Control of dissolved oxygen concentration using neural network in a batch bioreactor

Mete, Tufan; Özkan, Gülay; Hapoğlu, Hale; Alpbaz, Mustafa

doi:10.1002/cae.20430

Cited by 18 publications

(10 citation statements)

References 19 publications

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“…Conventional strategies in aerobic processes are essentially based on measurements of the dissolved oxygen concentration (DOC), which is then controlled by adjusting the stirrer speed and, occasionally, the composition of the gas supplied to the reactor (for instance, enrichment with pure oxygen) or the total gas flow rate [2, 3]. Some improvements have been made over time including more refined DOC controls through the use of mechanistic models [3] or neural networks [4], to mention a few.…”

Section: Introductionmentioning

confidence: 99%

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

et al. 2019

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Background Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation. Results The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae , defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L −1 h −1 ), with a final ethanol concentration of 87 g L −1 and yield of 0.46 g ethanol g substrate −1 . The other fermentation strategies showed lower yields (close to 0.40 g ethanol g substrate −1 ) and ethanol productivity around 4.0 g L −1 h −1 . Conclusion The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.

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Section: Introductionmentioning

confidence: 99%

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

et al. 2019

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“…ANN provide to learn system properties of many chemical applications. Although the history of ANN is old, studies have shown how neural networks can be used for classification, estimation and prediction of bioprocesses since the late of 1980s 10,16) . The structure of ANN, consisting of layers made of neurons, simulates the functioning of human brain 15) .…”

Section: Discussionmentioning

confidence: 99%

“…The structure of ANN, consisting of layers made of neurons, simulates the functioning of human brain 15) . There are many neural network applications in bioprocesses, such as surface roughness, surface hardness and wear prediction of materials 10) . However, no research on the surface roughness and hardness prediction model of dental materials has been reported.…”

Section: Discussionmentioning

confidence: 99%

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The accuracy of the prediction models for surface roughness and micro hardness of denture teeth

Deniz

Özkan

2019

Dent. Mater. J.

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The paper aimed to compare the performance of artificial neural network (ANN) model with the results of in vitro experiments. For these experiments, maxillary molars of four different denture teeth were subjected to tea, coffee, cola, cherry juice, distilled water. Vickers microhardness and surface roughness values were measured. Subsequently, ANN model for the prediction of microhardness and surface roughness of different denture teeth were examined. A back-propagation ANN has been used to develop a model relating to the amount of microhardness and surface roughness. The independent variables of the model are distilled water, tea, filtered coffee, cola, cherry juice, time and denture teeth. Microhardness and surface roughness were chosen as the dependent variables. According to the results, a neural network architecture having one input layer with ten neurons, two hidden layers with six neurons, one output layer with two neurons and an epoch size of 48 gives better prediction. Prediction models for dental materials could also be supportive for in vitro studies.

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“…Demonstrations of the performances of classical controllers (such as PID controllers in this study) on the electromechanical system can be given in undergraduate automatic control theory courses for aerospace engineering major at Beihang University School of Astronautics. It should be noted that the controller is open for development, and many advanced control approaches (sliding mode control , fuzzy control , and neural network control ) can be studied in the future by using the proposed HIL simulation system HIL‐GSS . On the other hand, HIL‐GSS can also serve as an experiment setup for graduate level courses (missile guidance theory and parameter identification ) at Beihang University School of Astronautics.…”

Section: Discussionmentioning

confidence: 99%

Development of an educational interactive hardware‐in‐the‐loop missile guidance system simulator

Chen

Liu

et al. 2017

Comp Applic In Engineering

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This paper presents a hardware‐in‐the‐loop (HIL) simulator, named HIL‐GSS, which aims to help the students of aerospace engineering learn control and missile guidance theories effectively by using interactive instrumentation. Electromechanical components and interactive panels are illustrated. Two case‐based studies are presented to show the advantages that HIL‐GSS offers to engineering education. Steady‐state tracking errors of the PID controller based on the HIL‐GSS are around zero. The final miss distance in the real‐time HIL TV guidance engagement simulation is 0.2376 m. Those results show the reliability of the proposed HIL simulator. In addition, the students' average response to survey questions is 4.22 (1–5 grade scale), which indicates that students strongly agree or agree that HIL‐GSS is an effective educational tool.

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Control of dissolved oxygen concentration using neural network in a batch bioreactor

Cited by 18 publications

References 19 publications

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

The accuracy of the prediction models for surface roughness and micro hardness of denture teeth

Development of an educational interactive hardware‐in‐the‐loop missile guidance system simulator

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