Practical issues in state estimation using particle filters: Case studies with polymer reactors

Shenoy, Arjun; Prakash, J.; Prasad, Vinay; Shah, Sirish L.; McAuley, Kimberley B.

doi:10.1016/j.jprocont.2012.09.003

Cited by 36 publications

(20 citation statements)

References 27 publications

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“…In this equation, the vectors x k-1 are the initial guesses or the prior estimates and v k is the uncertainty of the evolution model taken as a normal distribution, such that v k ~ N(0, s 2 m ). After developing the computational code, the following conditions were analyzed: a) Analysis of the number of particles (N p ): according to Shenoy et al (2013) and Chen, Morris and Martin (2005), the proper choice of the number of particles can improve the performance of the filter as much as the non-degeneration of the particles. But, high N p can result in high computational cost.…”

Section: Particle Filter Implementationmentioning

confidence: 99%

“…PFs have received attention since the early 2000s to approximate estimation problems (Doucet et al, 2000;Arulampalam et al, 2002;Chen et al, 2008;Shao et al, 2010;Khatibisepehr et al, 2013). For example, Shenoy et al (2013) compared the EKF, UKF and PF for polymerization processes, which are highly nonlinear systems, and observed that PFs delivered more accurate estimates. The main reason is that the PF algorithms are indicated for nonlinear and non-Gaussian systems, as is the case of industrial applications (Chen et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…The efficiency of such algorithms has been shown by several authors in the area of heat conduction Vianna et al, 2013;Silva et al, 2016), of polymerization processes (Shenoy et al, 2011;Shenoy et al, 2013) and in the realization of predictions of the life of lithium-ion batteries (Walker et al, 2015). Recently, Dias et al (2017) proposed a virtual sensor comprising a PF algorithm for state estimation and an artificial neural network (ANN) to predict the final quality of the product obtained in a polymerization reactor.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Parameters and States Using a Bayesian Particle Filter for the Sulfate Ion Adsorption Process in a Fixed Bed Column

Carvalho

Guimarães

Leão

et al. 2019

Braz. J. Chem. Eng.

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Ensuring that industrial effluents meet quality standards to be released into water bodies is still one of the major environmental concerns. Liquid phase adsoption in fixed bed collumns is one of the most known treatments. Measurements are needed to monitor the process. However, as they are often corrupted by noise from the measuring equipment, performing an accurate analysis becomes an important challenge. The present work demonstrates the effectiveness of particle filter Sampling Importance Resampling as a fast and robust tool for monitoring a problem of sulfate ion removal. Experimental measurements were used to validate the methodology and the particle filter (PF) performance was evaluated by means of error metrics, computational time and compared to the Unscented Kalman Filter. The results show that the PF provides sequentially very accurate estimates for the sulfate adsorption breakthrough curve.

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Section: Particle Filter Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Parameters and States Using a Bayesian Particle Filter for the Sulfate Ion Adsorption Process in a Fixed Bed Column

Carvalho

Guimarães

Leão

et al. 2019

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…This can cause the plant to operate outside of the desired conditions and, consequently, impair the product quality, process safety and final cost. One way to minimize the effects of uncertainty and lack of instruments is to use virtual sensors …”

Section: Introductionmentioning

confidence: 99%

Propylene Polymerization Reactor Control and Estimation Using a Particle Filter and Neural Network

Dias

Silva

Dutra

2017

Macro Reaction Engineering

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Polymeric materials are present in various industrial sectors and in daily life, presenting advantages such as low cost and durability. Several processes for manufacturing have been developed. To achieve safety and operational goals measurement methods for proper process monitoring and effective control are needed. However, in real polymer plants, measuring devices are subject to uncertainties and are not always available. Hence, this paper proposes a virtual sensor scheme based on a particle filter and artificial neural network (ANN) that is applied to a simulated polymerization reactor. This scheme reduces uncertainties and enables the observation of latent variables. The ANN is also used for predicting the final properties of the polymer. The goal is to provide controllers with more complete and improved information. The results show that the virtual sensor scheme improves the process control, providing accurate estimates and action times that are consistent with industrial sampling intervals, which highlights its potential for practical applications.

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“…Monte Carlo based approaches uses a significantly large number of random samples to represent the underlying densities. While this choice potentially yields a superior representation of the underlying densities, the computational burden for propagating the samples through the process models is excessively large and these approaches are often impractical for online implementation [15]. On the other hand, the Unscented Transformation (UT) [1] based approaches use a limited but deterministic choice of 2n + 1 samples (n -dimension of state vector), known as sigma points, to capture moments of the non-Gaussian densities.…”

Section: Introductionmentioning

confidence: 99%

Projection based constrained nonlinear state estimation using Gaussian sum filters

Kottakki

Bhushan

Bhartiya

2016

2016 Indian Control Conference (ICC)

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Constraint handling is an integral part of any practical state estimation procedure. Most current approaches to constrained state estimation ensure that the point estimate is feasible with respect to the constraints and are based on popular unconstrained approaches such as sampling based approaches such as Unscented Kalman Filter (UKF) [1], which use deterministically chosen set of sigma points. However, UKF is based on an implicit assumption that the conditional state densities at various steps are Gaussian. In a recent work [2], a new filter termed as Unscented Gaussian Sum Filter (UGSF) was proposed that represents the prior density as a Gaussian sum. It was shown, using simulation studies, that the UGSF outperforms UKF thereby demonstrating the advantage of using a non-Gaussian prior. In this work, we propose to extend UGSF to constrained UGSF by incorporation of constraints on the states in the estimation process. In particular, we propose to use Interval Constrained Unscented Transformation (ICUT) [3] and projection algorithm [4] with the UGSF framework. Implementation on the three state isothermal batch reactor case study [5] shows that the proposed constrained UGSF using the projection method outperforms constrained UKF while maintaining the computation effort similar to the constrained UKF version.

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Practical issues in state estimation using particle filters: Case studies with polymer reactors

Cited by 36 publications

References 27 publications

Estimation of Parameters and States Using a Bayesian Particle Filter for the Sulfate Ion Adsorption Process in a Fixed Bed Column

Estimation of Parameters and States Using a Bayesian Particle Filter for the Sulfate Ion Adsorption Process in a Fixed Bed Column

Propylene Polymerization Reactor Control and Estimation Using a Particle Filter and Neural Network

Projection based constrained nonlinear state estimation using Gaussian sum filters

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