Reconciling continuum and non-continuum data with industrial application

González, Rubén; Huang, Biao; Xu, Fangwei; Espejo, Aris; Amalraj, J. Jegathesh; Lam, William H. K.

doi:10.1016/j.compchemeng.2010.06.004

Cited by 2 publications

(3 citation statements)

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“…Data reconciliation was first proposed by Kuehn and Davidson to minimize least squares errors between the measured values and the reconciled values subject to the mass balance and heat balance conditions. During the last decades, data reconciliation research has undergone many improvements and now has been widely used in most chemical and energy processes, such as the mineral and metal processing industries, gas pipeline systems, and power plants . In fact, it is difficult to solve and analyze data reconciliation problems owing to the existence of unmeasured variables.…”

Section: Introductionmentioning

confidence: 99%

“…Create a time-correlation analysis matrix for each time-reference sequence; normalize the data in the matrix. Calculate the correlation value of each time-correlation analysis matrix by applying time-correlation analysis according to Equation (7). Select the fittest time-reference sequence whose correlation value is the highest.…”

Section: Time Registration Based On Time-correlation Analysis Matrixmentioning

confidence: 99%

“…During the last decades, data reconciliation research has undergone many improvements and now has been widely used in most chemical and energy processes, [5][6][7][8][9] such as the mineral and metal processing industries, gas pipeline systems, and power plants. [10][11][12] In fact, it is difficult to solve and analyze data reconciliation problems owing to the existence of unmeasured variables.…”

Section: Introductionmentioning

confidence: 99%

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Data reconciliation strategy with time registration for the evaporation process in alumina production

et al. 2017

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In real industrial production, many mass and heat transfer processes are influenced by high temperature, high pressure, and even strong acid or alkali conditions. In addition, some important variables cannot be measured and chemical compositions are analyzed offline with a long time delay, which leads to inaccurate measurements of the process data. In this paper, a layered data reconciliation (LDR) method based on time registration is proposed to improve the measurement accuracy and estimate unmeasured variables. Considering that the material cannot be tagged and tracked in process manufacturing, a temporal and spatial matching strategy for the process data is designed based on a time-correlation analysis matrix which is determined to describe the correlation of each time sequence in the data matrix. Then, a layered data reconciliation model with time registration is developed by reconciling the mass balance layer and the heat balance layer separately and stepwise, and the model is solved by the state transition algorithm. Meanwhile, regular terms and engineer's knowledge are introduced into the data reconciliation model to solve the problem of insufficient redundancy. The industrial verification results from the actual industrial evaporation process indicate that the accuracy of measured values is improved by using the proposed reconciliation strategy.

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Section: Introductionmentioning

confidence: 99%

Section: Time Registration Based On Time-correlation Analysis Matrixmentioning

confidence: 99%