EBookClubs

Read Books & Download eBooks Full Online

EBookClubs

Read Books & Download eBooks Full Online

Book Data Reconciliation and Gross Error Detection

Download or read book Data Reconciliation and Gross Error Detection written by Shankar Narasimhan and published by Elsevier. This book was released on 1999-11-29 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Data Reconciliation and Gross Error Detection

Download or read book Data Reconciliation and Gross Error Detection written by Shankar Narasimhan and published by Gulf Professional Publishing. This book was released on 2000 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: : 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.

Book Data Reconciliation Gross Error Detection recurso Electr nico

Download or read book Data Reconciliation Gross Error Detection recurso Electr nico written by Shankar Narasimhan and published by . This book was released on 1999 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: : 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.

Book Dynamic Data Reconciliation and Gross Error Detection

Download or read book Dynamic Data Reconciliation and Gross Error Detection written by Sriram Devanathan and published by . This book was released on 1993 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Simultaneous Gross Error Detection and Data Reconciliation Using Gaussian Mixture Distribution

Download or read book Simultaneous Gross Error Detection and Data Reconciliation Using Gaussian Mixture Distribution written by Hashem Alighardashi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Data Reconciliation and Gross Error Detection in Constrained Data Sets of Nonlinear Systems

Download or read book Data Reconciliation and Gross Error Detection in Constrained Data Sets of Nonlinear Systems written by Richard Oscar Adame and published by . This book was released on 1986 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Data Processing and Reconciliation for Chemical Process Operations

Download or read book Data Processing and Reconciliation for Chemical Process Operations written by José A. Romagnoli and published by Elsevier. This book was released on 1999-10-25 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Book Data Reconciliation and Gross Error Detection for a Mineral Processing Plant

Download or read book Data Reconciliation and Gross Error Detection for a Mineral Processing Plant written by David John Campbell and published by . This book was released on 1997 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: The results of the evaluation showed that both gross error detection algorithms gave similar performance results for most measurements. The detection on average was typically better than 60% for a bias of 3.6 times the measurement noise standard deviation, while maintaining a 95% rejection of the background measurement noise. However, in the heat interchange model the instrumentation arrangement resulted in some measurement residuals (the difference between the raw measurement and the reconciled measurement) being perfectly correlated.

Book Steady State Detection Data Reconciliation and Gross Error Detection

Download or read book Steady State Detection Data Reconciliation and Gross Error Detection written by Rocio del Pilar Moreno and published by . This book was released on 2010 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Process Stream Data Analysis

Download or read book Process Stream Data Analysis written by Cristina Murcia Mayo and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the major challenges for energy companies is to adapt their process plants to the continuous improvements of available technologies, so as to make their old plants as competitive and cost-efficient as the new ones. Along these lines, process stream data was recently collected for analysing opportunities for improved process integration of the Hydrocracker Unit of a major oil refinery located in Lysekil on the West Coast of Sweden. However, inconsistencies in the process data measurements, e.g. energy balances that do not add up, made the study cumbersome. For analysing heat exchanger networks it is essential to establish sets of process data with balanced heat balances for the existing heat exchangers. The aim of this thesis project was to develop a computer-based solution for systematic analysis, identification and correction of the "raw" data obtained from process data measurements in order to acquire such a consistent set of data. With this purpose, a tool for Data Reconciliation and Gross Error Detection for process stream data was developed using Visual Basic in Microsoft Excel. The tool is based on the Modified Iterative Measurement Test. A second tool, which is easier for handle large data sets and especially designed for networks with non-linear constraints was also developed. This second tool is only able to solve Data Reconciliation problems, so it is targeted for sets of data where there are exclusively random errors. Both developed tools were used to analyse the data set collected from the refinery's Hydrocracker Unit with the purpose of generating a consistent set of data with balanced heat exchangers. The solution proposed is an energy balanced network, where from the 32 temperature measurements, all the reconciled values, except two, are within the specified bounds indicated. The two reconciled temperatures outside the bounds are the ones in which the presence of a gross error has been confirmed. Since this is a preliminary study, the solution proposed must be taken as a recommendation.