Ian T. Cameron – författare

Process Systems Risk Management provides complete coverage of risk management concepts and applications for safe design and operation of industrial and other process facilities. The whole life cycle of the process or product is taken into account, from its conception to decommissioning.The breadth of human factors in risk management is also treated, ranging from personnel and public safety to environmental impact and business interruption. This unique approach to process risk management is firmly grounded in systems engineering. Numerous examples are used to illustrate important concepts –drawn from almost 40 years authors'' experience in risk analysis, assessment and management, with applications in both on- and off-shore operations.This book is essential reading on the relevant techniques to tackle risk management activities for small-, medium- and large-scale operations in the process industries. It is aimed at informing a wide audience of industrial risk management practitioners, including plant managers, engineers, health professionals, town planners, and administrators of regulatory agencies.- A computational perspective on the risk management of chemical processes- A multifaceted approach that includes the technical, social, human and management factors- Includes numerous examples and illustrations from real life incidents

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.- 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

This book covers the area of product and process modelling via a case study approach. It addresses a wide range of modelling applications with emphasis on modelling methodology and the subsequent in-depth analysis of mathematical models to gain insight via structural aspects of the models. These approaches are put into the context of life cycle modelling, where multiscale and multiform modelling is increasingly prevalent in the 21st century. The book commences with a discussion of modern product and process modelling theory and practice followed by a series of case studies drawn from a variety of process industries.The book builds on the extensive modelling experience of the authors, who have developed models for both research and industrial purposes. It complements existing books by the authors in the modelling area. Those areas include the traditional petroleum and petrochemical industries to biotechnology applications, food, polymer and human health application areas. The book highlights to important nature of modern product and process modelling in the decision making processes across the life cycle. As such it provides an important resource for students, researchers and industrial practitioners.Ian Cameron is Professor in Chemical Engineering at the University of Queensland with teaching, research, and consulting activities in process systems engineering. He has a particular interest in process modelling, dynamic simulation, and the application of functional systems perspectives to risk management, having extensive industrial experience in these areas. He continues to work closely with industry and government on systems approaches to process and risk management issues. He received his BE from the University of New South Wales (Australia) and his PhD from imperial College London. He is a Fellow of IChemE.Rafiqul Gani is a Professor of Systems Design at the Department of Chemical and Biochemical Engineering, Technical University of Denmark, and the director of the Computer Aided Product-Process Engineering Center (CAPEC). His research interests include the development of computer-aided methods and tools for modelling, property estimation and process-product synthesis and design. He received his BSc from Bangladesh University of Engineering and Technology in 1975, and his MSc in 1976 and PhD in 1980 from Imperial College London. He is the editor-in-chief of Computers and Chemical Engineering journal and Fellow of IChemE as well as AIChE.- Product and process modelling; a wide range of case studies are covered- Structural analysis of model systems; insights into structure and solvability- Analysis of future developments; potential directions and significant research and development problems to be addressed

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.

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