Use of variance spectra for in‐line validation of process measurements in continuous processes

Abstract

In‐line oil blending/certification, custody transfer, and performance monitoring are usually carried out with the help of analyzers that measure specific, and typically complex, properties or compositions, which must be reliable and verifiable for economic and regulatory reasons, as well as for safety and quality requirements. In these operations, in‐line measuring devices can generate process data at very high frequencies in typical continuous industrial plants. For this reason, it is proposed here that variance spectra generated by in‐line monitoring devices be used for validation of process measurements and implementation of quality control procedures, while laboratory practices/standards are used to support and evaluate the reliability of the proposed monitoring scheme on a regular basis. The illustrative application studies reveal the great influence that uncertainties and disturbances can exert upon decision‐making in continuous chemical processes and how the proposed monitoring schemes can allow for improvement of process operation and identification of process failures.

Modeling of Spiral Wound Membranes for Gas Separations—Part II: Data Reconciliation for Online Monitoring

“…This type of spectrum provides information about the various sources that contribute to the signal of a variable, including noise/measurement errors (short window sizes) and intrinsic process variations (large window sizes). The variance spectrum can be defined as a set of variances calculated while some variable related to them evolves [31]. The spectrum of variances for short sampling windows are controlled by the variances of the measuring instrument.…”

Numerical Aspects of Data Reconciliation in Industrial Applications

“…This requires the uncertainties of measurements to be evaluated somehow, which is not always easy. Regarding on-line operational measured data, which may contain gross errors, the standard deviation of measurement data may not be suitable for uncertainty evaluation and some techniques have been proposed for on-line characterization of process data for real-time applications [157,158]. An alternative to evaluate the standard deviations of measurement errors based on measuring instrument nominal accuracy is [159]:…”

How do you write and present research well? Answers to the 20 questions

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