Trajectory-level dynamic validation of a phase-structured grey-box model for consolidated bioprocessing using literature-derived ethanol trajectories

Trajectory-level dynamic validation of a phase-structured grey-box model for consolidated bioprocessing using literature-derived ethanol trajectories | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trajectory-level dynamic validation of a phase-structured grey-box model for consolidated bioprocessing using literature-derived ethanol trajectories Mark Korang Yeboah, Nana Yaw Asiedu, Ahmad Addo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9278223/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Consolidated bioprocessing (CBP) is a promising route for lignocellulosic ethanol production because enzyme generation, biomass hydrolysis, and fermentation can be integrated within a single process. However, the strong nonlinear coupling and limited observability of these stages make CBP difficult to model dynamically. This study presents a time-series-informed grey-box framework for dynamic validation of CBP using secondary, literature-derived ethanol trajectories. A curated benchmark dataset was assembled from heterogeneous published CBP studies, harmonized at the trajectory level, and partitioned by series; after curation and trimming, the dataset retained 23 trajectories and 211 observations. A data-driven timepoint model was first developed as a benchmark for ethanol prediction across diverse operating conditions. The same dataset was then used to calibrate a phase-structured grey-box model representing overlapping enzyme-production, hydrolysis, and fermentation stages through direct fitting to observed ethanol time series. Model performance was assessed using trajectory-level RMSE, MAE, final-point error, coefficient of determination, and residual diagnostics, together with analyses of fitted parameter distributions, phase activation behavior, and reconstructed trajectories. The grey-box model reproduced the dominant temporal patterns of literature-derived CBP ethanol trajectories while preserving mechanistic interpretability. Fitted parameters indicated that many trajectories could be captured through moderate adjustments in phase-specific kinetic capacity and phase timing rather than through major distortion of the underlying process structure. In direct comparison, the data-driven benchmark provided higher predictive accuracy, whereas the grey-box framework offered a more informative representation of CBP progression by linking ethanol accumulation to coordinated upstream and downstream process stages. Overall, the results show that secondary time-series data can extend CBP modeling beyond endpoint analysis toward dynamic validation, trajectory reconstruction, and soft-sensor-oriented process interpretation for lignocellulosic ethanol systems. Consolidated bioprocessing Hybrid modeling Dynamic validation Trajectory reconstruction State estimation Soft sensing Unscented Kalman filter Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 31 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9278223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620719552,"identity":"fef9a1c8-5de3-4747-97b5-5b49175d202a","order_by":0,"name":"Mark Korang Yeboah","email":"data:image/png;base64,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","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":true,"prefix":"","firstName":"Mark","middleName":"Korang","lastName":"Yeboah","suffix":""},{"id":620719555,"identity":"2c26d4a9-f63a-4fdd-8a41-cfdccf2924cc","order_by":1,"name":"Nana Yaw Asiedu","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Nana","middleName":"Yaw","lastName":"Asiedu","suffix":""},{"id":620719560,"identity":"8c9c56bd-f51b-43bd-9ea5-113889470d53","order_by":2,"name":"Ahmad Addo","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Addo","suffix":""}],"badges":[],"createdAt":"2026-03-31 10:10:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9278223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9278223/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107482179,"identity":"8f6537be-be6f-48f3-aa6b-46072d93a739","added_by":"auto","created_at":"2026-04-22 02:22:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7376033,"visible":true,"origin":"","legend":"","description":"","filename":"Dynamicvalidationpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9278223/v1_covered_bf2be190-ce0c-4e7f-bc4d-77cd8b963100.pdf"},{"id":107121928,"identity":"33142812-4af5-49c8-96f6-a64907240885","added_by":"auto","created_at":"2026-04-17 04:42:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":156745,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9278223/v1/2e652a27ef7026a01a301979.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trajectory-level dynamic validation of a phase-structured grey-box model for consolidated bioprocessing using literature-derived ethanol trajectories","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biotechnology-for-biofuels-and-bioproducts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bbio","sideBox":"Learn more about [Biotechnology for Biofuels](http://biotechnologyforbiofuels.biomedcentral.com/)","snPcode":"13068","submissionUrl":"https://submission.nature.com/new-submission/13068/3","title":"Biotechnology for Biofuels and Bioproducts","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Consolidated bioprocessing, Hybrid modeling, Dynamic validation, Trajectory reconstruction, State estimation, Soft sensing, Unscented Kalman filter","lastPublishedDoi":"10.21203/rs.3.rs-9278223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9278223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Consolidated bioprocessing (CBP) is a promising route for lignocellulosic ethanol production because enzyme generation, biomass hydrolysis, and fermentation can be integrated within a single process. However, the strong nonlinear coupling and limited observability of these stages make CBP difficult to model dynamically. This study presents a time-series-informed grey-box framework for dynamic validation of CBP using secondary, literature-derived ethanol trajectories. A curated benchmark dataset was assembled from heterogeneous published CBP studies, harmonized at the trajectory level, and partitioned by series; after curation and trimming, the dataset retained 23 trajectories and 211 observations. A data-driven timepoint model was first developed as a benchmark for ethanol prediction across diverse operating conditions. The same dataset was then used to calibrate a phase-structured grey-box model representing overlapping enzyme-production, hydrolysis, and fermentation stages through direct fitting to observed ethanol time series. 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