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

2021 ◽  
Author(s):  
Sujan Hazra ◽  
Prakash Abhale ◽  
Samik Nag ◽  
Sam Mathew ◽  
Shankar Narasimhan
Keyword(s):  
Blast Furnace ◽  
Error Detection ◽  
Data Reconciliation ◽  
Blast Furnace Process ◽  
Process Data ◽  
Gross Error Detection ◽  
Furnace Process ◽  
Detection Techniques ◽  
Gross Error

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.

Data Reconciliation and Suspect Measurement Identification for Gas Turbine Cogeneration Systems

2013 ◽  
Vol 135 (9) ◽  
Author(s):  
Mohammed S. Syed ◽  
Kerry M. Dooley ◽  
F. Carl Knopf ◽  
Michael R. Erbes ◽  
Frantisek Madron
Keyword(s):  
Power Systems ◽  
Error Detection ◽  
Monte Carlo Study ◽  
Data Reconciliation ◽  
Random Errors ◽  
Process Data ◽  
Gross Error Detection ◽  
Gross Error ◽  
Cogeneration System ◽  
Model Linearization

Data reconciliation is widely used in the chemical process industry to suppress the influence of random errors in process data and help detect gross errors. Data reconciliation is currently seeing increased use in the power industry. Here, we use data from a recently constructed cogeneration system to show the data reconciliation process and the difficulties associated with gross error detection and suspect measurement identification. Problems in gross error detection and suspect measurement identification are often traced to weak variable redundancy, which can be characterized by variable adjustability and threshold value. Proper suspect measurement identification is accomplished using a variable measurement test coupled with the variable adjustability. Cogeneration and power systems provide a unique opportunity to include performance equations in the problem formulation. Gross error detection and suspect measurement identification can be significantly enhanced by increasing variable redundancy through the use of performance equations. Cogeneration system models are nonlinear, but a detailed analysis of gross error detection and suspect measurement identification is based on model linearization. A Monte Carlo study was used to verify results from the linearized models.

An improved MT–NT method for gross error detection and data reconciliation

2004 ◽  
Vol 28 (11) ◽  
pp. 2189-2192 ◽  
Author(s):  
Fang Wang ◽  
Xiao-ping Jia ◽  
Shi-qing Zheng ◽  
Jin-cai Yue
Keyword(s):  
Error Detection ◽  
Data Reconciliation ◽  
Gross Error Detection ◽  
Gross Error

Correntropy based data reconciliation and gross error detection for bilinear systems

2020 ◽  
Vol 212 ◽  
pp. 115327 ◽  
Author(s):  
Zhengjiang Zhang ◽  
Lester Lik Teck Chan ◽  
Junghui Chen ◽  
Zhijiang Shao
Keyword(s):  
Error Detection ◽  
Bilinear Systems ◽  
Data Reconciliation ◽  
Gross Error Detection ◽  
Gross Error

Bayesian method for simultaneous gross error detection and data reconciliation

AIChE Journal ◽  
2015 ◽  
Vol 61 (10) ◽  
pp. 3232-3248 ◽  
Author(s):  
Yuan Yuan ◽  
Shima Khatibisepehr ◽  
Biao Huang ◽  
Zukui Li
Keyword(s):  
Error Detection ◽  
Bayesian Method ◽  
Data Reconciliation ◽  
Gross Error Detection ◽  
Gross Error

scholarly journals Gross Error Detection and Data Reconciliation using Historical Data

2011 ◽  
Vol 15 ◽  
pp. 55-59 ◽  
Author(s):  
Shaochao Sun ◽  
Dao Huang ◽  
Yanxue Gong
Keyword(s):  
Error Detection ◽  
Historical Data ◽  
Data Reconciliation ◽  
Gross Error Detection ◽  
Gross Error
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