Gross error management in data reconciliation
نویسندگان
چکیده
منابع مشابه
Data Reconciliation and Gross Error Detection in Chemical Process Networks
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In this article we show that the linear reconciliation problem can be represented by a standard multiple linear regression model. The appropriate criteria for redundancy, determinability and gross error detection are shown to follow in a straightforward manner from the standard theory of linear least squares. The regression approach suggests a natural measure of the redundancy of an observation...
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In any modern chemical plant, petrochemical process or refinery, hundreds or even thousands of variables such as flow rates, temperatures, pressures, levels, compositions, etc. are routinely measured and automatically recorded for the purpose of process control, online optimization or process economic evaluation. Modern computers and data acquisition systems facilitate collection and processing...
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On-line optimization provides a means for maintaining a process near its optimum operating conditions by providing set points to the process’s distributed control system (DCS). To achieve a plant-model matching for optimization, process measurements are necessary. However, a preprocessing of these measurements is required since they usually contain random and—less frequently—gross errors. These...
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1. Scope, aims and benefits of Data Reconciliation 1.1. Importance of Measurements for Process Monitoring 1.2. Sources of Experimental Errors 1.3. How to Achieve Measurement Redundancy 2. Exploiting redundancy 2.1. Variable Classification 2.2. Benefits of Model Based Data Validation 3. Mathematical formulation of the validation problem 3.1. Data Validation for Steady State Systems 3.2. Solution...
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2015
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2015.09.037