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MilliporeSigma, this development is driven by
interest in continuous manufacturing.
" The biomanufacturing industry is evolving from a
standard batch processing paradigm to a more
continuous operation supported by inline sensors
and process analytics to enable real-time product
release, " Revankar says. " Data-driven continuous
operations will help to significantly improve product
quality, reduce production costs and shorten the
time to market. "
The increasing complexity of production processes
is another factor, observes Krist V. Gernaey, PhD, head
of the process and systems engineering center at the
Technical University of Denmark.
" The most obvious advantage is that AI can potentially
detect patterns in datasets that are difficult to
observe for an operator or process chemist, " Gernaey
explains. " In this way, one could gain new information
about production processes and, of course, exploit
that later on toward improving such a process.
" When properly implemented, the use of AI should
in general also have the benefit of making use of
collected data, instead of just storing the data as
historical documentation about a production process
that has been completed. "
Jens Smiatek, PhD, an AI and data management
expert at the University of Stuttgart, is of a similar
opinion about the role of AI in biomanufacturing: " In
the plant, AI can be used for laboratory automation,
efficient document processing, and process control
and steering. Such applications mainly correspond to
the improvement of daily business as well as wet-lab
and manufacturing work. "
Machine learning
Smiatek cites machine learning (ML)-a technology
that provides systems the ability to " learn " by
analyzing data over time-as a form of AI that
Artificial intelligence has broad application in biomanufacturing
according to Vikas Revankar, head of Software
and Automation at MilliporeSigma. He predicts machine
learning algorithms trained on benchtop bioreactors
could be used to automate control of large-scale reactors.
industry is adapting and using to automate production
" In terms of efficient and improved unit operation
models, ML provides a plethora of novel approaches, "
he asserts. " In principle, there exist several standard
models for distinct unit operations, ranging from the
use of differential equations, in terms of mechanistic
models via metabolic flux models, to molecular
modeling approaches. "
Industry's approach has been to combine mechanistic
models, which are based on the assessment of
parameters, with artificial neural networks (ANNs),
which " learn " processes.
" Experimental data is evaluated by an ANN in
accordance with a high-dimensional multivariate
regression approach, which allows the modeler an
accurate determination of relevant parameters for
the combined mechanistic models, " he elaborates.
" For example, the calculation and prediction of key
parameters for cultivation or fermentation processes
as well as filtration approaches can be improved
significantly. "
GENengnews.com | 23


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