Reconciliation of data with non-random errors

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Abdollahi Douraki Zahra Sadat; Abdollahzadeh Ali Akbar; Khalesi Mohammad Reza

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Journal Paper

Paper Information

Journal: JOURNAL OF GEOMINE Year:2023 | Volume:1 | Issue:4 Page(s): 171-178

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Information Journal Paper

Title

Reconciliation of data with non-random errors

Author(s)

Abdollahi Douraki Zahra Sadat | Abdollahzadeh Ali Akbar | Khalesi Mohammad Reza | Issue Writer Certificate

Keywords

Data Reconciliation

Gross Error

M-estimators

Robust

Process Measurement

Abstract

Errors of measured data could impact the offline optimizations or online control systems, leading to potentially uneconomical or unsafe process conditions. To address this issue, data reconciliation methods are introduced to enhance the data as much as possible. In this regard, existence of non-random errors is challenging. This article debates the use of conventional sum of squares objective function in the case of presence of non-random errors and shows how a robust estimator such as the maximum likelihood ameliorate the reconciliation. The robustness of the new objective function was assessed using simulated data. Results indicate that the sum of errors between real simulated data of flowrates and their estimation counterparts decreases from 124% to 27% in the case of a gross error in one stream, when robust objective function is manipulated. Even if no non-random error exists, it is shown that robust estimator could result in better reconciliation of data, if optimum parameters are chosen for the robust objective function.

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