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Observability- and Identifiability-Guided Sensor-Set Design for Digital-Twin-Assisted Consolidated Bioprocessing
Mark Korang Yeboah1,2, Nana Yaw Asiedu2, Ahmad Addo2
1Chair of Dynamics and Control, University of Duisburg-Essen, Lotharstraße, 47057 Duisburg, Germany.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
Designing effective sensor sets is crucial for monitoring consolidated bioprocessing (CBP). The study found that combining ethanol, sugar, biomass, and substrate sensors offers the best performance for digital twin monitoring of CBP processes.
Area of Science:
- Biotechnology
- Chemical Engineering
- Process Monitoring
Background:
- Consolidated bioprocessing (CBP) involves simultaneous enzyme production, lignocellulose degradation, sugar release, and fermentation, making it difficult to monitor.
- Sparse measurements, feedstock variability, and model mismatches further complicate real-time process tracking.
Purpose of the Study:
- To develop a computational framework for designing optimal sensor sets for digital-twin-assisted CBP monitoring.
- To evaluate different sensor measurement packages for their effectiveness in state and parameter estimation.
Main Methods:
- A five-state virtual plant model was developed, including active biomass, enzyme activity, residual substrate, soluble sugar, and ethanol.
- Sixteen measurement packages were evaluated using sensitivity analysis, state-observability, parameter-identifiability, and unscented Kalman filter soft-sensing.
- Performance was assessed based on log-pseudodeterminant values, root-mean-square error (RMSE), and a combined score considering sensor value and burden.
Main Results:
- Ethanol-only sensing provided the weakest support for CBP digital twin reconstruction.
- The ethanol-sugar-biomass-substrate sensor package demonstrated strong state-observability and parameter-identifiability, outperforming others in robustness tests.
- Full-proxy monitoring (all five states) and ethanol-sugar-biomass-substrate offered the best soft-sensing performance, significantly reducing RMSE.
Conclusions:
- The ethanol-sugar-biomass-substrate sensor package is recommended as the best overall choice for CBP digital twin implementation due to its balance of performance and sensor burden.
- The proposed simulation-based framework effectively prioritizes informative measurement packages for practical CBP monitoring.
- Robustness tests confirm the reliability of the recommended sensor set under various challenging conditions.
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