Particle filters for state and parameter estimation in batch processes

Chen, Tao; Morris, Julian; Martin, E.B.

doi:10.1016/j.jprocont.2005.01.001

Cited by 147 publications

(111 citation statements)

References 18 publications

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“…The process shown in Figure 1 is a free-radical batch polymerization of methylmethacrylate (MMA) with a water solvent and benzoyl peroxide initiator (Chen et al [16]; Kiparissides et al [17]). The polymerization temperature is maintained .by a cascade control system through manipulating the flow rates of the hot and cold water streams.…”

Section: Batch Polymerization Processmentioning

confidence: 99%

Prediction from Uncertain Inputs for Partial Least Squares Regression

Chen¹,

Zhang²,

Ren³

et al. 2017

IJCA

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This paper presents two approaches to calculating the predictions for a partial least squares (PLS) regression model in the presence of uncertain inputs (predictors, covariates). Input uncertainty is a common issue associated with the practical application of PLS, such as in the use of multi-way PLS for the modeling of a batch process and the development of multi-step ahead predictions in time series. By assuming a multivariate normal distribution for the input variables, two methods are considered to accommodate the input uncertainty when calculating the mean and prediction intervals for a PLS model:Monte Carlo approximation and an analytical method. The proposed approaches are demonstrated for the prediction of the end-of-batch quality of a simulated batch polymerization process. 

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Section: Batch Polymerization Processmentioning

confidence: 99%

Prediction from Uncertain Inputs for Partial Least Squares Regression

Chen¹,

Zhang²,

Ren³

et al. 2017

IJCA

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“…Particle Filter s [3] ( PF) based methods: In this kind of method, various sophisticated stochastic process can be used to model the parameter process, such as random walk [4] , kernel smoothing [5,6] , time homogeneous Markov Chain [7][8][9] , etc. The PF based method has been widely applied in many cases in the last decade, however, a small number of particles may degrade the estimation performance of the PFs based methods.…”

Section: )mentioning

confidence: 99%

Interacting Multiple Gaussian Particle Filter

Qu¹

2013

Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information

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Abstract-Inspired by the framework of the Interacting Multiple Model (IMM), a method, called Interacting Multiple Gaussian Particle Filter (IMGPF), is proposed for solving the nonlinear Bayesian filtering problem with unknown continuous parameter. IMGPF regards the continuous parameter space as a union of disjoint subspaces, and each subspace is assigned to a model respectively. At each time step, for each model of IMGPF, under the assumption that the parameter belongs to the corresponding subspace, a Gaussian Particle Filter is applied to estimate the parameter and the state together. The parameter of each model of IMM is a fixed value, while the parameter of each model of IMGPF is a random variable need to be estimated. Thus IMGPF can achieve better estimation performance than IMM when the true parameter does not close to any element of the IMM model set. A simulation example of bearings only tracking problem is presented to demonstrate the effectiveness of IMGPF.

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“…The quality variables could be inferred through the mathematical modeling of the process based on physical and chemical mechanism [25,26]. However the development of mechanistic models require a deep understanding of the process and is typically time-consuming.…”

Section: Industrial Case Studymentioning

confidence: 99%

Bagging for Gaussian process regression

2009

Self Cite

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This paper proposes the application of bagging to obtain more robust and accurate predictions using Gaussian process regression models. The training data is re-sampled using the bootstrap method to form several training sets, from which multiple Gaussian process models are developed and combined through weighting to provide predictions. A number of weighting methods for model combination are discussed, including the simple averaging rule and the weighted averaging rules. We propose to weight the models by the inverse of their predictive variance, and thus the prediction uncertainty of the models is automatically accounted for. The bagging method for Gaussian process regression is successfully applied to the inferential estimation of quality variables in an industrial chemical plant.

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Particle filters for state and parameter estimation in batch processes

Cited by 147 publications

References 18 publications

Prediction from Uncertain Inputs for Partial Least Squares Regression

Prediction from Uncertain Inputs for Partial Least Squares Regression

Interacting Multiple Gaussian Particle Filter

Bagging for Gaussian process regression

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