematical modeling of coupled chemical reaction and heat and mass transfer processes occurring in porous catalysts. It focuses primarily on steady state catalyst operation which is the preferred industrial practice. Stationary operation may be important for the startup and shutdown of an industrial reactor, or with respect to dynamic process control. However, these effects are not discussed here in great detail because of the limited space available. Instead, the interested reader is referred to the various related monographs and articles available in the literature [6, 31, 46-49]. 

The solution of the above system of partial differential equations (eqs 4-12) yields the concentration and temperature profiles inside the catalyst pellet, and if necessary across the external boundary layer, as a function of time. However, there are only few cases of practical importance where this complete solution is required, as for instance startup and shutdown periods, dynamic process control options such as the so-called Matros concept with flow reversals (for redox processes), or situations where the catalyst is rapidly deactivated. 

Surface Patterning Approaches Based on Surface Instabilities or Dynamic Process Control... 

Figure 16.8 Aspen Dynamics process control diagram with pressure-compensated temperature control.

Representational Provides operator with a model of the system. Good for showing spatial (temporal) relationships between variables. Static (maps). Dynamic (Process control - signalboxes). ... 

Leeetal. [10] (1985) Dynamic/ Process Control 3 - Lump None... 

Ellis et al. [25] (1998) Dynamic/ Process Control 10 - Lump Light gas composition (C1-C4), RON/MON of gasoline products... 

The use of state-selective chemistry has been an experimental tool to elucidate the dynamics of chemical reactions, but its appHcation to practical chemical process control to enhance yields of specific products is ia the developmental stage. 

There are special numerical analysis techniques for solving such differential equations. New issues related to the stabiUty and convergence of a set of differential equations must be addressed. The differential equation models of unsteady-state process dynamics and a number of computer programs model such unsteady-state operations. They are of paramount importance in the design and analysis of process control systems (see Process control). 

F. Greg Shinskey/ B.S.Ch.E./ Consultant (retiredfrom Eoxboro Co.), North Sandwich, NH. (Fundamentals of Process Dynamics and Control, Unit Operations Control)... 

Simulation of Dynamic Models Linear dynamic models are particularly useful for analyzing control-system behavior. The insight gained through linear analysis is invaluable. However, accurate dynamic process models can involve large sets of nonlinear equations. Analytical solution of these models is not possible. Thus, in these cases, one must turn to simulation approaches to study process dynamics and the effect of process control. Equation (8-3) will be used to illustrate the simulation of nonhnear processes. If dcjdi on the left-hand side of Eq. (8-3) is replaced with its finite difference approximation, one gets ... 

The PI controller is by far the most commonly used controller in the process industries. The summation of the deviation with its integral in the above equation can be interpreted in terms of frequency response of the controller (Seborg, Edgar, and Melhchamp, Process Dynamics and Control, Wiley, New York, 1989). The PI controller produces a phase lag between zero and 90 degrees ... 

While the single-loop PID controller is satisfactoiy in many process apphcations, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods hereafter that can be implemented via computer control, namely feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. 

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