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Artificial Intelligence in Process Engineering
- 1st Edition - November 13, 2012
- Editor: Michael Mavrovouniotis
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 4 3 1 5 1 2 - 9
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 1 5 3 1 4 - 0
Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains… Read more
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Request a sales quoteArtificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers.
Contributors
Preface
1. Qualitative Modeling of Chemical Reaction Systems
Abstract
1. Introduction
2. The QSIM Algorithm for Qualitative Simulation
3. Building Qualitative Models of Reaction Systems
4. Partial Quantitative Knowledge in Qualitative Models
5. Discussion
Acknowledgment
Appendix: Curvature Constraint Derivations
References
2· Use of Qualitative Models in Discrete Event Simulation to Analyze Malfunctions in Processing Systems
Abstract
1. Introduction
2. The Problem
3. Modeling and Simulation Background
4. CONFIG Implementation and Examples
5. Conclusions and Future Work
Acknowledgment
References
3. An Expert System for Diagnosis of a Sequential, PLC-Controlled Operation
1. Introduction
2. Programmable Logic Controllers
3. The Dead Operating State Diagnostic Scenario
4. Diagnosis
5. Diagnostic Methods
6. Expert System Development
7. General Aspects of the Expert System: "WRAPITUP"
8. Discussion and Summary
Acknowledgments
References
4. Fault Detection and Diagnosis Using Artificial Neural Networks
Abstract
1. Introduction
2. Characteristics of Artificial Neural Networks
3. ZNL Architecture
4. Fault Detection and Diagnosis Examples
5. Conclusion
References
5. A Modular Approach to Multiple Faults Diagnosis
Abstract
1. Introduction
2. Shallow Versus Deep Knowledge
3. The Model-Based Approach
4. Multiple Faults Diagnosis
5. Other Approaches
6. Divide and Conquer (MFD2)
7. Conclusions
References
6. Modeling Real-World Processes: Deep and Shallow Knowledge Integrated with Approximate Reasoning in a Diagnostic Expert System
1. Introduction
2. Overview
3. The Scenario
4. A Real-World Domain: The Power Plant
5. The Plant Model
6. The Diagnostic Expert System
7. Conclusions and Future Work
Acknowledgments
References
7. XIMKON—An Expert Simulation and Control Program
Abstract
1. Introduction
2. Control System Design Process
3. XIMKON
4. Expert Process Modeling
5. Expert Controller Design
6. Conclusion
Acknowledgments
References
8. Exothermic Batch Chemical Reactor Automation Via Expert System
Abstract
1. Introduction
2. Review
3. The Generalized Batch Reactor Control Problem
4. An Expert System Approach
5. Associated Conventional Control Strategies
6. Testing by Simulation and Follow-Up of Control Logic
7. Future Goals and Directions
Acknowledgments
Nomenclature
References
9. Design of Protein Purification Processes by Heuristic Search
Abstract
1. Introduction
2. Approaches to Design
3. BioSep Designer
4. Conclusion
References
10. An Adaptive Heuristic-Based System for Synthesis of Complex Separation Sequences
Abstract
1. Introduction
2. Problem Specifications
3. Knowledge Representation Strategy
4. Reasoning Strategy
5. Adaptation Mechanism
6. Example
7. Discussion and Conclusion
Acknowledgment
Appendix A: Quantitative Expression for the Term "Vary Widely"
Appendix B: Fuzzy Membership Function Representing the Antecedent of Rule 2.9
Appendix C: Pattern Recognition for Stream Division or Separation
References
Other Suggested Readings
Index
- No. of pages: 382
- Language: English
- Edition: 1
- Published: November 13, 2012
- Imprint: Academic Press
- Paperback ISBN: 9780124315129
- eBook ISBN: 9780323153140
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