P rocess dynamics and control is an inter-disciplinary area. Three disciplines, process, control and information engineering, are of importance, Process engineering offers the knowledge about an application. Understanding a process is always the basis of modeling and control. A rigorous dynamic process model should be developed to increase the understanding about the operation fundamentals and to test the control hypothesis. Experimental model verification is essential to be aware of all uncertainties and peculiarities of the process. Control engineering offers methods and techniques for (sub-)optimal operation at all hierarchical control and operational levels. For all process operational problems encountered, an appropriate or promising control method should be tested to meet the defined requirements. Software engineering offers the means for implementation. The simulation approach or control solution that is developed should be implemented in an appropriate way and on an appropriate hardware and software platform.
The three disciplines process, control and information technology answer questions such as: for what, why, how, and in which way. Other disciplines are also of interest. Business management sets the production incentives and defines the coupling between the production floor and the office. Human factors study the relation between humans and automation. The contents and format of the supplied information has to meet certain standards to enable the personnel to perform their control and supervisory tasks well. This may conflict with the hierarchical structure of the control functions, which will be based on the partitioning of equipment operations. For the most part, flexibility of the automation infrastructure can solve these conflicts. In addition the degree of automation along the control hierarchy should be chosen with care. Chemical analysis supports quality control. The product quality is one of the most important operation constraints in process operation. In this respect, it should be mentioned that quality measurement is often problematic owing to its time delays and its unreliability. This can be overcome by a quality estimator based on mathematical principles. This book will create a link between specific applications on the one hand and generalized mathematical methods used for the description of a system on the other. The dynamic systems that will be considered are chemical and physiological systems. System behavior will be determined by using analytical mathematical solutions as well as by using simulation, for example Matlab-Simulink. Information flow diagrams will be used to reveal the model structure. These techniques will enable us to investigate the relationship between system variables and their dependencies. This book is organized in three parts. The first part deals with physical modeling, where the model is based on laws of conservation of mass, momentum and energy and additional equations to complete the model description. In this case physical insight into the process is necessary. It is probably the best model description that can be developed, since this type of model imitates the phenomena that are present in reality. However, it can also be a very time-consuming effort. In this first part, numerous unit operations are described and numerous examples have been worked out, to enable the reader to learn by example. The second part of the book deals with empirical modeling. Various empirical modeling techniques are used that are all data based. Some techniques enable the user to develop linear models; with other techniques non-linear models can be developed. It is good practice to always start with the most simple linear model and proceed to more complicated methods only if required. The last part of the book deals with process control. Guidelines are given for developing control schemes for entire plants. The importance of the process model in controller tuning is shown and control of two process units with multiple inputs and multiple outputs is demonstrated. Control becomes increasingly important owing to increased mass and energy integration in process plants. In addition, modern plants are highly flexible for the type of feed they can process. In modern plants it is also common practice to reduce the size of buffer tanks or eliminate certain buffer tanks altogether. Much emphasis is therefore placed on well-designed and properly operating control systems.
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