An Empirical Investigation of the Use of a Neural Network Committee for Identifying the Streptococcus Pneumoniae Growth Phases in Batch Cultivations

Horta, Antonio Carlos Luperni; Zangirolami, Teresa Cristina; Nicoletti, M. C.; Montera, Luciana; Carmo, Talita Souza; Gonçalves, Viviane Maimoni

doi:10.1007/978-3-540-69052-8_23

Cited by 4 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the absolute error of the prediction for each group was used to obtain the MAE. More information about this approach can be found elsewhere (Horta et al, 2008, 2014).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors

Mesquita

Campani

Giordano

et al. 2021

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

Various bio‐based processes depend on controlled micro‐aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro‐organism employed, while for industrial applications, there is no cost‐effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux‐based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome‐scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro‐aerobic fermentation strategy (MF‐ANN). The meta‐model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro‐aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro‐aeration strategies, including respiratory quotient‐based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF‐ANN provided volumetric ethanol productivity of 4.16 g·L−1·h−1 and a yield of 0.48 gethanol.gsubstrate−1, which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L−1·h−1 and 0.35–0.40 gethanol·gsubstrate−1, respectively). Due to its modular character, the MF‐ANN strategy could be adapted to other micro‐aerated bioprocesses.

show abstract

“…Then, the absolute error of the prediction for each group was used to obtain the MAE. More information about this approach can be found elsewhere (Horta et al, 2008, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…More information about this approach can be found elsewhere (Horta et al, 2008(Horta et al, , 2014. ), were used to adjust the air flow rate.…”

Section: Annsmentioning

confidence: 99%

Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors

Mesquita

Campani

Giordano

et al. 2021

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, it is possible to create a set of specific neural networks, where their combination in a committee would also allow interpolations of different values for the substrate composition. In a similar way, Horta et al [34] proposed a committee of neural networks for identifying Streptococcus pneumoniae growth phases, used for on-line state inference.…”

Section: Prediction Of Methane Production Using Specific Neural Networkmentioning

confidence: 99%

Mathematical Modeling and Computational Simulation Applied to the Study of Glycerol and/or Molasses Anaerobic Co-Digestion Processes

2023

View full text Add to dashboard Cite

An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab (v. 6.1.1), considering the Monod two-substrate with an intermediate (M2SI) kinetic model proposed by Rakmak et al. (Rakmak, N.; Noynoo, L.; Jijai, S.; Siripatana, C. Lecture Notes in Applied Mathematics and Applied Science in Engineering. Melaka, Malaysia, p. 11–20, 2019), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi–Sugeno approach available in the ANFIS package of Matlab. The biomethane production data simulated using Scilab were considered in neural network modeling and validation, firstly employing a “generic” network applicable to all eight scenarios, providing a very good fit (R2 > 0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition, again by using validation data generated by the M2SI model). The parameters of the M2SI model for the eight different conditions were also mapped using a neural network, as a function of the organic material composition, providing a fit with R2 > 0.99 when using 25 neurons. In the case of fuzzy logic, an RMSE (Root Mean Squared Error) of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks).

show abstract

“…The human experts, identified as HEX, classified each instance of the data sets into one of the phases, introducing a dummy response variable [28]. This new vector was formed by ''zeros,'' for the batch phase and ''ones'' for the feeding phase.…”

Section: Construction Of the Ai Systemmentioning

confidence: 99%

“…A detailed review about NN theory and NN committees can be found in Haykin [25]. There are several works available in the literature reporting the application of NNs to bioreactors, which include data classification, noise filtration and reaction rates inference [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations

Horta

Silva

Sargo

et al. 2011

Bioprocess Biosyst Eng

View full text Add to dashboard Cite

One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI) system, based on a neural network committee (NNC), which identifies the end of the batch phase. The AI system was successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm was validated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO2 molar fraction in the exhaust gas.

show abstract

An Empirical Investigation of the Use of a Neural Network Committee for Identifying the Streptococcus Pneumoniae Growth Phases in Batch Cultivations

Cited by 4 publications

References 6 publications

Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors

Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors

Mathematical Modeling and Computational Simulation Applied to the Study of Glycerol and/or Molasses Anaerobic Co-Digestion Processes

Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations

Contact Info

Product

Resources

About