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Journal ArticleDOI

Estimation of flows and temperatures in process networks

G. M. Stanley, +1 more
- 01 Sep 1977 - 
- Vol. 23, Iss: 5, pp 642-650
TLDR
In the course of daily operation of a petroleum refinery or chemical complex, many thousands of items of infor-mation are generated, gathered, and recorded, and these data are, in turn, used to plan, schedule, control, and evaluate process operations.
Abstract
In the course of daily operation of a petroleum refinery or chemical complex, many thousands of items of infor-mation are generated, gathered, and recorded. These data are, in turn, used to plan, schedule, control, and evaluate process operations. Because of the highly integrated nature of modern processes, inaccurate data taken from one part of the process can easily lead to poor decisions that affect other parts of the processes. For instance, if inventory and production data on one product are inaccurate, the manufacturer may be forced to substitute a premium grade product to meet his delivery, thereby incurring a quality giveaway and creating an additional demand for the substitute product. Or, he may have to procure the supply from some other sources at additional costs. Or, he may accumulate unnecessarily large inventory, thereby tying up production and storage facilities needed for other products. Because of the immense scale of operations, even a small percentage change in inventory or flow may make a substantial difference in revenues or profits. The availability of accurate and consistent process data is therefore crucial to all process analyses.

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Citations
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Journal ArticleDOI

Survey of model-based failure detection and isolation in complex plants

TL;DR: In this article, the authors surveyed techniques to detect and isolate failures in complex technological systems, such as sensor biases, actuator malfunctions, leaks, and equipment deterioration, based on analytical redundancy afforded by a mathematical model of the system.
Journal ArticleDOI

A new structural framework for parity equation-based failure detection and isolation

TL;DR: A new framework for developing parity equations that prevent incorrect isolation decisions under marginal size failures in a decision process that tests each residual independently is described.
Journal ArticleDOI

Detection and identification of faulty sensors in dynamic processes

TL;DR: In this article, a dynamic structured residual approach with maximized sensitivity is proposed which generates a set of structured residuals, each decoupled from one subset of faults but most sensitive to others.
Journal ArticleDOI

On the theory of optimal sensor placement

TL;DR: In this paper, an equivalent reformulation of the design problem such that the dimension of the NLP is independent of all decision variables is presented, and the traditional sensor-placement problem based on static process conditions is extended to linear dynamic processes.
Journal ArticleDOI

Detection, identification, and reconstruction of faulty sensors with maximized sensitivity

TL;DR: In this paper, a new method was proposed to detect, reconstruct, and identify faulty sensors using a normal process model, which can be built from first principles or statistical methods such as partial least squares or principal component analysis.
References
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Book

Stochastic Processes and Filtering Theory

TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Book

Applied optimal control

Journal ArticleDOI

Reconcillation and Rectification of Process Flow and Inventory Data

TL;DR: In this article, the authors show how information inherent in the process constraints and measurement statistics can be used to enhance flow and inventory data and propose a graph-theoretic approach to simplify the reconciliation of conflicting data and estimation of unmeasured process streams.
Journal ArticleDOI

A Tracking Static State Estimator

TL;DR: A tracking static state estimator is a digital feedback loop (a computer algorithm) which uses real time measurements of voltage magnitudes, watt flows, and var flows to track the static state (voltage at all buses) as it varies during the daily load cycle as discussed by the authors.
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