Moving horizon state estimation with global convergence using interval techniques: application to biotechnological processes

Valdés-González, Héctor; Flaus, Jean Marie; Acuña, Gonzalo

doi:10.1016/s0959-1524(02)00060-4

Cited by 28 publications

(11 citation statements)

References 19 publications

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“…Some examples of these techniques employing "a priori" information on states are: interval observers (Dochain, 2003) and moving horizon state estimation (Valdes-González et. al., 2003).…”

Section: Physical Constraints Of the State Vectormentioning

confidence: 99%

Differential Neural Networks Observers: Development, Stability Analysis and Implementation

García¹,

Poznyak²,

Chaírez³

et al. 2008

Systems Structure and Control

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“…Some examples of these techniques employing "a priori" information on states are: interval observers (Dochain, 2003) and moving horizon state estimation (Valdes-González et. al., 2003).…”

Section: Physical Constraints Of the State Vectormentioning

confidence: 99%

Differential Neural Networks Observers: Development, Stability Analysis and Implementation

García¹,

Poznyak²,

Chaírez³

et al. 2008

Systems Structure and Control

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“…MHEs are very well established as state estimators for chemical processes, as they can handle nonlinearities in the process dynamics and state constraints that are inherent to most reaction systems in a fairly straightforward manner [17]. This type of algorithm is very versatile and it has been applied to different processes, from solid and submerged fermentations [18,19], metabolic fluxes estimation [20], and lipids production [21]. Thus, it is a good candidate for application in the enzymatic hydrolysis of biomass.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

et al. 2020

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Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filter weights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%.

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“…To overcome this difficulty, some interval observation based algorithms have been proposed for state estimation (see e.g. [22,4]). The same problem arises in the sensor fusion setting.…”

Section: Introductionmentioning

confidence: 99%

Imprecise expectations for imprecise linear filtering

Rico¹,

Strauss²

2010

International Journal of Approximate Reasoning

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In the last 10 years, there has been increasing interest in interval valued data in signal processing. According to the conventional view, an interval value supposedly reflects the variability of the observation process. Generally, the considered variability is associated with either random noise or the uncertainty that underlies the observation process. In most sensor measure based applications, the raw sensor signal has to be processed by an appropriate filter to increase the signal to noise ratio or simply to recover the signal to be measured. In both cases, the output filter is obtained by convoluting the sensor signal with a supposedly known appropriate impulse response. However, in many real life situations, this impulse response cannot be precisely specified. The filtered value can thus be considered as biased by this arbitrary choice of one impulse response among all possible impulse responses considered in this specific context. In this paper, we propose a new approach to perform filtering that aims at computing an interval-valued signal that contains all outputs of filtering processes involving a coherent family of conventional linear filters. This approach is based on a very straightforward extension of the expectation operator involving appropriate concave capacities.

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Moving horizon state estimation with global convergence using interval techniques: application to biotechnological processes

Cited by 28 publications

References 19 publications

Differential Neural Networks Observers: Development, Stability Analysis and Implementation

Differential Neural Networks Observers: Development, Stability Analysis and Implementation

Estimation of Biomass Enzymatic Hydrolysis State in Stirred Tank Reactor through Moving Horizon Algorithms with Fixed and Dynamic Fuzzy Weights

Imprecise expectations for imprecise linear filtering

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