Practical issues in the application of the particle filter for estimation of chemical processes

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

doi:10.3182/20110828-6-it-1002.03272

Cited by 4 publications

(5 citation statements)

References 14 publications

(22 reference statements)

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“…This structure is applied to each inequality separately. Proof of the first inequality: From Relations (8) and (10) it follows that for all t 1:N and y(t 1:N ), V N (t 1:N ; y(t 1:N )) = F N (t 1:N ; y(t 1:N )).…”

Section: Appendix Proof Of Theoremmentioning

confidence: 96%

“…Particle filtering is a popular approach to estimate the state of such systems from a set of noisy measurements [4]. This tool has been largely used, among others, in computer vision [5]- [7], positioning and navigation [8], [9], chemistry [10], [11], mechanics [12], robotics [13], and medicine [14]. In practice, performing measurements may be difficult due to energy The authors are with the ICTEAM, Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium (e-mail: antoine.aspeel@uclouvain.be; amaury.gouverneur@ student.uclouvain.be; raphael.jungers@uclouvain.be; benoit.macq@uclouvain.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Intermittent Particle Filter

Aspeel

Gouverneur

Jungers

et al. 2022

IEEE Trans. Signal Process.

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Section: Appendix Proof Of Theoremmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Optimal Intermittent Particle Filter

Aspeel

Gouverneur

Jungers

et al. 2022

IEEE Trans. Signal Process.

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“…Particle filtering is a popular approach to estimate the state of such systems from a set of noisy measurements [4]. This tool has been largely used, among others, in computer vision [5], [6], [7], positioning and navigation [8], [9], chemistry [10], [11], mechanics [12], robotics [13], and medicine [14]. In practice, performing measurements may be difficult due to energy consumption, economic constraints, or health hazards.…”

Section: Introductionmentioning

confidence: 99%

Optimal Intermittent Particle Filter

Aspeel,

Gouverneur,

Jungers

et al. 2022

Preprint

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The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal intermittent filters, whose optimality criteria depend on the information available at the time of decision making. For the first, the stochastic program filter, the measurement times are given by a policy that determines whether a measurement should be taken based on the measurements already acquired. The second, called the offline filter, determines all measurement times at once by solving a combinatorial optimization program before any measurement acquisition. For the third one, which we call online filter, each time a new measurement is received, the next measurement time is recomputed to take all the information that is then available into account. We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter. However, these filters are generally intractable. For this reason, the filter estimate is approximated by a particle filter. Moreover, the mean square error is approximated using a Monte-Carlo approach, and different optimization algorithms are compared to approximately solve the combinatorial programs (a random trial algorithm, greedy forward and backward algorithms, a simulated annealing algorithm, and a genetic algorithm). Finally, the performance of the proposed methods is illustrated on two examples: a tumor motion model and a common benchmark for particle filtering.

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“…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%

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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Practical issues in the application of the particle filter for estimation of chemical processes

Cited by 4 publications

References 14 publications

Optimal Intermittent Particle Filter

Optimal Intermittent Particle Filter

Optimal Intermittent Particle Filter

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

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