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Data Reconciliation And Gross Error Detection

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Data Reconciliation And Gross Error Detection

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Author : Shankar Narasimhan
language : en
Publisher: Elsevier
Release Date : 1999-11-29

Data Reconciliation And Gross Error Detection written by Shankar Narasimhan and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-11-29 with Business & Economics categories.

This book provides a systematic and comprehensive treatment of the variety of methods available for applying data reconciliation techniques. Data filtering, data compression and the impact of measurement selection on data reconciliation are also exhaustively explained.Data errors can cause big problems in any process plant or refinery. Process measurements can be correupted by power supply flucutations, network transmission and signla conversion noise, analog input filtering, changes in ambient conditions, instrument malfunctioning, miscalibration, and the wear and corrosion of sensors, among other factors. Here's a book that helps you detect, analyze, solve, and avoid the data acquisition problems that can rob plants of peak performance. This indispensable volume provides crucial insights into data reconciliation and gorss error detection techniques that are essential fro optimal process control and information systems. This book is an invaluable tool for engineers and managers faced with the selection and implementation of data reconciliation software, or for those developing such software. For industrial personnel and students, Data Reconciliation and Gross Error Detection is the ultimate reference.

Data Reconciliation Gross Error Detection Recurso Electr Nico

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Author : Shankar Narasimhan
language : en
Publisher:
Release Date : 1999

Data Reconciliation Gross Error Detection Recurso Electr Nico written by Shankar Narasimhan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Automatic data collection systems categories.

: Introduction. Measurement Errors and Error Reduction Techniques. Steady State Data Reconciliation for Bilinear Systems. Nonlinear Steady State Data Reconciliation. Data Reconciliation in Dynamic Systems. Introduction to Gross Error Detection. Multiple Gross Error Identification Strategies for Steady State Processes. Gross Error Detection in Dynamic Processes. Design of Sensor Networks. Industrial Applications of Data Reconciliation and Gross Error Detection Technologies. Appendix A: Basic concepts of linear algebra. Appendix B: Basic concepts of Graph Theory. Appendix C: Statistical Hypotheses Testing.

Data Processing And Reconciliation For Chemical Process Operations

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Author : José A. Romagnoli
language : en
Publisher: Elsevier
Release Date : 1999-10-25

Data Processing And Reconciliation For Chemical Process Operations written by José A. Romagnoli and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-10-25 with Technology & Engineering categories.

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. - 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

Simultaneous Gross Error Detection And Data Reconciliation Using Gaussian Mixture Distribution

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Author : Hashem Alighardashi
language : en
Publisher:
Release Date : 2017

Simultaneous Gross Error Detection And Data Reconciliation Using Gaussian Mixture Distribution written by Hashem Alighardashi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Errors, Scientific categories.

The intensive competitive nature of the world market, the growing significance of quality products, and the increasing importance and the number of safety and environmental issues and regulations, respectively, have increased the need for fast and low-cost changes in chemical processes to enhance their performance. Any possible changes and modifications in a system in order to control, optimize, evaluate the behavior of the process, or achieve the maximal performance of the system require clear understanding and knowledge of its actual state. This information is obtained by processing a data set - collecting it, ameliorating its accuracy, and storing/using it for further analysis. It should be emphasized that in today's highly competitive world market, increasing the accuracy of measurements by resolving even small errors can result in substantial improvements in plant efficiency and economy. Industrial process measurements play a significant role in online optimization, process monitoring, identification, and control. These measurements are used to make decisions which potentially influence product quality, plant safety, and profitability. Nonetheless, they are inherently contaminated by errors, which may be random and/or systematic/gross errors, due to sensor accuracy, improper instrumentation, poor calibration, process leak, and so on. The objective of data reconciliation and gross error detection is the estimation of the true states and the detection of any faults in the instruments which could seriously degrade the performance of the system. Data reconciliation techniques deal with the problem of improving the accuracy of raw process measurements and their application allows optimal adjustment of measurement values to satisfy material and energy constraints. These methods also make possible estimation of the unmeasured variables. However, data reconciliation approaches do not always provide valid estimates of the actual states, and the presence of gross errors in the measurements significantly affect the accuracy levels that can be accomplished using reconciliation. Therefore, the main focus of this work is to develop a framework to obtain the accurate estimates of reconciled values while reducing the impact of gross errors. In reality, operating conditions under which a process works change with different circumstances. Therefore, it is vital to develop a model that is capable of identifying and switching between operating regions. To this end, a method is proposed for simultaneous gross error detection and rectification of a data set which contains different operating regions. First, the data set is divided into several clusters based on the number of operating regions. Then, the same operation, i.e., data rectification is performed on each operating region. It must be noted that all of the proposed approaches in this thesis do not require to preset the parameters of the error distribution model, rather they are determined as part of the solution. They are also applicable to problems with both linear and nonlinear constraints, in addition to the ability to determine the magnitude of gross errors. Furthermore, these methods/approaches detect partial gross errors, so it is not required to assume that gross errors exist in the entire data set. Finally, the performance of the proposed methods is verified through various simulation studies and realistic examples.

Smart Process Plants Software And Hardware Solutions For Accurate Data And Profitable Operations

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Author : Miguel J. Bagajewicz
language : en
Publisher: McGraw Hill Professional
Release Date : 2009-09-22

Smart Process Plants Software And Hardware Solutions For Accurate Data And Profitable Operations written by Miguel J. Bagajewicz and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-22 with Technology & Engineering categories.

A Detailed Guide to the New Generation of Smart Process Plants Maximize plant profitability by minimizing operating costs. Smart Process Plants addresses measurements and the data they generate, error-free process variable estimation, control, fault detection, instrumentation upgrade, and maintenance optimization, and then connects these activities to plant economics. Methods for calculating the value of the information produced are included. The book discusses optimal instrumentation type, quality, precision, and location along with preventive maintenance techniques. Practical examples throughout the book demonstrate how to perform essential calculations. Smart Process Plants covers: Measurement instrument performance and measurement errors Variable classification and canonical representation Linear, nonlinear, and dynamic data reconciliation Gross error detection, equivalency, size elimination, and estimation Accuracy of estimators Value of accuracy, control strategies, parametric fault identification, and instrumentation upgrade Maintenance optimization

Data Reconciliation And Gross Error Detection

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Author : Shankar Narasimhan
language : en
Publisher: Gulf Professional Publishing
Release Date : 2000

Data Reconciliation And Gross Error Detection written by Shankar Narasimhan and has been published by Gulf Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Business & Economics categories.

Process Plant Instrumentation

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Author : Miguel J. Bagajewicz
language : en
Publisher: CRC Press
Release Date : 2000-11-27

Process Plant Instrumentation written by Miguel J. Bagajewicz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-11-27 with Science categories.

This is the first in-depth presentation in book form of current analytical methods for optimal design, selection and evaluation of instrumentation for process plants. The presentation is clear, concise and systematic-providing process engineers with a valuable tool for improving quality, costs, safety, loss prevention, and production accounting.

Data Reconciliation And Gross Error Detection In Constrained Data Sets Of Nonlinear Systems

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Author : Richard Oscar Adame
language : en
Publisher:
Release Date : 1986

Data Reconciliation And Gross Error Detection In Constrained Data Sets Of Nonlinear Systems written by Richard Oscar Adame and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Error-correcting codes (Information theory) categories.

A User S Guide To Principal Components

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Author : J. Edward Jackson
language : en
Publisher: John Wiley & Sons
Release Date : 2005-01-21

A User S Guide To Principal Components written by J. Edward Jackson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-21 with Mathematics categories.

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. From the Reviews of A User’s Guide to Principal Components "The book is aptly and correctly named–A User’s Guide. It is the kind of book that a user at any level, novice or skilled practitioner, would want to have at hand for autotutorial, for refresher, or as a general-purpose guide through the maze of modern PCA." –Technometrics "I recommend A User’s Guide to Principal Components to anyone who is running multivariate analyses, or who contemplates performing such analyses. Those who write their own software will find the book helpful in designing better programs. Those who use off-the-shelf software will find it invaluable in interpreting the results." –Mathematical Geology

Dynamic Data Reconciliation And Gross Error Detection

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Author : Sriram Devanathan
language : en
Publisher:
Release Date : 1993

Dynamic Data Reconciliation And Gross Error Detection written by Sriram Devanathan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with categories.

Data Reconciliation And Gross Error Detection

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