PROCESS DATA RECONCILIATION AND RECTIFICATION

1990 ◽  
pp. 385-466 ◽  
Author(s):  
RICHARD S.H. MAH
Keyword(s):  
Data Reconciliation ◽  
Process Data

Global Balance of Plant in NPPs Using Process Data Reconciliation According to VDI 2048

2014 ◽  
Author(s):  
Magnus Langenstein ◽  
Bernd Laipple
Keyword(s):  
Monitoring System ◽  
Mass Flow ◽  
Online Monitoring ◽  
Water Mass ◽  
Data Reconciliation ◽  
Measurement Information ◽  
Feed Water ◽  
Process Data ◽  
Operation Conditions ◽  
Plant Process

The large quantities of measurement information gathered throughout a plant process make the closing of the mass and energy balance nearly impossible without the help of additional tools. For this reason, a variety of plant monitoring tools for closing plant balances was developed. A major problem with the current tools lies in the non-consideration of redundant measurements which are available throughout the entire plant process. The online monitoring reconciliation system is based on the process data reconciliation according to VDI 2048 standard and is using all redundant measurements within the process to close mass and energy balances. As a result, the most realistic process with the lowest uncertainty can be monitored. This system is installed in more than 35 NPPs worldwide and is used ○ as a basis for correction of feed water mass flow and feed water temperature measurements (recover of lost Megawatts). ○ as a basis for correction of Taverage (Tav) (recover of steam generator outlet pressure in PWRs). ○ for maintaining the thermal core power and the feed water mass flow under continuous operation conditions. ○ for automatic detection of erroneous measurements and measurement drift. ○ for detection of inner leakages, non-condensable gases and system losses. ○ for calculating non measured values (e.g. heat transfer coefficients, ΔT, preheater loads,…). ○ as a monitoring system for the main thermodynamic process. ○ for verifying warranty tests more accurate. ○ as a application of condition-based maintenance and component monitoring. ○ for What-if scenarios (simulation, not PDR) This paper describes the methodology according to VDI 2048 (use of Gaussian correction principle and quality criterias). The benefits gained from the use of the online monitoring system are demonstrated.

Effect of redundancy on estimation accuracy in process data reconciliation

1987 ◽  
Vol 42 (9) ◽  
pp. 2115-2121 ◽  
Author(s):  
Alexandros Kretsovalis ◽  
Richard S.H. Mah
Keyword(s):  
Data Reconciliation ◽  
Estimation Accuracy ◽  
Process Data

Robust Data Reconciliation in Linear Process

2011 ◽  
Vol 383-390 ◽  
pp. 667-671
Author(s):  
Ling Ke Zhou
Keyword(s):  
Least Squares ◽  
Linear Process ◽  
Measurement Noise ◽  
Least Squares Estimator ◽  
Data Reconciliation ◽  
Robust Estimator ◽  
Process Data ◽  
Simulation Results ◽  
Spatial Redundancy

Data reconciliation is based on spatial redundancy to adjust process data to improve the quality of measurement corruption due to measurement noise. However, the presence of gross errors can severely bias the reconciled results. Robust estimators can significantly reduce the effect of gross errors and yield less biased results. In this paper, a method is proposed to solve the robust data reconciliation problem. By using the proposed method, the robust estimator problem can be transformed into least squares estimator problem which leads to the convenience in computation. Simulation results for a linear process verify the efficiency of the proposed method.

Power Recapture and Power Uprate in NPPs With Process Data Reconciliation in Accordance With VDI 2048

2006 ◽  
Author(s):  
Magnus Langenstein
Keyword(s):  
Process Parameters ◽  
Control Volume ◽  
Reactor Power ◽  
Data Reconciliation ◽  
Thermal Reactor ◽  
Process Data ◽  
Pressurized Water ◽  
Statistical Probability ◽  
Water Reactor ◽  
Heat Cycle

The determination of the thermal reactor power is traditionally done by establishing the heat balance: • for a boiling water reactor (BWR) at the interface of reactor control volume and heat cycle; • for a pressurized water reactor (PWR) at the interface of the steam generator control volume and turbine island on the secondary side. The uncertainty of these traditional methods is not easy to determine and it can be in the range of several percent. Technical and legal regulations (e.g. 10CFR50) cover an estimated instrumentation error of up to 2% by increasing the design thermal reactor power for emergency analysis to 102% of the licensed thermal reactor power. Basically, the licensee has the duty to warrant at any time operation inside the analysed region for thermal reactor power. This is normally done by keeping the indicated reactor power at the licensed 100% value. A better way is to use a method which allows a continuous warranty evaluation. The quantification of the level of fulfilment of this warranty is only achievable by a method which: • is independent of single measurements accuracies; • results in a certified quality of single process values and for the total heat cycle analysis; • leads to complete results including 2-sigma deviation especially for thermal reactor power. This method, which is called ‘process data reconciliation based on VDI 2048 guideline’, is presented here [1, 2]. The method allows to determine the true process parameters with a statistical probability of 95%, by considering closed material, mass- and energy balances following the Gaussian correction principle. The amount of redundant process information and complexity of the process improves the final results. This represents the most probable state of the process with minimized uncertainty according to VDI 2048. Hence, calibration and control of the thermal reactor power are possible with low effort but high accuracy and independent of single measurement accuracies. Furthermore, VDI 2048 describes the quality control of important process parameters. Applied to the thermal reactor power, the statistical certainty of warranting the allowable value can be quantified. This quantification allows keeping a safety margin in agreement with the authority. This paper presents the operational application of this method at an operating plant and describes the additional use of process data reconciliation for acceptance tests, power recapture and system and component diagnosis.

Warranty Test for a High-Pressure Turbine Retrofit Using Process Data Reconciliation in Accordance With VDI 2048

2008 ◽  
Author(s):  
Magnus Langenstein ◽  
Steffen Riehm ◽  
Jan Hansen-Schmidt
Keyword(s):  
High Pressure ◽  
Statistical Approach ◽  
Mathematical Statistic ◽  
Reactor Power ◽  
Data Reconciliation ◽  
Thermal Reactor ◽  
Process Data ◽  
High Pressure Turbine ◽  
Process Information ◽  
Statistic Approach

The mathematical and statistical approach called “process data reconciliation in accordance with VDI 2048 [1, 2]” • calculates thermal reactor power; • performs warranty tests with on-site operational instrumentation only (without additional instrumentation). with an accuracy of approximately 0.4%. Process data reconciliation based on VDI 2048 is a mathematical-statistic approach that makes use of redundant process information. The overall process monitored continuously in this manner therefore provides hourly process information of a quality equal to acceptance measurements [3 – 10]. At a NPP in Germany a warranty test for a high pressure turbine retrofit was performed in 2007 exclusively using this method. In this paper, the approach and the results of the warranty test are described.

Financial Benefits of Process Data Reconciliation in Power Generating Plants

2006 ◽  
Author(s):  
Andy Jansky
Keyword(s):  
Peak Load ◽  
Data Reconciliation ◽  
Process Data ◽  
Process Measurement ◽  
Financial Benefits ◽  
Entire Plant ◽  
Output Time ◽  
Main Factors ◽  
Measurement Uncertainties ◽  
Plant Process

Process Data Reconciliation (PDR) is a certified method that calculates the most likely values considering process measurement uncertainties and closing all energy- and material balances where all interdependencies within the entire plant process are fulfilled in a covariance matrix. There are three main factors that generate the financial benefits for the user of reconciled data, depending on the type of plant and base/peak load behaviour: • Increased efficiency / maximized output; • Time advantage in retrieving “lost” megawatts; • Reduction of maintenance costs.

scholarly journals Industrial Processes: Data Reconciliation and Gross Error Detection

2009 ◽  
Vol 42 (7) ◽  
pp. 209-215 ◽  
Author(s):  
Yu Miao ◽  
Hongye Su ◽  
Rong Gang ◽  
Jian Chu
Keyword(s):  
Error Detection ◽  
Industrial Applications ◽  
Vital Role ◽  
Data Reconciliation ◽  
Industrial Processes ◽  
Random Errors ◽  
Process Data ◽  
Gross Error Detection ◽  
Business Decision ◽  
Gross Error

Process data plays a vital role in industrial processes, which are the basis for process control, monitoring, optimization and business decision making. However, it is inevitable that process data measurements will be corrupted by random errors. Therefore, data reconciliation has been developed to improve accuracy of process data by reducing the effect of random errors. Unfortunately, reconciled values would be deteriorated by gross errors, which may be present during measurement. Therefore, gross error detection is necessary to guarantee the efficiency of data reconciliation, which has been developed to identify and eliminate gross errors in process data. In this paper, a review of data reconciliation and gross error detection and relevant industrial applications are presented. As the efficiency of data reconciliation and gross error detection largely depends upon the locations of sensors, sensor networks design is also included in the review. Meanwhile, some achievements of the authors are also included.

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