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Robust Large-Scale State-Estimate Prediction

Source: AIChE
  • Type:
    Conference Presentation
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    AIChE Member Credits 0.5
    AIChE Members $19.00
    AIChE Graduate Student Members Free
    AIChE Undergraduate Student Members Free
    Non-Members $29.00
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 20, 2020
  • Duration:
    15 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.30

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Effective monitoring and control of a process require online measurements of all state variables of the process. Measurement of all state variables are typically not available due to the lack of reliable online sensors and/or high costs of online sensors. These unmeasured variables are often directly related to product properties, which are important to monitor and control. Their estimates are usually obtained using state estimators. As a result of the need for and the importance of state estimation, the development of accurate state estimators that are robust with respect to model uncertainties and unmeasured inputs, has been the subject of research for decades [1-5]. There have been a few reports on the design of large-scale state estimators [6-11]. Although the implementation of centralized estimators for large-scale systems seems to be an optimal approach, these estimators are not scaleable to complex, large-scale systems, as they suffer from the curse of dimensionality.

In this paper, we address the problem of large-scale robust state-estimate prediction; that is, the prediction of future values of state estimates robustly at every time instant in large-scale processes. To this end, we decompose a large-scale state-estimate prediction problem into a set of smaller-scale state-estimate prediction problems so that the resulting set of the smaller-scale state-estimate predictors are more robust, easier to design and implement, and more computationally efficient. We also study tuning of the smaller-scale state-estimate predictors to maximize the robustness of the predictors set while ensuring the stability of the error dynamics of the entire set. In addition, we address the problem of implementing the smaller-scale state-estimate predictors in parallel (using a computer with parallel processors) to improve computational efficiency of the state estimate prediction while preserving the accuracy and robustness of state-estimate predictions.

References

[1] J.M. Ali, N.H. Hoang, M.A. Hussain, D. Dochain, Review and classification of recent observers applied in chemical process systems, Computers & Chemical Engineering, 76 (2015) 27-41.

[2] A.K. Jana, A nonlinear exponential observer for a batch distillation, in: 2010 11th International Conference on Control Automation Robotics & Vision, IEEE, 2010, pp. 1393-1396.

[3] M. Soroush, State and parameter estimations and their applications in process control, Computers & Chemical Engineering, 23 (1998) 229-245.

[4] S. Tatiraju, M. Soroush, Nonlinear state estimation in a polymerization reactor, Industrial & engineering chemistry research, 36 (1997) 2679-2690.

[5] N. Zambare, M. Soroush, B.A. Ogunnaike, Robustness improvement in multi-rate state estimation, in: Proceedings of the 2001 American Control Conference.(Cat. No. 01CH37148), IEEE, 2001, pp. 993-998.

[6] N. Abdel-Jabbar, C. Kravaris, B. Carnahan, A partially decentralized state observer and its parallel computer implementation, Industrial & engineering chemistry research, 37 (1998) 2741-2760.

[7] N. Abdel-Jabbar, C. Kravaris, B. Carnahan, Structural analysis and partitioning of dynamic process models for parallel state estimation, in: Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No. 98CH36207), IEEE, 1998, pp. 3170-3176.

[8] J.B. Carvalho, F.M. Barbosa, Parallel and distributed processing in state estimation of power system energy, in: MELECON'98. 9th Mediterranean Electrotechnical Conference. Proceedings (Cat. No. 98CH36056), IEEE, 1998, pp. 969-973.

[9] R. Ebrahimian, R. Baldick, State estimation distributed processing [for power systems], IEEE Transactions on Power Systems, 15 (2000) 1240-1246.

[10] D.M. Falcao, F.F. Wu, L. Murphy, Parallel and distributed state estimation, IEEE Transactions on Power Systems, 10 (1995) 724-730.

[11] D.B. Pourkargar, M. Moharir, A. Almansoori, P. Daoutidis, Distributed estimation and nonlinear model predictive control using community detection, Industrial & Engineering Chemistry Research, 58 (2019) 13495-13507.

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Checkout

Checkout

Do you already own this?

Pricing


Individuals

AIChE Member Credits 0.5
AIChE Members $19.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $29.00
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