Industrial Processes: Data Reconciliation and Gross Error Detection
نویسندگان
چکیده
منابع مشابه
Data Reconciliation and Gross Error Detection in Chemical Process Networks
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متن کاملTheory and practice of simultaneous data reconciliation and gross error detection for chemical processes
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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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...
متن کاملBasic Statistical Tests For Gross Error Detection
The technique of data reconciliation crucially depends on the assumption that only random errors are present in the data and systematic errors either in the measurements or the model equations are not present. If this assumption is invalid, reconciliation can lead to large adjustments being made to the measured values, and the resulting estimates can be very inaccurate and even infeasible. Thus...
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ژورنال
عنوان ژورنال: Measurement and Control
سال: 2009
ISSN: 0020-2940
DOI: 10.1177/002029400904200704