Hybrid metabolic flux analysis/artificial neural network modeling of bioprocesses

Teixeira, Augusto; Alves, Carlos Filipe Pereira; Alves, Paula M.; Carrondo, Manuel J.T.; Oliveira, Rui

doi:10.1109/ichis.2005.59

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…The most widely used approach in hybrid modeling of bio-processing systems is based on the conservation equations for the bioreactor combined with reaction rate kinetics approximated by artificial neural networks (Oliveira, 2004). In this way, simpler mechanistic descriptions and neural networks can be combined to construct a hybrid (grey) model for the complex system (De Azevado and Oliveira, 1997;Harada et al, 2002;James et al, 2002;Oliveira, 2004;Teixeira et al, 2005;Valencia et al, 2007;Meleiro et al, 2009). A simple representation of grey box modeling for simulation of biochemical processes is shown in Figure 6.…”

Section: Hybrid Modeling Scheme Based On Anns As a Reduced Form Of Dfbamentioning

confidence: 99%

“…Eslamloueyan and P. Setoodeh data-based estimated parameters, D is the dilution rate, and u is a vector of volumetric control inputs (Oliveira, 2004;Teixeira et al, 2005). There is a rich literature on applying feed-forward neural networks in classification, estimation, and control problems related to chemical and biochemical engineering systems (Karim et al, 1997;Warnes et al, 1998;Renotte et al, 2001;Nagy, 2007;Arpornwichanop and Shomchoam, 2009).…”

mentioning

confidence: 99%

“…According to Cybenko Figure 6. Hybrid modeling structure for bioprocess simulation (Teixeira et al, 2005). …”

mentioning

confidence: 99%

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Optimization of Fed-Batch Recombinant Yeast Fermentation for Ethanol Production Using a Reduced Dynamic Flux Balance Model Based on Artificial Neural Networks

Eslamloueyan

Setoodeh

2011

Chemical Engineering Communications

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In this work, a reduced form of dynamic flux balance model based on artificial neural networks for batch and fed-batch fermentation of xylose-utilizing engineered Saccharomyces cerevisiae RWB 218 is developed. The intracellular description of carbon metabolism in the model is simulated by a multilayer-perceptron (MLP) network. First, this hybrid model is compared to the full mechanistic dynamic flux balance analysis (DFBA) in terms of accuracy and computational time regarding available experimental data on anaerobic batch cultivation. Afterwards, it is used in a model-based sequential dynamic optimization procedure in order to maximize ethanol productivity. The initial liquid volume charged in the bioreactor, the feed flow rates in aerobic and anaerobic conditions, the final batch time, and the switching time from aerobic to anaerobic conditions are considered as decision variables. Differential evolution (DE), as a robust and efficient optimization method, is employed to solve the problem for several glucose mass fractions in the feed stream. The results show that in similar conditions a small deviation in each operating parameter from its optimal value may lead to considerable decrease in ethanol productivity.

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Section: Hybrid Modeling Scheme Based On Anns As a Reduced Form Of Dfbamentioning

confidence: 99%

mentioning

confidence: 99%

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Optimization of Fed-Batch Recombinant Yeast Fermentation for Ethanol Production Using a Reduced Dynamic Flux Balance Model Based on Artificial Neural Networks

Eslamloueyan

Setoodeh

2011

Chemical Engineering Communications

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“…As introduction it is necessary to mention applications as mathematical modelling of bioprocesses in[1], [2], prediction models and control of boilers, furnaces and turbines in [3] or industrial ANN control of calcinations procedures and iron ore processes [4].…”

Section: Introductionmentioning

confidence: 99%

Piecewise-Linear Neural Model for Helicopter Elevation Control

Doležel¹,

Havlicek²,

Mareš³

2012

CONTROL

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The contribution is focused on helicopter elevation control. The experiments are performed on helicopter model, which is significantly nonlinear plant and it simulates in some simplified way dynamics of real helicopter. There is derived new way to control it which uses piecewise-linear neural model. As it is shown at the end of the paper, this new control algorithm brings decent performance improvement compared to certain classical control technique.

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“…As introduction, some concrete examples of successful application of ANN can be mentioned, e.g. mathematical modeling of bioprocesses [Montague et al, 1994], [Teixeira et al, 2005], prediction models and control of boilers, furnaces and turbines [Lichota et al, 2010] or industrial ANN control of calcinations processes, or iron ore process [Dwarapudi, et al, 2007]. Specifically in our paper, the aim is to explain and describe usage of neural network in the case of nonlinear reactor furnace control.…”

Section: Introductionmentioning

confidence: 99%