Advances in Bioprocess Monitoring and Control Systems

dc.authorscopusid 59187721600
dc.authorscopusid 57217008505
dc.authorscopusid 57208180267
dc.authorscopusid 58038030900
dc.authorscopusid 57218947493
dc.authorscopusid 57188769770
dc.authorscopusid 60210092400
dc.contributor.author Kumar, R.
dc.contributor.author Chattaraj, S.
dc.contributor.author Boyno, G.
dc.contributor.author Alloun, W.
dc.contributor.author Andjelkovic, S.
dc.contributor.author Živković, S.
dc.contributor.author Guerra Sierra, B.E.
dc.date.accessioned 2025-12-30T16:06:08Z
dc.date.available 2025-12-30T16:06:08Z
dc.date.issued 2025
dc.department T.C. Van Yüzüncü Yıl Üniversitesi en_US
dc.department-temp [Kumar] Rahul, Graphic Era Deemed to be University, Dehradun, UK, India; [Chattaraj] Sourav, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, OR, India; [Boyno] Gökhan, Van Yüzüncü Yıl Üniversitesi, Van, Turkey; [Alloun] Wiem, Université Constantine 1, Constantine, Constantine Province, Algeria; [Andjelkovic] S., Institute for Forage Crops, Krusevac, Serbia; [Živković] Sanja P., University of Niš, Nis, Serbia; [Guerra Sierra] B. E., Faculty of Science, Universidad de Santander, Bucaramanga, Santander, Colombia; [Mitra] Debasis, Graphic Era Deemed to be University, Dehradun, UK, India en_US
dc.description.abstract The bioprocess monitoring market, valued at $12.3 billion in 2023, is expected to grow at a CAGR of 9.1% to reach $20.5 billion by 2030. This growth is driven by biosensors, machine learning, and Industry 4.0. Innovations like Raman spectroscopy and NMR have improved metabolite profiling accuracy, leading to enhanced process control. Artificial intelligence-driven models have reduced batch variability by 20%, while digital twin technologies have reduced process development time by 25%. Automated fed-batch strategies have increased recombinant protein yields by 15-25%, while microfluidic bioreactors enable high-throughput screening with a 5-fold reduction in reagent costs. Soft-sensor technologies have adjusted metabolic flux projections by 35%, reducing process variation. IoT-enabled bioprocessing has reduced manual interventions by 40%, improving operational effectiveness. ©2026, IGI Global Scientific Publishing. All rights reserved. en_US
dc.identifier.doi 10.4018/979-8-3373-2873-7.ch005
dc.identifier.endpage 142 en_US
dc.identifier.isbn 9798337328751
dc.identifier.isbn 9798337328737
dc.identifier.scopus 2-s2.0-105022855404
dc.identifier.scopusquality N/A
dc.identifier.startpage 123 en_US
dc.identifier.uri https://doi.org/10.4018/979-8-3373-2873-7.ch005
dc.identifier.uri https://hdl.handle.net/20.500.14720/29372
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher IGI Global en_US
dc.relation.publicationcategory Kitap Bölümü - Uluslararası en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.title Advances in Bioprocess Monitoring and Control Systems en_US
dc.type Book Part en_US
dspace.entity.type Publication

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