Fisher and R.
Bro and S. Every chapter includes a set of references for further reading which include control and process control engineering text book, classical journal papers and more recent journal papers detailing some of the specific techniques selected for presentation. Chapter 1 gives an introduction of basic control concepts. Successful control structures typically occurring in industrial practice are briefly discussed. An introduction into modelling and identification fundamentals is given in Chapter 3.
A recommended identification procedure is presented first for impulse and step response models. Chapter 4 contains a number of examples to illustrate the identification techniques introduced in Chapter 3. Essentials of the design of experiments and of model conversion are briefly addressed in the second part of the chapter. Chapter 5 reviews dynamic matrix control DMC as an example of a linear multivariable predictive control scheme after static and dynamic relative gain arrays have been introduced for the analysis of multivariable interaction.
A discussion of output weighting, of constraint formulation and handling as well as of commercial multivariable control software complements the material of the chapter. The design of controllers using the internal model principle is the subject of Chapter 7. Chapter 8 is devoted to nonlinear multivariable control techniques. The basic ideas of nonlinear model predictive control, of nonlinear quadratic DMC and of generic model control GMC and some variants are outlined.
A comprehensive case study, the control of a continuous stirred tank reactor, is employed to illustrate the implementation and the properties of GMC and its variants. A brief account of the numerical techniques for the solution of different kinds of linear and nonlinear, constrained and unconstrained optimization problems is given.
Chapter 10 complements the theoretical exposition of the previous chapter with a wide range of examples. The formulation of typical optimization problems and their solution with AMPL is presented. A seawater desalination plant and a chemical reactor are used as case studies.
The installation and exemplary use of the accompanying MCPC control software is described in Appendix I, while Appendix II contains another comprehensive case study comparing different multivariable control techniques for controlling a specific polymerization reactor. The control techniques are largely presented from an implementation perspective, almost in the style of a cook book where recipes for different occasions are offered one after the other.
There is neither a thorough discussion of the theoretical background of the control techniques and concepts selected for presentation, nor is there a discussion of practical experience during the application of the techniques in a real plant. The presentation concentrates on the nature of selected advanced process control concepts and their implementation using state of the art software tools. Such a style is definitely appreciated by the practitioner and often also by the student since it focuses on the essentials of applying advanced control in industrial practice rather than understanding the theoretical concepts and foundations.
Indeed, it is the other way around with this book. Hence, the book definitely does enrich the textbook literature on process control.
It is suitable for a brief overview and introduction into selected advanced control technologies, the choice of which at least in part reflects the preferences of the authors. The target audience is indeed the industrial practitioner or the chemical engineering student who wants to build up working knowledge in advanced process control quickly.
As such it should be considered complementary to established textbooks emphasizing process control theory and practice such as References 1 - 5. Volume 16 , Issue 2. The full text of this article hosted at iucr.
If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Book Review Free Access. The common objective is to manage complex interactions within a process in such a way to reduce process variability and allow the plant to run closer to the operating constraints.
This results in higher energy efficiency and product quality.
One of the important advanced control techniques is model predictive control MPC. A simplified block diagram of a model predictive control system is shown in Figure 2.
MPC is used to optimize hybrid systems with large multivariable constrained control issues. MPC is implemented to reduce variation in process variables in industrial applications, which in turn leads to an increased throughput and higher profit.
This text and reference offers an application-oriented approach to process control . It systematically explains process identification, control and optimization, the. One of the disciplines that will help the process engineer to achieve this is process control. There are many industrial automation systems to day that will offer.
MPC is a multivariable strategy that encompasses constraints, handling of actuators, states, process outputs, and other variables. It brings a structured approach to solutions where the main aim is to minimize a performance criterion in the future.
The criterion would possibly be subject to constraints on the manipulated inputs and outputs, and the future behavior is computed according to a model of the plant. MPC utilizes an internal dynamic model of the process, a history of past control moves, and an optimization cost function over the receding prediction horizon to calculate the optimum control needed.
The optimum operating conditions for a plant are determined as part of the process design. However, during plant operations, due to changes in equipment availability, economic conditions, and process disturbances, for example, the optimum conditions change frequently over the course of time.
Hence, the optimum operating conditions need to be re-calculated on a regular basis. This control activity is defined as real-time optimization RTO level 4 in the hierarchy discussed earlier. A suitable problem statement needs to be formulated and solved once a process has been selected. For RTO, optimization of set points requires two models-the economic model and the operating model.
The economic model consists of an objective function that needs to be maximized or minimized. This includes costs and product values. The operating model is a steady-state process model and contains all process variable constraints. The input and output variables for the process that are identified in this step are employed in the process model and the objective function.
The next step is to select an objective function based on operating profit, product qualities and quantities, as well as plant configuration. The third step is to formulate steady-state process models and identify the operating limits for the process variable. To ensure compatibility with the most effective solution techniques, it is important to simplify the model as well as the objective function at this stage.
The fourth step involves calculating the optimum set points after choosing an optimization technique. In the last step the most sensitive parameters in the optimization systems are identified through varying model and cost parameters. In the example shown in Figure 4, implementing an advanced control strategy project can be carried out in four phases. The decision to implement any advanced control strategy is based on a cost-benefit analysis.
This phase is the most important one in implementing a control strategy for a plant process.
Mistakes lead to incorrect cost estimates, which will have negative consequences on the project. Modeling process dynamics and configuration of the real-time database and the controller are required before implementation of advanced control algorithms. First, plants tests are carried out to obtain a model for process dynamics.
After the completion of plant tests, a real-time database is designed. The next step is to define the communication with the process, determining and establishing the protocol between a workstation and the DCS. The process model is derived from process identification.
For the controller configuration, the manipulated, controlled variables and constraints are defined.