Unscented Transformation‐Based Filters: Performance Comparison Analysis for the State Estimation in Polymerization Processes with Delayed Measurements

Galdeano, Ruben; Asteasuain, Mariano; Sánchez, Mabel

doi:10.1002/mren.201000060

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…This process has been chosen in several works dealing with non-linear state-estimation and process-control applications. [9,23] Figure 1 shows a diagram of the copolymerization reactor.…”

Section: Case Studymentioning

confidence: 99%

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Efficient and robust state estimation: Application to a copolymerization process

2021

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Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance for non-linear state estimation. Besides, filters based on robust statistics have been proposed to deal with the presence of outliers. However, reported robust filters have employed only the Huber M-estimator as the loss function of the estimation problem. This work presents a new state-estimation procedure based on the unscented transformation and robust statistics concepts. When outliers are present, estimates are more accurate than when using the conventional filter. In contrast to previous research, our methodology is also efficient when there are no outliers. The performances of different loss functions for solving the estimation problem are presented. The results show that redescending Mestimators outperform the Huber function. The behaviour of the technique is analyzed for a copolymerization process.

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“…This process has been chosen in several works dealing with non-linear state-estimation and process-control applications. [9,23] Figure 1 shows a diagram of the copolymerization reactor.…”

Section: Case Studymentioning

confidence: 99%

“…The UKF has provided better results than EKF for the monitoring of polymerization processes. [9,10] Moreover, control strategies that use the UKF for non-linear state estimation have shown very good performance for those processes. [11,12] Recently, particle filtering (PF) has also been applied to polymerization processes.…”

Section: Introductionmentioning

confidence: 99%

Efficient and robust state estimation: Application to a copolymerization process

2021

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“…Under assumptions such as constant ethylene concentration, excess co-catalyst concentration, well-mixed reactor, initiation rate equal to propagation rate for ethylene (k p 11 ), and propagation controlled by the chemical nature of the monomer species, the set of differential equations that describe the system are shown in Equations (8)- (21).…”

Section: Process Modellingmentioning

confidence: 99%

Data-Driven Estimation of Significant Kinetic Parameters Applied to the Synthesis of Polyolefins

et al. 2019

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A data-driven strategy for the online estimation of important kinetic parameters was assessed for the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst at different diene concentrations and reaction temperatures. An initial global sensitivity analysis selected the significant kinetic parameters of the system. The retrospective cost model refinement (RCMR) algorithm was adapted and implemented to estimate the significant kinetic parameters of the model in real time. After verifying stability and robustness, experimental data validated the algorithm performance. Results demonstrate the estimated kinetic parameters converge close to theoretical values without requiring prior knowledge of the polymerization model and the original kinetic values.

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“…More recently, the unscented Kalman filter (UKF) showed to be an efficient filter that overcomes most of the problems found with the EKF such as divergence caused by model linearizations. In the polymerization field, the UKF has been analyzed with simulated and with experimental data. ,− UKF and EKF are purely recursive filters, which are not recommended for processes with frequent perturbations and highly nonlinear models, as for the case of semicontinuous emulsion polymerization systems. For these systems, the UKF and EKF estimation may present some violation of the physical constrains .…”

Section: Introductionmentioning

confidence: 99%

The Autocovariance Least-Squares Method for Batch Processes: Application to Experimental Chemical Systems

Rincón

Roux

Lima

2014

Ind. Eng. Chem. Res.

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Chemical engineering processes need careful monitoring in order to ensure product specification (i.e., composition, quality, etc.). Physicochemical analytical techniques can be applied for characterization, but those techniques can be time-consuming and become impractical for chemical processes for which online monitoring is needed. Calorimetry is frequently used to monitor and control batch and semibatch reactions. State and covariance estimation for batch processes employing advanced techniques, such as moving horizon estimation (MHE) for state and autocovariance least-squares (ALS) for covariance, have not been studied together. In this paper, the ALS technique is extended for batch processes and validated using laboratory data of three experimental case studies, with focus on polymerization processes. The problem of the simultaneous online estimation of states and parameters in these systems, employing recursive, extended Kalman filter (EKF) and unscented Kalman filter (UKF), and optimization-based (MHE) estimators with covariances estimated by the ALS, is examined. The results of the implementation of the proposed approach show accuracy of the estimated variables for the polymerization processes considered. Thus, this work provides a systematic procedure for the successful determination of covariances and states for experimental batch cases, including systems under repeated perturbations.

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Unscented Transformation‐Based Filters: Performance Comparison Analysis for the State Estimation in Polymerization Processes with Delayed Measurements

Cited by 7 publications

References 25 publications

Efficient and robust state estimation: Application to a copolymerization process

Efficient and robust state estimation: Application to a copolymerization process

Data-Driven Estimation of Significant Kinetic Parameters Applied to the Synthesis of Polyolefins

The Autocovariance Least-Squares Method for Batch Processes: Application to Experimental Chemical Systems

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