This book describes the use of models in process engineering. Process engineering is all about manufacturing--of just about anything! To manage processing and manufacturing systematically, the engineer has to bring together many different techniques and analyses of the interaction between various aspects of the process. For example, process engineers would apply models to perform feasibility analyses of novel process designs, assess environmental impact, and detect potential hazards or accidents.
To manage complex systems and enable process design, the behavior of systems is reduced to simple mathematical forms. This book provides a systematic approach to the mathematical development of process models and explains how to analyze those models. Additionally, there is a comprehensive bibliography for further reading, a question and answer section, and an accompanying Web site developed by the authors with additional data and exercises.
* Introduces a structured modeling methodology emphasizing the importance of the modeling goal and including key steps such as model verification, calibration, and validation.
* Focuses on novel and advanced modeling techniques such as discrete, hybrid, hierarchical, and empirical modeling
* Illustrates the notions, tools, and techniques of process modeling with examples and advances applications

Analysis and Control of Polynomial Dynamic Models with Biological Applications synthesizes three mathematical background areas (graphs, matrices and optimization) to solve problems in the biological sciences (in particular, dynamic analysis and controller design of QP and polynomial systems arising from predator-prey and biochemical models). The book puts a significant emphasis on applications, focusing on quasi-polynomial (QP, or generalized Lotka-Volterra) and kinetic systems (also called biochemical reaction networks or simply CRNs) since they are universal descriptors for smooth nonlinear systems and can represent all important dynamical phenomena that are present in biological (and also in general) dynamical systems.

Almost all process systems are nonlinear in nature. Nonlinear control is traditionally an area of interest in process systems engineering which is of great practical importance. These facts notwithstanding, many process engineers have difficulty with the paradigms and results of modern nonlinear control theory because they lack the mathematical background usually associated with such methods or because of their computational difficulty and small-scale applicability in the general case. Analysis and Control of Nonlinear Process Systems overcomes these barriers.

Features:

a [ The necessary mathematical preliminaries for readers from a process engineering background.

a [ Constant reference to the widely-known finite-dimensional linear time-invariant continuous case as a basis for extension to the nonlinear situation.

a [ The most promising theories and analytical methods for nonlinear process control laid out clearly and straightforwardly with exercises to reaffirm the techniques as they are taught.

a [ Emphasis on the importance of process knowledge and first-principles-based models in obtaining feasible and effective solutions in particular circumstances from general cases.

a [ Illustration of applications with simple examples and case studies.

Analysis and Control of Nonlinear Process Systems will interest graduate process engineers wishing to study advanced control methods either with a view to further research or application in industry as well as to academics seeking to move process control courses into more complicated but up-to-date territory. It will also be a great assistance to those in their senior undergraduate years who will form the next generation ofindustrial process engineers and need unfussy access to the most modern nonlinear control ideas.