Katalin M. Hangos

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.

Although nonlinear control is traditionally an area of interest in process systems engineering which is of great practical importance, 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. This textbook overcomes these barriers. Features: • Mathematical preliminaries for readers from a process engineering background. • Constant reference to the finite-dimensional linear time-invariant continuous case as a basis for extension to the nonlinear situation. • Theories and analytical methods laid out clearly and straightforwardly with exercises to reaffirm the techniques as they are taught. • Emphasis on process knowledge and first-principles-based models in obtaining feasible and effective solutions in particular circumstances from general cases. • Simple examples and case studies.

Process Modelling and Model Analysis 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.