Process control/design/modelling

Several process control design methods, such as the Generic Model Control (GMC) [41], the Globally Linearizing Control (GLC) [37], the Internal Decoupling Control (IDC) [7], the reference system synthesis [8], and the Nonlinear Internal Model Control (NIMC) [29], are based on input-output linearization. 

Often analytical solutions are permitted. Moreover, on today s high-speed computers they impose only minor CPU requirements, and therefore could find considerable use in process control, design and optimization. They also become very attractive models for solution on inexpensive personal computers which could be placed in a remote location or refinery. 

Virtually instantaneous and rugged, PDAs have yet to be fully utilized in process control. Larger models are difficult to use for production as they were designed for static samples. They also require collection of the light diffusely reflected from the solid samples via mirrors and lenses. Most commercial designs are excellent for lab use, but not rugged enough for the production line. 

There are several approaches that can be used to tune PID controllers, including model-based correlations, response specifications, and frequency response (Smith and Corripio 1985 Stephanopoulos 1984). An approach that has received much attention recently is model-based controller design. Model-based control requires a dynamic model of the process the dynamic model can be empirical, such as the popular first-order plus time delay model, or it can be a physical model. The selection of the controller parameters Kc, ti, to) is based on optimizing the dynamic performance of the system while maintaining closed-loop stability. 

Luyben Process Modeling, Simulation, and Control for Chemical Engineers Luyben and Luyben Essentials of Process Control McCabe, Smith, and Harriott Unit Operations of Chemical Engineering Marlin Process Control Designing Processes and Control Systems for Dynamic Performance... 

In this section, the proposed process-control design approach is illustrated with a representative starved emulsion semibatch polymerization and numerical simulations, with a model that emulates and industrial size reactor [11], Moreover, the simulation example corresponds to a scaled-up version of the theoretical-experimental calorimetrie estimation study presented before with a laboratory scale reactor [15]. 

For optimisation of process design and process control, the efficiency and effectiveness of the various methods depend on the process being modeled and the process modeling software that is used. 

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). 

Since the mental model elicited by IMAS explicitly identifies the information needed to identify the causes of disturbances (and to distinguish among alternative causes), it can be used to specify the critical variables that need to be readily available to the process controller at the interface. This information can be used as an input to the design and upgrading of interfaces, particularly when new technology is being installed. 

A chemical process must be designed to operate under a chosen set of conditions, each of which must be controlled within specified limits if the process is to operate rehably and yield a product of specified quality. Accurate, complex, computer-solvable models of chemical processes will incorporate features of the controls that are needed to maintain the desired process conditions. Such models will be able to... 

I/O data-based prediction model can be obtained in one step from collected past input and output data. However, thiCTe stiU exists a problem to be resolved. This prediction model does not require any stochastic observer to calculate the predicted output over one prediction horiajn. This feature can provide simplicity for control designer but in the pr ence of significant process or measurement noise, it can bring about too noise sensitive controller, i.e., file control input is also suppose to oscillate due to the noise of measursd output... 

Sinnar, R., Impact of model uncertainties and nonlinearities on modem controller design In Chemical Process Control, CPC-III. (Morari, M. and McAvoy, T. J., eds.), p. 53 CACHE-Elsevier, 1986. 

Further stability models based on surface area, equilibrium water-content-pressure relationships, and electric double-layer theory can successfully characterize borehole stability problems [1842]. The application of surface area, swelling pressure, and water requirements of solids can be integrated into swelling models and mud process control approaches to improve the design of water-based mud in active or older shales. 

In this chapter, however, our objective is more restricted. We will purposely choose simple cases and make simplifying assumptions such that the results are PID controllers. We will see how the method helps us select controller gains based on process parameters (/. e., the process model). The method provides us with a more rational controller design than the empirical tuning relations. Since the result depends on the process model, this method is what we considered a model-based design. 

A more elegant approach than direct synthesis is internal model control (IMC). The premise of IMC is that in reality, we only have an approximation of the actual process. Even if we have the correct model, we may not have accurate measurements of the process parameters. Thus the imperfect model should be factored as part of the controller design. 

Internal model control Extension of direct synthesis. Controller design includes an internal approximation process function. 

Off-line analysis, controller design, and optimization are now performed in the area of dynamics. The largest dynamic simulation has been about 100,000 differential algebraic equations (DAEs) for analysis of control systems. Simulations formulated with process models having over 10,000 DAEs are considered frequently. Also, detailed training simulators have models with over 10,000 DAEs. On-line model predictive control (MPC) and nonlinear MPC using first-principle models are seeing a number of industrial applications, particularly in polymeric reactions and processes. At this point, systems with over 100 DAEs have been implemented for on-line dynamic optimization and control. 

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

Although, as explained in Chapter 9, many optimization problems can be naturally formulated as mixed-integer programming problems, in this chapter we will consider only steady-state nonlinear programming problems in which the variables are continuous. In some cases it may be feasible to use binary variables (on-off) to include or exclude specific stream flows, alternative flowsheet topography, or different parameters. In the economic evaluation of processes, in design, or in control, usually only a few (5-50) variables are decision, or independent, variables amid a multitude of dependent variables (hundreds or thousands). The number of dependent variables in principle (but not necessarily in practice) is equivalent to the number of independent equality constraints plus the active inequality constraints in a process. The number of independent (decision) variables comprises the remaining set of variables whose values are unknown. Introduction into the model of a specification of the value of a variable, such as T = 400°C, is equivalent to the solution of an independent equation and reduces the total number of variables whose values are unknown by one. 

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