Pattern Recognition in Industry (Hardcover)

Description:

Two wave fronts are upon us today: we are being bombarded by an enormous amount of data, and we are confronted by continually increasing technical and business advances.


Ideally, the endless stream of data should be one of our major assets. However, this potential asset often tends to overwhelm rather than enrich. Competitive advantage depends on our ability to extract and utilize nuggets of valuable knowledge and insight from this data deluge. The challenges that need to be overcome include the under-utilization of available data due to competing priorities, and the separate and somewhat disparate existing data systems that have difficulty interacting with each other.


Conventional approaches to formulating models are becoming progressively more expensive in time and effort. To impart a competitive edge, engineering science in the 21st century needs to augment traditional modelling processes by auto-classifying and self-organizing data; developing models directly from operating experience, and then optimizing the results to provide effective strategies and operating decisions. This approach has wide applicability; in areas ranging from manufacturing processes, product performance and scientific research, to financial and business fields.


This monograph explores pattern recognition technology, and its concomitant role in extracting useful knowledge to build technical and business models directly from data, and in optimizing the results derived from these models within the context of delivering competitive industrial advantage. It is not intended to serve as a comprehensive reference source on the subject. Rather, it is based on first-hand experience in the practice of this technology: its development and deployment for profitable application in industry.


The technical topics covered in the monograph will focus on the triad of technological areas that constitute the contemporary workhorses of successful industrial application of pattern recognition. These are: systems for self-organising data; data-driven modelling; and genetic algorithms as robust optimizers.

 

 

Table of Contents:

Preface

Acknowledgments

About the Author

Part I Philosophy

CHAPTER 1: INTRODUCTION

CHAPTER 2: PATTERNS WITHIN DATA

CHAPTER 3: ADAPTING BIOLOGICAL PRINCIPLES FOR DEPLOYMENT IN COMPUTATIONAL SCIENCE

CHAPTER 4: ISSUES IN PREDICTIVE EMPIRICAL MODELING

Part II Technology

CHAPTER 5: SUPERVISED LEARNING—CORRELATIVE NEURAL NETS

CHAPTER 6: UNSUPERVISED LEARNING: AUTO-CLUSTERING AND SELF-ORGANIZING DATA

CHAPTER 7: CUSTOMIZING FOR INDUSTRIAL STRENGTH APPLICATIONS

CHAPTER 8: CHARACTERIZING AND CLASSIFYING TEXTUAL MATERIAL

CHAPTER 9: PATTERN RECOGNITION IN TIME SERIES ANALYSIS

CHAPTER 10: GENETIC ALGORITHMS

Part III Case Studies

CHAPTER 11: HARNESSING THE TECHNOLOGY FOR PROFITABILITY

CHAPTER 12: REACTOR MODELING THROUGH IN SITU ADAPTIVE LEARNING

CHAPTER 13: PREDICTING PLANT STACK EMISSIONS TO MEET ENVIRONMENTAL LIMITS

CHAPTER 14: PREDICTING FOULING/COKING IN FIRED HEATERS

CHAPTER 15: PREDICTING OPERATIONAL CREDITS

CHAPTER 16: PILOT PLANT SCALE-UP BY INTERPRETING TRACER DIAGNOSTICS

CHAPTER 17: PREDICTING DISTILLATION TOWER TEMPERATURES: MINING DATA FOR CAPTURING DISTINCT OPERATIONAL VARIABILITY

CHAPTER 18: ENABLING NEW PROCESS DESIGN BASED ON LABORATORY DATA

CHAPTER 19: FORECASTING PRICE CHANGES OF A COMPOSITE BASKET OF COMMODITIES

CHAPTER 20: CORPORATE DEMOGRAPHIC TREND ANALYSIS

EPILOGUE

Appendices

APPENDIX A1: THERMODYNAMICS AND INFORMATION THEORY

APPENDIX A2: MODELING