Dynamic Data Reconciliation for Enhancing Performance of Minimum Variance Control in Univariate and Multivariate Systems

Zhang, Zhengjiang; Chen, Junghui

doi:10.1021/acs.iecr.6b02532

Cited by 13 publications

(7 citation statements)

References 30 publications

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“…Assuming that the measured variables and the disturbance variables are independent, then Σ m and Σ δ satisfy (22) where n is the number of variables. Then, (23) while (24) Taking the trace, (25) The proof process of eq 21 is completed. Eq 21 indicates that the trace of the covariance of φ(t) is less than the minimum value of those of ε(t) and δ(t), which means that DDR can suppress the measurement noise and reduce the prediction error.…”

Section: Ddr For Prediction Error Reductionmentioning

confidence: 99%

“…DDR is based on the Bayesian estimation, and the optimal estimation obtained by synthesizing the information of measurements and model predictions is relatively closer to the actual outputs. 23 DDR has been used for the correction of measurement information, that is, the suppression of measurement noise. 24 However, for the correction of distributed predicted outputs, the application of DDR has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Data Reconciliation for Improving the Prediction Performance of the Data-Driven Model on Distributed Product Outputs

Zhu

Zhang

Liu

2022

Ind. Eng. Chem. Res.

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The product quality of some chemical processes is affected by those output variables exhibiting distributed characteristics. To enable process monitoring and quality analysis, effective measurements of distributed product outputs are particularly important. Because of the limitation and high cost of measurement techniques, soft sensors are gradually used in distributed process monitoring. In this work, for the soft sensor model developed by ensemble just-in-time kernel learning (EJKL), dynamic data reconciliation (DDR) is proposed and combined with EJKL to improve the accuracy of model prediction. First, the EJKL-based model is designed to implement online prediction of distributed outputs. Based on offline measured outputs with Gaussian distributed measurement noise, DDR is then developed for distributed production processes to reduce the prediction errors of the EJKL-based model. Furthermore, the improvement of DDR for prediction results under non-Gaussian measurement noise is also presented. Using the information from the model-based prediction and the offline measurements, DDR can reduce prediction errors through the maximum posterior probability estimation. The case studies of the model predictions on molecular weight distribution and crystal size distribution verify that the accuracy of the predicted outputs can be improved by the proposed method.

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Section: Ddr For Prediction Error Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Data Reconciliation for Improving the Prediction Performance of the Data-Driven Model on Distributed Product Outputs

Zhu

Zhang

Liu

2022

Ind. Eng. Chem. Res.

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“…For defining the upper and lower values, the same weights and the square of the weights considered for the modified Hurst index are chosen, respectively. The proposed modified index in equation (17) lies between the values defined in equations ( 18) and (19).…”

Section: Performance Assessment Of Mimo Systemsmentioning

confidence: 99%

“…Further research was performed to extend the MV index for MIMO systems. [10][11][12][13][14][15][16][17] In MIMO systems, the interaction between control loops should also be considered. As a result, due to the interactions between different variables, the required prior information and the computational burden to obtain the MV index are increased.…”

Section: Introductionmentioning

confidence: 99%

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Khosroshahi

Poshtan

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Control system performance assessment is significant, especially in practical applications. One of the most important indices for the performance assessment of control systems is minimum variance. Calculating the minimum variance index in multivariate systems requires prior knowledge of system parameters and models, and is therefore an obstacle in practical applications. In this article, an index is proposed for the performance assessment of multivariate control loops, evaluating the system performance with the minimum variance criterion and using only the system’s routine operation data. This index can quantify the performance using neither any prior knowledge of system parameters nor the system’s optimal operation data. The proposed index is based on the Hurst exponent, a parameter for measuring correlations in time series data. In this article, detrended fluctuation analysis and rescaled range analysis are used to estimate the Hurst exponents of system outputs. Using a combination of these Hurst exponents, an index is defined for the performance assessment of multivariate systems. The results of simulation examples illustrate that the proposed index can assess the performance efficiently.

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“… demonstrated that DDR was an effective filter inside control loops. In our previous work, DDR is combined within the feedback MV controller to enhance the performance of controller . In this section, DDR is extended to be used to decrease the effect of measurement noise on the results of CPA.…”

Section: Based On Cpa Combined With Ddrmentioning

confidence: 99%

Dynamic Data Reconciliation to Decrease the Effect of Measurement Noise on Controller Performance Assessment

Yang

Zhang

Zhao

et al. 2020

IEEJ Transactions Elec Engng

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Controller performance assessment (CPA) is capable to evaluate the controller performance and has been widely used in industrial processes. In the existing research studies, CPA usually assumes that the feedback signals are the actual process outputs without considering the measurement noise. Minimum variance (MV)‐based CPA is the most common method for controller performance evaluation. In this paper, the effect of measurement noise is considered in the feedback signals of the MV‐based CPA. The dynamic data reconciliation (DDR) is incorporated in the MV‐based CPA (DDR‐MV‐CPA) to decrease the effect of measurement noise on CPA. The effectiveness of the proposed DDR‐MV‐CPA method is demonstrated via theoretical derivation, simulations, and experimental comparisons. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Dynamic Data Reconciliation for Enhancing Performance of Minimum Variance Control in Univariate and Multivariate Systems

Cited by 13 publications

References 30 publications

Dynamic Data Reconciliation for Improving the Prediction Performance of the Data-Driven Model on Distributed Product Outputs

Dynamic Data Reconciliation for Improving the Prediction Performance of the Data-Driven Model on Distributed Product Outputs

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Dynamic Data Reconciliation to Decrease the Effect of Measurement Noise on Controller Performance Assessment

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