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010   $a3-030-71660-0
024 7 $a10.1007/978-3-030-71660-8
035   $a(CKB)4100000011902523
035   $a(MiAaPQ)EBC6568330
035   $a(Au-PeEL)EBL6568330
035   $a(OCoLC)1247681854
035   $a(PPN)255289316
035   $a(DE-He213)978-3-030-71660-8
035   $a(EXLCZ)994100000011902523
100   $a20210425d2021      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDigital Twins $eTools and Concepts for Smart Biomanufacturing /$fedited by Christoph Herwig, Ralf Pörtner, Johannes Möller
205   $a1st ed. 2021.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021.
215   $a1 online resource (267 pages)
225 1 $aAdvances in Biochemical Engineering/Biotechnology,$x1616-8542 ;$v176
311 08$a3-030-71659-7 
327   $aTowards the development of digital twins for the biomanufacturing industry -- When is an In silico representation a digital twin? A biopharmaceutical industry approach to the digital twin concept -- Digitalization and bioprocessing, regulatory aspects -- Usage of Digital Twins along a typical process development cycle -- Mechanistic Mathematical Models as a Basis for Process Optimization and Digital Twins -- Digital Seed Train Twins and Statistical Methods -- Digital Twins in Biomanufacturing.
330   $aThis is the first of two volumes that together provide an overview of the latest advances in the generation and application of digital twins in bioprocess design and optimization. Both processes have undergone significant changes over the past few decades, moving from data-driven approaches into the 21st-century digitalization of the bioprocess industry. Moreover, the high demand for biotechnological products calls for efficient methods during research and development, as well as during tech transfer and routine manufacturing. In this regard, one promising tool is the use of digital twins, which offer a virtual representation of the bioprocess. They reflect the mechanistics of the biological system and the interactions between process parameters, key performance indicators and product quality attributes in the form of a mathematical process model. Furthermore, digital twins allow us to use computer-aided methods to gain an improved process understanding, to test and plan novel bioprocesses, and to efficiently monitor them. This book explains the mathematical structure of digital twins, their development and the model?s respective parts, as well as concepts for the knowledge-driven generation and structural variability of digital twins. Covering fundamentals as well as applications, the two volumes offer the ideal introduction to the topic for researchers in academy and industry alike. .
410  0$aAdvances in Biochemical Engineering/Biotechnology,$x1616-8542 ;$v176
606   $aBiochemical engineering
606   $aBiotechnology
606   $aComputational intelligence
606   $aManufactures
606   $aBioprocess Engineering
606   $aBiotechnology
606   $aComputational Intelligence
606   $aMachines, Tools, Processes
615  0$aBiochemical engineering.
615  0$aBiotechnology.
615  0$aComputational intelligence.
615  0$aManufactures.
615 14$aBioprocess Engineering.
615 24$aBiotechnology.
615 24$aComputational Intelligence.
615 24$aMachines, Tools, Processes.
676   $a660.63
676   $a338.476606
702   $aHerwig$b Christoph
702   $aPo?rtner$b Ralf
702   $aMo?ller$b Johannes
702   $aAppl$b C.
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910483653803321
996   $aDigital twins$92480004
997   $aUNINA