research-article

Soft-sensors based on Black-box Models for Bioreactors Monitoring and State Estimation

Authors:

Vygandas Vaitkus,

Kęstutis Brazauskas,

Jolanta RepšytėAuthors Info & Claims

ICBBT '20: Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology

Pages 43 - 47

https://doi.org/10.1145/3405758.3405780

Published: 10 July 2020 Publication History

Abstract

Efficient control of bioprocess is becoming more and more relevant in today's biotechnology industry and environment of changing technologies. Many of important bioprocess variables are not measured on-line. Usually it took 15 min and some of them only after 24 hours (Dry biomass). On-line estimation of unknown bioprocess parameters provide improved process control performance. In this article, three advanced techniques for soft-sensors design were investigated: support vector regression, relevance vector regression and random forest regression model. As direct measurements for estimation were used glucose/lactose feed rates and oxygen uptake rate along with its integrated quantity. Estimation quality of models analyzed in first stage was tested by using data of mechanistic process model. Later models were tested on E. coli BL21 (DE3) pET21-IFN-alfa-5 strain cultivation process measurements. As inputs were used: Time after induction, CPR (Carbon dioxide Production Rate) and Feeding flow along with its integrated values. Outputs of the models were: OD (Optical Density) and acetate quantity. All models provide results close to each other but random forest showed slightly better efficiency.

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Cited By

Acosta-Pavas JCorrales DMartínez-Menéndez IVillela SBouhaouala-Zahar BGeorgakilas GMexis KXenios SDalamagas TKokosis AO’donohue MFillaudeau LAceves-Lara C(2025)A Soft Sensing Approach for Efficient Monitoring of Nanobody-Based Scorpion Antivenom ProductionDistributed Computing and Artificial Intelligence, 21st International Conference10.1007/978-3-031-82073-1_27(271-281)Online publication date: 18-Feb-2025
https://doi.org/10.1007/978-3-031-82073-1_27
Todhunter MJubair SVerma RSaqe RShen KDuffy B(2024)Artificial intelligence and machine learning applications for cultured meatFrontiers in Artificial Intelligence10.3389/frai.2024.14240127Online publication date: 24-Sep-2024
https://doi.org/10.3389/frai.2024.1424012
Iglesias CRistovski MBolic MCuperlovic-Culf M(2023)rAAV Manufacturing: The Challenges of Soft Sensing during Upstream ProcessingBioengineering10.3390/bioengineering1002022910:2(229)Online publication date: 8-Feb-2023
https://doi.org/10.3390/bioengineering10020229
Show More Cited By

Index Terms

Soft-sensors based on Black-box Models for Bioreactors Monitoring and State Estimation
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation

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cover image ACM Other conferences

ICBBT '20: Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology

May 2020

163 pages

ISBN:9781450375719

DOI:10.1145/3405758

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NWPU: Northwestern Polytechnical University
Universidade Nova de Lisboa

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2020

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ICBBT 2020

ICBBT 2020: 2020 12th International Conference on Bioinformatics and Biomedical Technology

May 22 - 24, 2020

Xi'an, China

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Cited By

Acosta-Pavas JCorrales DMartínez-Menéndez IVillela SBouhaouala-Zahar BGeorgakilas GMexis KXenios SDalamagas TKokosis AO’donohue MFillaudeau LAceves-Lara C(2025)A Soft Sensing Approach for Efficient Monitoring of Nanobody-Based Scorpion Antivenom ProductionDistributed Computing and Artificial Intelligence, 21st International Conference10.1007/978-3-031-82073-1_27(271-281)Online publication date: 18-Feb-2025
https://doi.org/10.1007/978-3-031-82073-1_27
Todhunter MJubair SVerma RSaqe RShen KDuffy B(2024)Artificial intelligence and machine learning applications for cultured meatFrontiers in Artificial Intelligence10.3389/frai.2024.14240127Online publication date: 24-Sep-2024
https://doi.org/10.3389/frai.2024.1424012
Iglesias CRistovski MBolic MCuperlovic-Culf M(2023)rAAV Manufacturing: The Challenges of Soft Sensing during Upstream ProcessingBioengineering10.3390/bioengineering1002022910:2(229)Online publication date: 8-Feb-2023
https://doi.org/10.3390/bioengineering10020229
Lai GYu JWang JLi WLiu GWang ZGuo MTang Y(2023)Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 TApplied Microbiology and Biotechnology10.1007/s00253-023-12633-x107:17(5351-5365)Online publication date: 8-Jul-2023
https://doi.org/10.1007/s00253-023-12633-x
Lyubenova VKostov GDenkova-Kostova R(2021)Model-Based Monitoring of Biotechnological Processes—A ReviewProcesses10.3390/pr90609089:6(908)Online publication date: 21-May-2021
https://doi.org/10.3390/pr9060908

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