Application Of Data Reconciliation Research Articles

Application of data reconciliation and gross error detection techniques to enhance reliability and consistency of the blast furnace process data

AbstractEffective control of a blast furnace (BF) process requires accurate estimates of key process indicators (KPIs), namely, productivity, coke rate, direct reduction per cent, adiabatic flame temperature, bosh gas volume and top gas utilization. Some of these KPIs are obtained directly from the measurements, and some are derived by carrying out material and energy balances on measurements of different feeds, their compositions and temperatures. Due to errors in the measurements, the estimates of the KPIs can be inconsistent or misleading, which may result in misinterpretation of the current state of the BF process. Hence, it is necessary to reconcile the measurement data before these are used either for interpreting the current furnace state directly or as an input to other models. In the proposed methodology, data reconciliation and gross error detection techniques are used to improve the accuracy of the estimates of process variables and parameters, by ensuring that they satisfy process constraints such as elemental balances of iron, nitrogen, carbon, oxygen and hydrogen. Since the BF is a fed‐batch process, a customized version of these techniques has been developed and applied real time to an operating BF. The method is shown to be useful in deriving consistent estimates of the hot metal production rate, identifying gross errors in the online gas analyser and for estimating unmeasured parameters, such as top gas flow rate, its moisture concentration and calorific value which are useful for the purpose of stove heating in the downstream process.

Application of Data Reconciliation and Fault Detection and Isolation of Ion Chambers in Advanced Heavy Water Reactor

In a nuclear reactor, e.g. Advanced Heavy Water Reactor, a large number of neutron flux detectors are used for measurement of core flux based on which different monitoring, control and protection functions are performed. Changes in ambient and operating conditions of these detectors result in deviations from the nominal signals. These deviations, termed as faults, lead to erroneous inferences about the operation of the reactor. In order to ensure that such faults do not hamper the intended functionality, adequate degree of redundancy is provided in the detectors and the signal that deviates considerably from the signals of other detectors is rejected. However, it would be desirable to develop a method to continuously monitor the detectors for occurrence of faults and isolate the faulty reading based on more formal and systematic approach. In addition to occasional faults, there might also be random errors resulting from various sources. This paper aims at detection and isolation of faults in Ion Chambers of Advanced Heavy Water Reactor and minimization of random errors in the signal data with the help of a Data Reconciliation based scheme. This scheme makes use of the process constraint models developed from the multivariate statistical techniques, viz., Principal Component Analysis and Iterative Principal Component Analysis combined with Iterative Measurement Test method of Fault Detection and Isolation. Development of constraint models relies on data from Ion Chambers in the form of current signals from their amplifiers. These signals are functions of the neutron fluxes at their respective locations. Such data were generated for four representative situations of the reactor operation with the help of the mathematical model of the reactor and the amplifier models associated with the Ion Chambers. To these data, various additive biases were applied, subsequently, Data Reconciliation and Fault Detection and Isolation are attempted within the ambit of linear steady-state Data Reconciliation and the results are presented. The study establishes the efficacy of Iterative Principal Component Analysis and Iterative Measurement Test for Data Reconciliation and Fault Detection and Isolation of Ion Chamber signals in a nuclear reactor.

Application of Data Reconciliation and Gross Error Detection to Nuclear Material Accounting for Spent Fuel Reprocessing

Data reconciliation and gross error detection are applied to nuclear material accounting of spent fuel reprocessing in order to demonstrate their usefulness for verification activities in a safeguards system. In a simple flowsheet of the Purex process including dissolution, co-decontamination, and uranium-plutonium partition processes, the measurements of the steady-state mass flow rate, which include artificial random errors imitating measurement errors and/or process disturbances, are reconciled by a least-squares method with linear constraints of mass balance equations. The reconciled measurements satisfy the mass balance equations and their random errors are reduced. By use of another set of measurements, which includes both random and systematic errors, the gross error detection with χ2 test statistic is tested in order to find the systematic errors, which can correspond to the failure of measurement devices and the loss of nuclear materials. The systematic error is successfully detected with a given confidence limit.

Application of Data Reconciliation and Gross Error Detection to Nuclear Material Accounting for Spent Fuel Reprocessing

Data reconciliation and gross error detection are applied to nuclear material accounting of spent fuel reprocessing in order to demonstrate their usefulness for verification activities in a safeguards system. In a simple flowsheet of the Purex process including dissolution, co-decontamination, and uranium-plutonium partition processes, the measurements of the steady-state mass flow rate, which include artificial random errors imitating measurement errors and/or process disturbances, are reconciled by a least-squares method with linear constraints of mass balance equations. The reconciled measurements satisfy the mass balance equations and their random errors are reduced. By use of another set of measurements, which includes both random and systematic errors, the gross error detection with χ2 test statistic is tested in order to find the systematic errors, which can correspond to the failure of measurement devices and the loss of nuclear materials. The systematic error is successfully detected with a given confidence limit.

Data reconciliation — Progress and challenges

Measurements in a chemical process are subject to errors, both random and systematic, so that the laws of conservation of mass and energy are not obeyed. In order to record the performance of the process, these measurements are adjusted in order that they conform to the conservation laws and any other constraints imposed upon them. This procedure is known as data reconciliation. Advances in the theory and application of data reconciliation are reviewed and current problems are highlighted. In addition to defining the basic problem, we discuss the detection of gross errors in data and of pre-adjustment of data, finding departures from steady state, estimation of the variance structure of the data, observability of unmeasured quantities and redundancy of measurements.

Application of data-reconciliation and optimisation procedure to hydrogen plant

Application of data-reconciliation and optimisation procedure to hydrogen plant

Application of data-reconciliation and optimisation procedure to hydrogen plant

Aim of this paper is to present an application of the reconciliation and optimisation package ORO (On-line Reconciliation and Optimisation) to an hydrogen plant of an existing refinery. The industrial plant performances optimisation can be divided into three phases : gross errors detection, reconciliation and optimisation. All the difficulties linked to each of these phases and the general approach are presented. The model reconciliation and optimisation skills were tested by comparing the predictions with plant measurements for two different plant capacity conditions. Finally different optimisation strategies are shown, for different production targets.