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Data Processing and Reconciliation for Chemical Process Operations, Volume 2 (Process Systems Engineering) [Hardcover]

José A. Romagnoli (Author), Mabel Cristina Sanchez (Author)


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Book Description

October 25, 1999 0125944608 978-0125944601 1st
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

Editorial Reviews

From the Back Cover

Routinely collected sensor data provide the first level of information for use in ensuring the optimal and safe operation of manufacturing plants. However, these numerical observations are seldom consistent, as they may be corrupted by measurement errors as well as by random and gross errors regardless of how well the instrumentation system is designed and operated. Erroneous data may compromise plant safety and lead to off-specification production.

Although the reconciliation of process data has been studied for more than 20 years, Data Processing and Reconciliation for Chemical Process Operations provides the first unified treatment of this important subject. An introduction to the modern literature is presented together with in-depth coverage of the relevant theory of data reconciliation. The theory is illuminated by numerous examples from representative chemical processes, including detailed industrial case studies.

Product Details

  • Hardcover: 270 pages
  • Publisher: Academic Press; 1st edition (October 25, 1999)
  • Language: English
  • ISBN-10: 0125944608
  • ISBN-13: 978-0125944601
  • Product Dimensions: 10.3 x 7.1 x 0.8 inches
  • Shipping Weight: 1.6 pounds
  • Amazon Best Sellers Rank: #2,596,287 in Books (See Top 100 in Books)

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Inside This Book (learn more)
First Sentence:
The highly competitive nature of the world market, the increasing importance of product quality, and the growing number of environmental and safety regulations have increased and need to introduce fast and low-cost changes in chemical processes to improve their performance. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
data reconciliation problem, output set assignment, indeterminable variables, data reconciliation procedure, dynamic data reconciliation, process sampling data, redundancy classification, joint parameter estimation, suspect measurements, gross error detection, reconciled data, total flowrates, gross error identification, reconciled values, general estimation problem, unmeasured variables, process flow rates, process topology, orthogonal factorizations, reconciliation techniques, bilinear systems, nonlinear programming techniques, least squares objective, normalization equations, process flowsheet
Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Monte Carlo, Greek Symbols, Englewood Cliffs, Simulation Sciences Inc, Ellis Horwood, Flow Difference, Process Plant Performance
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