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Process Modelling and Model Analysis
 
 

Process Modelling and Model Analysis, 1st Edition

 
Process Modelling and Model Analysis, 1st Edition,Ian Cameron,Katalin Hangos,ISBN9780121569310
 
 
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9780121569310

9780080514925

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Key Features

* 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

Description

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.

Readership

Undergraduate and graduate students in chemical, process, environmental, or control engineering. Professionals engineers in industry and government.

Ian Cameron

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.

Affiliations and Expertise

School of Chemical Engineering, University of Queensland, Brisbane, Australia

View additional works by Ian T. Cameron

Katalin Hangos

Katalin Hangos is currently a research professor at the Computer and Automation Research Institute, Hungary. She is one of the few woman professors in process systems engineering with a strong background in systems and control theory and computer science. Dr. Hangos's main interest is dynamic process modeling for control and diagnosis purposes. She is co-author of more than 100 papers on various aspects of modeling and its control applications including nonlinear and stochastic process system models, Petri nets, qualitative, and graph-theoretical models.

Affiliations and Expertise

Systems and Control Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, Hungary

Process Modelling and Model Analysis, 1st Edition

Introduction


FUNDAMENTAL PRINCIPLES AND PROCESS MODEL DEVELOPMENT

The Role of Models in Process Systems Engineering;
A Systematic Approach to Model Building;
Conservation Principles;
Constitutive Relations;
Dynamic Models - Lumped Parameter Systems; Solution Strategies for Lumped Parameter Models; Dynamic Models - Distributed Parameter Systems
Solution Strategies for Distributed Parameter Systems; Process Model Hierarchies



ADVANCED PROCESS MODELING AND MODEL ANALYSIS

Basic Tools for Process Model Analysis; Data Acquisition and Analysis; Statistical Model Calibration and Validation; Analysis of Dynamic Process Models; Process Modeling for Control and Diagnostic Purposes; Modeling Discrete Event Systems; Modeling Hybrid Systems; Modeling Applications in Process Systems; Computer Aided Process Modeling; Empirical Model Building; Appendix: Basic Mathematic Tools; Bibliography;
Index

 
 
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