Selecting proper uncertainty model for steady-state data reconciliation – Application to mineral and metal processing industries

Vasebi, Amir; Poulin, Éric C.; Hodouin, D.

doi:10.1016/j.mineng.2014.06.001

Cited by 26 publications

(14 citation statements)

References 42 publications

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“…The aim of data reconciliation is to solve optimization problem through minimizing error about measurement and estimate variable with respecting constraint in process model, such as the law of mass and energy balance. The application of data reconciliation is also used for complementing some methods to improved estimation in plant, such as process monitoring [7]; plant simulation [19]; advanced process control [20]; or real time optimization [3].…”

Section: Data Reconciliationmentioning

confidence: 99%

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Data Reconciliation on PLTGU Gresik Using Particle Swarm Optimization (PSO)

Pratiwi

Biyanto

2017

IJOS

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AbstractThe innaccurate on process data in PLTGU Gresik does not satisfy the mass and energy balance. Data reconciliation techniques can effectively improve precision and reduce measurement error on process variable estimation of data plant through modeling and optimization techniques. In this paper, we propose PSO (Particle Swarm Optimization) algorithm to solve the data reconciliation problem for precise improvement and error minimization. As a result, the standard deviation of data measurement and reconciliation is different on each variable heat exchanger component, so that indicates random errors on measurement. Based on the result, PSO algorithm is capable generate reliable data and minimizing error with sum square error is equal to 1.153. It means PSO algorithm is compatible with the instrument system on PLTGU Gresik. Moreover, data reconciliation is applied then followed with detection gross error using statistical test that is Global Test. As the result, there is not gross error on the measurement.

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Section: Data Reconciliationmentioning

confidence: 99%

“…Data reconciliation is a technique for data processing to improve the accuracy, precision and reliability of process data [2], [3]. A motivation to apply data reconciliation to reduce imprecision and unreliability measurement data and to complete the unmeasured data.…”

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confidence: 99%

Data Reconciliation on PLTGU Gresik Using Particle Swarm Optimization (PSO)

Pratiwi

Biyanto

2017

IJOS

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“…To seek simplicity and bring the problem to a linear case, only the valuable mineral flowrates are here considered as process variables. This assumption does not compromise the generality of the proposed technique, since literature has already introduced some innovative methods to transform the bilinear data reconciliation problem to a linear one (Crowe, 1989;Vasebi et al, 2014). Therefore, it could easily be applied to chemical/physical species flowrates as well as total flowrates.…”

Section: Plant Modelmentioning

confidence: 99%

Determining a dynamic model for flotation circuits using plant data to implement a Kalman filter for data reconciliation

Vasebi

Poulin

Hodouin

2015

Minerals Engineering

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“…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. Under this situation, the projection matrix is constructed on both sides of the balance constraints to set the unmeasured variables to zero so that they can be eliminated.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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Selecting proper uncertainty model for steady-state data reconciliation – Application to mineral and metal processing industries

Cited by 26 publications

References 42 publications

Data Reconciliation on PLTGU Gresik Using Particle Swarm Optimization (PSO)

Data Reconciliation on PLTGU Gresik Using Particle Swarm Optimization (PSO)

Determining a dynamic model for flotation circuits using plant data to implement a Kalman filter for data reconciliation

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

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