Data Processing and Reconciliation for Chemical Process Operations - 1st Edition - ISBN: 9780125944601, 9780080530277

Data Processing and Reconciliation for Chemical Process Operations, Volume 2

1st Edition

Authors: José Romagnoli Mabel Sanchez
eBook ISBN: 9780080530277
Imprint: Academic Press
Published Date: 11th October 1999
Page Count: 270
Sales tax will be calculated at check-out Price includes VAT/GST
Price includes VAT/GST
× DRM-Free

Easy - Download and start reading immediately. There’s no activation process to access eBooks; all eBooks are fully searchable, and enabled for copying, pasting, and printing.

Flexible - Read on multiple operating systems and devices. Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle.

Open - Buy once, receive and download all available eBook formats, including PDF, EPUB, and Mobi (for Kindle).

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.

Table of Contents

General Introduction. Reliable and Complete Knowledge. Some Issues Associated with a General Data Reconciliation Problem. About This Book. References of Chapter 1. Estimability and Redundancy Within the Framework of the General Estimation Theory. Introduction. Basic Concepts and Definitions. Decomposition of the General Estimation Problem. Structural Analysis. Conclusions. Notation. References of Chapter 2. Appendix 2 - A. Classification of the Process Variables for Chemical Plants. Introduction. Modeling Aspects. Classification of Process Variables. Analysis of the Process Topology. Different Approaches for Solving the Classification Problem. Use of Output Set Assignments for Variable Classification. The Solution of Special Problems. A Complete Classification Example. Formulation of a Reduced Reconciliation Problem. Conclusions. Notation. References of Chapter 3. Appendix 3 - A. Appendix 3 - B. Decomposition Using Orthogonal Transformations. Introduction. Linear Mass Balances. Bilinear Multicomponent and Energy Balances. Conclusions. Notation. References of Chapter 4. Steady State Data Reconciliation. Introduction. Problem Formulation. Linear Data Reconciliation. Non-Linear Data Reconciliation. Conclusions. Notation. References of Chapter 5. Appendix 5 - A. Sequential Processing of the Information. Introduction. Sequential Processing of the Constraints. Sequential Processing of the Measurements. Alternative Formulation from Estimation Theory. Conclusions. Notation. References of Chapter 6. Appendix 6 - A. Treatment of Gross Errors. Introduction. Gross Error detection. Identification of the Measurements with Gross Error. Estimation of the Magnitude of Bias and Leaks. A Recursive Scheme for Gross Error Identification and Estimation. Conclusions. Notation. References of Chapter 7. Appendix 7 - A. Appendix 7 - B. Rectification of Process Measur


Computer techniques have made online measurements available at every sampling period in a chemical process. However, measurement errors are introduced that require suitable techniques for data reconciliation and improvements in accuracy. Reconciliation of process data and reliable monitoring are essential to decisions about possible system modifications (optimization and control procedures), analysis of equipment performance, design of the monitoring system itself, and general management planning. While the reconciliation of the process data has been studied for more than 20 years, there is no single source providing a unified approach to the area with instructions on implementation. Data Processing and Reconciliation for Chemical Process Operations is that source. Competitiveness on the world market as well as increasingly stringent environmental and product safety regulations have increased the need for the chemical industry to introduce such fast and low cost improvements in process operations.

Key Features

  • Introduces the first unified approach to this important field
  • Bridges theory and practice through numerous worked examples and industrial case studies
  • Provides a highly readable account of all aspects of data classification and reconciliation
  • Presents the reader with material, problems, and directions for further study


Industrial practitioners and academic researchers in chemical engineering (particularly in process monitoring and control), as well as advanced (senior) undergraduate and graduate students.


No. of pages:
© Academic Press 1999
Academic Press
eBook ISBN:

Ratings and Reviews

About the Authors

José Romagnoli Author

Affiliations and Expertise

University of Sydney, Australia

Mabel Sanchez Author

Affiliations and Expertise

Planta Piloto de Ingeniería Química, Bahia Blanca, Argentina