Application of process data reconciliation in power plants

Abstract

The operation of power plants and chemical processes requires process measurements for optimal operations. Process measurements are essential for plant performance optimization, process monitoring and process control. It is vital to have reliable and accurate process data to achieve process optimization. However, process measurements are inevitably subject to measurement errors. These measurement errors are classified as random and gross errors. Data reconciliation technique is an effective data treatment method that is used in chemical processes to enhance the quality of process data. The purpose of data reconciliation is to reduce random errors to achieve measurements which are as accurate and reliable as possible. Data reconciliation technique uses available process measurements to produce consistent and accurate estimates, so close to the true values that they satisfy model constraints. Further, data reconciliation technique depends on measurement redundancy to perform reconciliation and produce reliable estimates. In addition, data reconciliation can also provide estimates of unmeasured observable variables. Process data reconciliation is not complete without a gross error detection strategy that can effectively detect and eliminate gross errors in measurements. Data reconciliation is applied to linear and nonlinear steady state processes with measured and partially measured variables. Heat exchanger and steam generator models with nonlinear mass and energy constraints are used. The reconciliation process is applied in a feed water flow measurements model to illustrate the applicability of data reconciliation.