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Open Access Process Optimisation Based on Large Databases of Routinely Monitored Industrial Process Data

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Huge amounts of data are routinely logged and stored during the monitoring of biotechnological production processes. A concept is described to extract and analyse the information these data contain and to subsequently apply it for process improvement. In total, roughly 100,000 time series of raw and derived signals which stemmed from 173 high-cell-density processes with recombinant microorganisms at 50 m3 scale (working volume) were processed. As is often the case, no mathematical process models were readily available and therefore data-driven, computer-intensive methods were applied. These endeavours helped to stimulate a change in manufacturing strategy, which in turn has led to an increase in the final product titre of 26% on average.

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Keywords: COMPUTER-INTENSIVE METHODS; DATA-DRIVEN STATISTICAL METHODS; INTERVENTION-IMPACT ANALYSIS; LARGE DATABASE

Document Type: Research Article

Publication date: October 1, 2005

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  • International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions

    CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.

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