Detailed mathematical models are increasingly being used by companies to gain competitive advantage through such applications as model-based process design, control and optimization. Thus, building various types of high quality models for processing systems has become a key activity in Process Engineering. This activity involves the use of several methods and techniques including model solution techniques, nonlinear systems identification, model verification and validation, and optimal design of experiments just to name a few. In turn, several issues and open-ended problems arise within these methods, including, for instance, use of higher-order information in establishing parameter estimates, establishing metrics for model credibility, and extending experiment design to the dynamic situation. The material covered in this book is aimed at allowing easier development and full use of detailed and high fidelity models. Potential applications of these techniques in all engineering disciplines are abundant, including applications in chemical kinetics and reaction mechanism elucidation, polymer reaction engineering, and physical properties estimation. On the academic side, the book will serve to generate research ideas. - Contains wide coverage of statistical methods applied to process modelling - Serves as a recent compilation of dynamic model building tools - Presents several examples of applying advanced statistical and modelling methods to real process systems problems

'Bottom line: For a holistic view of chemical engineering design, this book provides as much, if not more, than any other book available on the topic.' Extract from Chemical Engineering Resources review. Chemical Engineering Design is a complete course text for students of chemical engineering. Written for the Senior Design Course, and also suitable for introduction to chemical engineering courses, it covers the basics of unit operations and the latest aspects of process design, equipment selection, plant and operating economics, safety and loss prevention. It is a textbook that students will want to keep through their undergraduate education and on into their professional lives.

Chemical Engineering Design: SI Edition is one of the best-known and most widely used textbooks available for students of chemical engineering. The enduring hallmarks of this classic book are its scope and practical emphasis which make it particularly popular with instructors and students who appreciate its relevance and clarity. This new edition provides coverage of the latest aspects of process design, operations, safety, loss prevention, equipment selection, and much more, including updates on plant and equipment costs, regulations and technical standards. - Includes new content covering food, pharmaceutical and biological processes and the unit operations commonly used - Features expanded coverage on the design of reactors - Provides updates on plant and equipment costs, regulations and technical standards - Integrates coverage with Honeywell's UniSim® software for process design and simulation - Includes online access to Engineering's Cleopatra cost estimating software

Modern science is, to a large extent, a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play here? These questions have kept philosophers of science busy for many years, and much work has been done to identify modeling as the central activity of theoretical science. At the same time, these questions have been addressed by methodologically-minded scientists, albeit from a different point of view. While philosophers typically have an eye on general aspects of scientific modeling, scientists typically take their own science as the starting point and are often more concerned with specific methodological problems. There is, however, also much common ground in middle, where philosophers and scientists can engage in a productive dialogue, as the present volume demonstrates. To do so, the editors of this volume have invited eight leading scientists from cosmology, climate science, social science, chemical engeneering and neuroscience to reflect upon their modeling work, and eight philosophers of science to provide a commentary.

This concise and clear introduction to the topic requires only basic knowledge of calculus and linear algebra - all other concepts and ideas are developed in the course of the book. Lucidly written so as to appeal to undergraduates and practitioners alike, it enables readers to set up simple mathematical models on their own and to interpret their results and those of others critically. To achieve this, many examples have been chosen from various fields, such as biology, ecology, economics, medicine, agricultural, chemical, electrical, mechanical and process engineering, which are subsequently discussed in detail. Based on the author`s modeling and simulation experience in science and engineering and as a consultant, the book answers such basic questions as: What is a mathematical model? What types of models do exist? Which model is appropriate for a particular problem? What are simulation, parameter estimation, and validation? The book relies exclusively upon open-source software which is available to everybody free of charge. The entire book software - including 3D CFD and structural mechanics simulation software - can be used based on a free CAELinux-Live-DVD that is available in the Internet (works on most machines and operating systems).

This book presents the fundamental theories, methodologies and case studies of marine ecosystem modeling with a special focus on marine ecological dynamics that could provide scientists and researchers with a stabile and reliabile technical framework to study marine life and their developments.This book also clarifies the research objective and model classification methods of marine ecosystem dynamics research and analyzes the key marine ecological processes that affect modeling. The technical framework for improving the performance of modeling is also proposed, and the latest progress in research, as well as existing difficulties and challenges in end-to-end dynamics models are reviewed and analyzed. A dimensionality reduction theorem is established and derived for analyzing the stability of the solutions of a class of self-conserving marine ecosystem dynamic models. Also included in this work are several new types of marine ecosystem dynamics models constructed by modern computing methods — including artificial neural networks, cellular automata, and statistical dynamics — and case studies.This book is a suitable reference for professional and technical personnel, managers and graduate students specializing in the evolution mechanism, simulation, predication and regulation of marine ecosystems.

Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.