A Hybrid Soft Sensor Approach Combining Partial Least-Squares Regression and an Unscented Kalman Filter for State Estimation in Bioprocesses
Պահպանված է:
| Հրատարակված է: | Bioengineering vol. 12, no. 6 (2025), p. 654 |
|---|---|
| Հիմնական հեղինակ: | |
| Այլ հեղինակներ: | |
| Հրապարակվել է: |
MDPI AG
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| Խորագրեր: | |
| Առցանց հասանելիություն: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ցուցիչներ: |
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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|---|---|---|---|
| 001 | 3223876903 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5354 | ||
| 024 | 7 | |a 10.3390/bioengineering12060654 |2 doi | |
| 035 | |a 3223876903 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Lucas, Hermann | |
| 245 | 1 | |a A Hybrid Soft Sensor Approach Combining Partial Least-Squares Regression and an Unscented Kalman Filter for State Estimation in Bioprocesses | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Real-time information on key state variables during fermentation is crucial for the effective optimization and control of bioprocesses. Specialized sensors for online or at-line monitoring of these variables are often associated with high costs, especially during early-stage process optimization. In this study, fed-batch processes of an L-phenylalanine (L-phe) production process were carried out using a recombinant Escherichia coli strain under varying inducer concentrations. The available online process variables from the L-phe production process were used to estimate the state variables biomass, glycerol, L-phe, acetate, and L-tyrosine (L-tyr) via partial least-squares regression (PLSR). These predictions were then incorporated as measurements into an unscented Kalman filter (UKF). The filter uses a coarse-grained model as a state estimator, which, in addition to extracellular variables, also provides information on intracellular states. The results of PLSR showed very good prediction accuracy for L-phe, moderate accuracy for glycerol, biomass, and L-tyr and poor performance for acetate concentrations. In combination with the UKF, the estimation of the L-phe concentrations was greatly improved compared to the CGM, whereas further improvement is still needed for the remaining state variables. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Process variables | ||
| 653 | |a Glycerol | ||
| 653 | |a Biomass | ||
| 653 | |a Least squares method | ||
| 653 | |a State estimation | ||
| 653 | |a Measurement techniques | ||
| 653 | |a Batch culture | ||
| 653 | |a E coli | ||
| 653 | |a Kalman filters | ||
| 653 | |a Batch processing | ||
| 653 | |a Proteins | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Phenylalanine | ||
| 653 | |a Knowledge | ||
| 653 | |a Sensors | ||
| 653 | |a State variable | ||
| 653 | |a Optimization | ||
| 653 | |a Tyrosine | ||
| 653 | |a Acetic acid | ||
| 653 | |a Fermentation | ||
| 700 | 1 | |a Kremling, Andreas | |
| 773 | 0 | |t Bioengineering |g vol. 12, no. 6 (2025), p. 654 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223876903/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223876903/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223876903/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |