A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals
Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/10/17864 |
| _version_ | 1797999510999269376 |
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| author | Dan Paulsson Robert Gustavsson Carl-Fredrik Mandenius |
| author_facet | Dan Paulsson Robert Gustavsson Carl-Fredrik Mandenius |
| author_sort | Dan Paulsson |
| collection | DOAJ |
| description | Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel. |
| first_indexed | 2024-04-11T11:05:48Z |
| format | Article |
| id | doaj.art-ee78077c70a84fd49e10b31fd03ceb32 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:05:48Z |
| publishDate | 2014-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ee78077c70a84fd49e10b31fd03ceb322022-12-22T04:28:21ZengMDPI AGSensors1424-82202014-09-011410178641788210.3390/s141017864s141017864A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat SignalsDan Paulsson0Robert Gustavsson1Carl-Fredrik Mandenius2Division of Biotechnology/IFM, Linköping University, Linköping 581 83, SwedenDivision of Biotechnology/IFM, Linköping University, Linköping 581 83, SwedenDivision of Biotechnology/IFM, Linköping University, Linköping 581 83, SwedenSoft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.http://www.mdpi.com/1424-8220/14/10/17864bioprocess controlbio-calorimetrysoftware sensorssoft sensor implementationbioprocess user interface |
| spellingShingle | Dan Paulsson Robert Gustavsson Carl-Fredrik Mandenius A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals Sensors bioprocess control bio-calorimetry software sensors soft sensor implementation bioprocess user interface |
| title | A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals |
| title_full | A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals |
| title_fullStr | A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals |
| title_full_unstemmed | A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals |
| title_short | A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals |
| title_sort | soft sensor for bioprocess control based on sequential filtering of metabolic heat signals |
| topic | bioprocess control bio-calorimetry software sensors soft sensor implementation bioprocess user interface |
| url | http://www.mdpi.com/1424-8220/14/10/17864 |
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