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Allampalli SSP, Sivaprakasam S. Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality. World J Microbiol Biotechnol 2024; 40:196. [PMID: 38722368 DOI: 10.1007/s11274-024-03993-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.
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Affiliation(s)
- Satya Sai Pavan Allampalli
- BioPAT Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India
| | - Senthilkumar Sivaprakasam
- BioPAT Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India.
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Urniezius R, Masaitis D, Levisauskas D, Survyla A, Babilius P, Godoladze D. Adaptive control of the E. coli-specific growth rate in fed-batch cultivation based on oxygen uptake rate. Comput Struct Biotechnol J 2023; 21:5785-5795. [PMID: 38213900 PMCID: PMC10781999 DOI: 10.1016/j.csbj.2023.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/17/2023] [Accepted: 11/17/2023] [Indexed: 01/13/2024] Open
Abstract
In this study, an automatic control system is developed for the setpoint control of the cell biomass specific growth rate (SGR) in fed-batch cultivation processes. The feedback signal in the control system is obtained from the oxygen uptake rate (OUR) measurement-based SGR estimator. The OUR online measurements adapt the system controller to time-varying operating conditions. The developed approach of the PI controller adaptation is presented and discussed. The feasibility of the control system for tracking a desired biomass growth time profile is demonstrated with numerical simulations and fed-batch culture E . c o l i control experiments in a laboratory-scale bioreactor. The procedure was cross-validated with the open-loop digital twin SGR estimator, as well as with the adaptive control of the SGR, by tracking a desired setpoint time profile. The digital twin behavior statistically showed less of a bias when compared to SGR estimator performance. However, the adaptation-when using first principles-was outperformed 30 times by the model predictive controller in a robustness check scenario.
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Affiliation(s)
- Renaldas Urniezius
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Deividas Masaitis
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Donatas Levisauskas
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Arnas Survyla
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Povilas Babilius
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
| | - Dziuljeta Godoladze
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367 Kaunas, Lithuania
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Muloiwa M, Dinka M, Nyende‐Byakika S. Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process. Eng Life Sci 2023; 23:e2200058. [PMID: 37153027 PMCID: PMC10158627 DOI: 10.1002/elsc.202200058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/05/2023] [Accepted: 03/12/2023] [Indexed: 05/09/2023] Open
Abstract
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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Affiliation(s)
- Mpho Muloiwa
- Department of Civil EngineeringTshwane University of TechnologyPretoriaSouth Africa
| | - Megersa Dinka
- Department of Civil Engineering ScienceUniversity of JohannesburgJohannesburgSouth Africa
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Survyla A, Urniezius R, Simutis R. Viable cell estimation of mammalian cells using off-gas-based oxygen uptake rate and aging-specific functional. Talanta 2023; 254:124121. [PMID: 36462281 DOI: 10.1016/j.talanta.2022.124121] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 11/27/2022]
Abstract
This study developed an estimation routine for counting the viable cells in an in vitro fed-batch Chinese hamster ovary cultivation that relies on off-gas information and inlet gas mixture knowledge. We computed the oxygen uptake rate bound to the bioreactor exhaust gas outlet when the inlet gas mixture was stationary. Our mammalian biosynthesis analysis determined the stoichiometric parameters as a function of the average population age. We cross-validated an identical algorithm for mammalian and microbial cultivations and found that the' 99% confidence band of the model generally overlapped with the error bars defined from observations. The resulting RMSE and MAE averages were 0.188 and 0.14e9cells L-1, respectively, when estimating the viable mammalian cell count. The validation for the estimation of total bacterial biomass yielded an MAE and RMSE of 1.78 g L-1 and 2.53 g L-1, respectively. Moreover, our proposed approach provides an online estimation of the average population age for both aerobically cultivated microorganisms.
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Affiliation(s)
- Arnas Survyla
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367, Kaunas, Lithuania
| | - Renaldas Urniezius
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367, Kaunas, Lithuania.
| | - Rimvydas Simutis
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367, Kaunas, Lithuania
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Cheng Y, Bi X, Xu Y, Liu Y, Li J, Du G, Lv X, Liu L. Artificial intelligence technologies in bioprocess: Opportunities and challenges. BIORESOURCE TECHNOLOGY 2023; 369:128451. [PMID: 36503088 DOI: 10.1016/j.biortech.2022.128451] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/01/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.
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Affiliation(s)
- Yang Cheng
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yameng Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China.
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