An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes

Survyla, Arnas; Levišauskas, Donatas; Urniezius, Renaldas; Simutis, Rimvydas

doi:10.1016/j.csbj.2021.10.015

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…The stationary phase takes place due to the depletion of nutrients and the presence of toxic substrate in the biological treatment process. OUR is a useful parameter which can be used to monitor the health of microorganisms hence, it has been used to monitor the physiological state of microorganisms in the biological treatment process [ 92 ].…”

Section: Resultsmentioning

confidence: 99%

Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process

Muloiwa

Dinka

Nyende‐Byakika

2023

Engineering in Life Sciences

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The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to model biomass growth. Three metrics: coefficient of determination ( R 2 ), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the model. Sensitivity analysis was applied to confirm variables that have a strong influence on biomass growth. The results of the study showed that MLP ANN algorithm was able to model biomass growth successfully. R 2 values were 0.844, 0.853, and 0.823 during training, validation, and testing phases, respectively. RMSE values were 0.7476, 1.1641, and 0.7798 during training, validation, and testing phases respectively. MSE values were 0.5589, 1.3551, and 0.6081 during training, validation, and testing phases, respectively. Sensitivity analysis results showed that temperature (47.2%) and dissolved oxygen (DO) concentration (40.2%) were the biggest drivers of biomass growth. Aeration period (4.3%), chemical oxygen demand (COD) concentration (3.2%), and oxygen uptake rate (OUR) (5.1%) contributed minimally. The biomass growth model can be applied at different wastewater treatment plants by different plant managers/operators in order to achieve optimum biomass growth. The optimum biomass growth will improve the removal of organic and inorganic matter in the biological treatment process.

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Section: Resultsmentioning

confidence: 99%

Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process

Muloiwa

Dinka

Nyende‐Byakika

2023

Engineering in Life Sciences

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“…

BL21 (DE3) pET21-IFN-alfa-5 bacteria were grown in a minimal mineral medium, and the cultivation process protocol, as described in the research of [37] , is shown in Table 1 . The oxygen concentrations' online measurements, ranging from 0 to 100%, were retrieved using the BlueSens BlueInOne Ferm gas analyzer for oxygen uptake rate evaluation.…”

Section: Methodsmentioning

confidence: 99%

Adaptive control of the E. coli-specific growth rate in fed-batch cultivation based on oxygen uptake rate

Urniezius,

Masaitis,

Levisauskas

et al. 2023

Computational and Structural Biotechnology Journal

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“…Previous studies showed that specific growth rate µ is one of the best descriptors for estimating bioprocess parameters [10]. This parameter and oxygen uptake rate (OUR) carry much information about the growth and life of the cell [11,12]. The latter is estimated using a soft-sensor with off-gas information.…”

Section: Input Selectionmentioning

confidence: 99%

Non-Invasive Estimation of Acetates Using Off-Gas Information for Fed-Batch E. coli Bioprocess

Matukaitis

Masaitis

Urniezius

et al. 2022

The 1st International Electronic Conference on Processes: Processes System Innovation

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An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes

Abstract: Graphical abstract

Cited by 8 publications

References 33 publications

Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process

Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process

Adaptive control of the E. coli-specific growth rate in fed-batch cultivation based on oxygen uptake rate

Non-Invasive Estimation of Acetates Using Off-Gas Information for Fed-Batch E. coli Bioprocess

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