Modeling and optimization of acetic acid fermentation: A polynomial-based approach

Santos-Dueñas, Inés M.; Jiménez-Hornero, Jorge E.; Cañete-Rodríguez, Ana M.; García-García, Isidoro

doi:10.1016/j.bej.2015.03.002

Cited by 10 publications

(21 citation statements)

References 33 publications

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“…There are various models for acetification (particularly as regards the biotransformation step). Most are of the first-principles (mechanistic) [6,9] or black-box (polynomial regression) type [24]. Although first-principles models are the more complex, they tend to hold over broader operational ranges because they use available information about the process concerned.…”

Section: Modelling Acetificationmentioning

confidence: 99%

“…Acetic fermentation has been examined with the aid of various polynomial black-box models based on acetic acid production and the mean fermentation rate. Thus, Santos-Duenas et al [24] used the above-mentioned factors (viz., ethanol concentration at unloading time, volume to be unloaded at the end of a cycle and rate of raw substrate loading) at levels spanning the typical operational ranges for industrial processes in combination with an appropriate experimental design.…”

Section: Modelling Acetificationmentioning

confidence: 99%

“…First-principles and black-box models for acetification have been used under the operating conditions maximizing acetic acid production [24,31] and provided very similar results.…”

Section: Modelling Acetificationmentioning

confidence: 99%

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Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

2020

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Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling.

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Section: Modelling Acetificationmentioning

confidence: 99%

Section: Modelling Acetificationmentioning

confidence: 99%

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Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

2020

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“…Notwithstanding the previous difficulties, there is a wealth of technical experience and knowledge about the most suitable working methods and operating conditions for acetification. The use of non-segregated non-structured models [24] could be appropriated to model this kind of system; working with this type of approach allowed us to reach most of the knowledge on this particular process, which has allowed quantitative relations between operational variables and diverse industrial objective functions such as productivity to be established [8][9][10][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Previous modelling studies [7,10,30] led to proposing the use of two serially arranged bioreactors to optimize the outcome of the acetification process under operationally restricted conditions. Thus, vinegar production is most often subjected to strict regulations with regards to the properties of the end-product, which, for example, should contain very little or no ethanol.…”

Section: Introductionmentioning

confidence: 99%

Modelling of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

et al. 2020

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In the scope of a broader study about modelling wine acetification, the use of polynomial black-box models seems to be the best choice. Additionally, the use of two serially arranged bioreactors was expected to result in increased overall acetic acid productivity. This paper describes the experiments needed to obtain enough data for modelling the process and the use of second-order polynomials for this task. A fractional experimental design with central points was used with the ethanol concentrations during loading of the bioreactors, their operation temperatures, the ethanol concentrations at unloading time, and the unloaded volume in the first one as factors. Because using two serial reactors imposed some constraints on the operating ranges for the process, an exhaustive combinatorial analysis was used to identify a working combination of such ranges. The obtained models provided highly accurate predictions of the mean overall rate of acetic acid formation, the mean total production of acetic acid of the two-reactor system, and ethanol concentration at the time the second reactor is unloaded. The operational variables associated with the first bioreactor were the more strongly influential to the process, particularly the ethanol concentration at the time the first reactor was unloaded, the unloaded volume, and the ethanol concentration when loading.

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García-García¹,

García-Parrilla²,

Santos-Dueñas³

et al. 2020

Valorization of Fruit Processing by-Products

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Modeling and optimization of acetic acid fermentation: A polynomial-based approach

Cited by 10 publications

References 33 publications

Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Modelling of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

Strawberry

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