Dissolved Oxygen Control of Batch Bioreactor using Model Reference Adaptive Control scheme

Chitra, M.; Pappa, N.; Abraham, Anuj

doi:10.1016/j.ifacol.2018.06.008

Cited by 15 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The stirrer speed is kept initially at 150 rev min −1 and is varied to a maximum of 800 rev min −1 during the experiment (Chitra et al . 2018) in order to maintain the dissolved oxygen above 35% throughout the batch/Fed‐batch process. The biomass concentration is found by measuring the OD of 10 ml sample culture using UV spectrophotometer (UV‐1800, Shimadzu) at 560 nm.…”

Section: Methodsmentioning

confidence: 99%

Application of deep neural techniques in predictive modelling for the estimation ofEscherichia coligrowth rate

2020

Self Cite

View full text Add to dashboard Cite

Aims To develop a predictive model for Escherichia coli using deep neural networks. Methods and Results Batch experiments are conducted at different temperatures closer to optimum value (36·5°C, 37°C, 37·5°C, 38°C and 38·5°C) to obtain the growth curves of E .coli K‐12. Two primary models namely modified Gompertz and new logistic are chosen. Three secondary models namely Gaussian, nonlinear autoregressive eXogenous (NARX) model and long short‐term memory (LSTM) are developed. The novelty in this paper is the development of secondary models using artificial neural network (ANN) and deep network. The performance measures chosen to compare the developed primary and secondary models are correlation coefficient (R2), root‐mean‐square error (RMSE) and accuracy factor (Af). Results show that modified Gompertz model has better R2 (0·99) and RMSE (0·019) when compared to new logistic model. Also, the deep network model outperforms other secondary models. Based on the primary and novel secondary model, a predictive model (tertiary model) is developed with improved accuracy and is validated. Conclusions The proposed predictive model exhibit good validation results in terms of RMSE and R2 values and can be applied for determining the growth rate of E. coli at a particular temperature value. Significance and Impact of the Study The proposed model can be used in food processing industries during enzyme production such as Chymosin, to predict the growth rate of E. coli as a function of temperature. Also, the developed LSTM and NARX models can be used to predict maximum specific growth rate of other microbial strains with proper training.

show abstract

Section: Methodsmentioning

confidence: 99%

Application of deep neural techniques in predictive modelling for the estimation ofEscherichia coligrowth rate

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, elevated oxygen levels SA production of (0.3 mol L −1 ) using glucose as the sole carbon substrate in the reactor was lower when compared to the mixture of sugars (data not shown). When a reactor is run in batch mode, important process variables, (e.g., cell mass, pH, dissolved oxygen (DO), substrate concentration) may vary significantly [57]. If the dissolved oxygen level is kept constant, the aeration efficiency can be used as an indicator of the biological activity and it may also be beneficial for process supervision.…”

Section: Fermentation In the Bench-top Fermenters (B-tfs)mentioning

confidence: 99%

Well Knowledge of the Physiology of Actinobacillus succinogenes to Improve Succinic Acid Production

Salma

Djelal

Abdallah

et al. 2021

Applied Microbiology

View full text Add to dashboard Cite

The anaerobic fermentation of glucose and fructose was performed by Actinobacillus succinogenes 130Z in batch mode using three different volume of bioreactors (0.25, 1 and 3 L). The strategy used was the addition of MgCO3 and fumaric acid (FA) as mineral carbon and the precursor of succinic acid, respectively, in the culture media. Kinetics and yields of succinic acid (SA) production in the presence of sugars in a relevant synthetic medium were investigated. Work on the bench scale (3 L) showed the best results when compared to the small anaerobic reactor's succinic acid yield and productivity after 96 h of fermentation. For an equal mixture of glucose and fructose used as substrate at 0.4 mol L−1 with the addition of FA as enhancer and under proven optimal conditions (pH 6.8, T = 37 °C, anaerobic condition and 1% v/v of biomass), about 0.5 mol L−1 of SA was obtained, while the theoretical production of succinic acid was 0.74 mol L−1. This concentration corresponded to an experimental yield of 0.88 (mol-C SA/mol-C sugars consumed anaerobically) and a volumetric productivity of 0.48 g-SA L−1 h−1. The succinic acid yield and concentration obtained were significant and in the order of those reported in the literature.

show abstract

“…In most cases, dissolved oxygen is monitored, as one of the significant parameters in an aerobic fermentation process. Its control is difficult to achieve, due to variations in process dynamics during both batch and fed-batch processes [5].…”

Section: Current State and Motivationmentioning

confidence: 99%

Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

2020

View full text Add to dashboard Cite

This paper presents the advanced control theory’s original utilisation to realise a system that controls the fermentation process in batch bioreactors. Proper fermentation control is essential for quality fermentation products and the economical operation of bioreactors. Batch bioreactors are very popular due to their simple construction. However, this simplicity presents limitations in implementing control systems that would ensure a controlled fermentation process. Batch bioreactors do not allow the inflow/outflow of substances during operation. Therefore, we have developed a control system based on a stirrer drive instead of material flow. The newly developed control system ensures tracking of the fermentation product time course to the reference trajectory by changing the stirrer’s speed. Firstly, the paper presents the derivation of the enhanced mathematical model suitable for developing a control system. A linearisation and eigenvalue analysis of this model were made. Due to the time-consuming determination of the fermentation model and the variation of the controlled plant during operation, the use of adaptive control is advantageous. Secondly, a comparison of different adaptive approaches was made. The model reference adaptive control was selected on this basis. The control theory is presented, and the control realisation described. Experimental results obtained with the laboratory batch bioreactor confirm the advantages of the proposed adaptive approach compared to the conventional PI-control.

show abstract

Dissolved Oxygen Control of Batch Bioreactor using Model Reference Adaptive Control scheme

Cited by 15 publications

References 12 publications

Application of deep neural techniques in predictive modelling for the estimation ofEscherichia coligrowth rate

Application of deep neural techniques in predictive modelling for the estimation ofEscherichia coligrowth rate

Well Knowledge of the Physiology of Actinobacillus succinogenes to Improve Succinic Acid Production

Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

Contact Info

Product

Resources

About