Artificial neural networks as a biomass virtual sensor for a batch process

Ascencio, R.R. Leal; Reynaga, F.; Gschaedler, Anne Christine

doi:10.1109/isic.2001.971516

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…Recently [15] publicized an interesting work about the applications of recent observers to chemical process systems and classify them into six classes, which differentiate them with respect to their features and assists in the design of observers. In [9] and [16] biomass estimation in a bioprocess is performed by the use of different kinds of Neural Networks, and later using sensorial fusion techniques. [17] worked on variables prediction in batch processes, by the use of subspaces identifications techniques.…”

Section: Introductionmentioning

confidence: 99%

Substrate Feeding Strategy Integrated with a Biomass Bayesian Estimator for a Biotechnological Process

Amicarelli

Montoya

Sciascio

2016

International Journal of Chemical Reactor Engineering

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This work proposes a substrate feeding strategy for a bioprocess integrated with a biomass estimator based in nonlinear filtering techniques. The performance of the proposed estimator and the substrate strategy are illustrated for the δ-endotoxin production of Bacillus thuringiensis (Bt) in batch and fed batch cultures. Nonlinear filtering techniques constitutes an adequate option as estimation tool because of the strongly nonlinear dynamics of this bioprocess and also due to nature of the uncertainties and perturbations that cannot be supposed Gaussians distributed. Biomass estimation is performed from substrate and dissolved oxygen. Substrate feeding strategy is intended to obtain high product concentration. Simulations results along with their experimental verifications demonstrate the acceptable performance of the proposed biomass estimator and the substrate feeding strategy.

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Section: Introductionmentioning

confidence: 99%

Substrate Feeding Strategy Integrated with a Biomass Bayesian Estimator for a Biotechnological Process

Amicarelli

Montoya

Sciascio

2016

International Journal of Chemical Reactor Engineering

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“…From this area, in recent years, the artificial neural network (ANN) methodology has become one of the most important techniques applied to biomass estimation, e.g. and references therein. Neal's work on Bayesian learning for neural networks shows that many Bayesian regression models based on neural networks converge to a class of probability distributions known as Gaussian processes according as the number of hidden neurons tends to infinity.…”

Section: Introductionmentioning

confidence: 99%

Behavior comparison for biomass observers in batch processes

Amicarelli

Quintero

Sciascio

2013

Asia-Pacific J Chem Eng

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On‐line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on‐line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ‐endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression‐based estimator, an artificial neural networks‐based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.

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A Novel Fermentation Control Method Based on Neural Networks

Yang

Sun

2004

Lecture Notes in Computer Science

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Artificial neural networks as a biomass virtual sensor for a batch process

Cited by 9 publications

References 3 publications

Substrate Feeding Strategy Integrated with a Biomass Bayesian Estimator for a Biotechnological Process

Substrate Feeding Strategy Integrated with a Biomass Bayesian Estimator for a Biotechnological Process

Behavior comparison for biomass observers in batch processes

A Novel Fermentation Control Method Based on Neural Networks

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