{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:09:39Z","timestamp":1787029779814,"version":"build-2736575974"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:00:00Z","timestamp":1782000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001655","name":"German Academic Exchange Service","doi-asserted-by":"publisher","award":["57693451"],"award-info":[{"award-number":["57693451"]}],"id":[{"id":"10.13039\/501100001655","id-type":"DOI","asserted-by":"publisher"}]},{"award":["57693451"],"award-info":[{"award-number":["57693451"]}],"id":[{"id":"https:\/\/ror.org\/039djdh30","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant\u2013model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a 6h sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol\u2013sugar\u2013biomass\u2013substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and 24h, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol\u2013sugar\u2013biomass\u2013enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and 24h, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol\u2013biomass\u2013enzyme\u2013substrate with 0.3843 and ethanol\u2013sugar\u2013biomass\u2013substrate with 0.4121. The primary aggregate ranking identified ethanol\u2013sugar\u2013biomass\u2013substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol\u2013sugar\u2013biomass\u2013substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings.<\/jats:p>","DOI":"10.3390\/s26123948","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:03:23Z","timestamp":1782263003000},"page":"3948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Observability- and Identifiability-Guided Sensor-Set Design for Digital-Twin-Assisted Consolidated Bioprocessing"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2131-3147","authenticated-orcid":false,"given":"Mark","family":"Korang Yeboah","sequence":"first","affiliation":[{"name":"Chair of Dynamics and Control, University of Duisburg\u2013Essen, Lotharstra\u00dfe, 47057 Duisburg, Germany"},{"name":"Faculty of Mechanical and Chemical Engineering, Kwame Nkrumah University of Science and Technology, PMB, University Post Office, Kumasi 00233, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7424-3668","authenticated-orcid":false,"given":"Nana Yaw","family":"Asiedu","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical and Chemical Engineering, Kwame Nkrumah University of Science and Technology, PMB, University Post Office, Kumasi 00233, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1096-3607","authenticated-orcid":false,"given":"Ahmad","family":"Addo","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical and Chemical Engineering, Kwame Nkrumah University of Science and Technology, PMB, University Post Office, Kumasi 00233, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1089\/ind.2024.0003","article-title":"Performance of Machine Learning Based-Modelling Approach in Consolidated Bioprocessing with Microbial Consortium for Bioethanol Production","volume":"20","author":"Yeboah","year":"2024","journal-title":"Ind. 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