Digital twins : tools and concepts for smart biomanufacturing /

Digital twins : tools and concepts for smart biomanufacturing /

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Bibliographic Details
Imprint:	Cham, Switzerland : Springer, [2021].
Description:	1 online resource (vii, 262 pages) : illustrations (some color).
Language:	English
Series:	Advances in Biochemical Engineering/Biotechnology, 0724-6145 ; 176 Advances in biochemical engineering/biotechnology ; 176.
Subject:	Biochemical engineering. Biomedical engineering. Biochemical engineering. Biomedical engineering. Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12612441

Hidden Bibliographic Details
Varying Form of Title:	Tools and concepts for smart biomanufacturing
Other authors / contributors:	Herwig, Christoph, editor. Pörtner, Ralf, editor. Möller, Johannes, editor.
ISBN:	9783030716608 3030716600 9783030716592 3030716597
Notes:	Online resource; title from PDF title page (SpringerLink, viewed April 26, 2021).
Summary:	This 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.
Other form:	Original 3030716597 9783030716592
Standard no.:	10.1007/978-3-030-71660-8

Description
Summary:	This 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. <p>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.</p> <p>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.</p> <p>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. </p> <p>Covering fundamentals as well as applications, the two volumes offer the ideal introduction to the topic for researchers in academy and industry alike.</p> <p> </p>
Physical Description:	1 online resource (vii, 262 pages) : illustrations (some color).
ISBN:	9783030716608 3030716600 9783030716592 3030716597
ISSN:	0724-6145 ;