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. 2017 Nov 22;34(12):2596–2613. doi: 10.1007/s11095-017-2308-y

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© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Fig. 5 — Workflow showing the data-driven knowledge discovery approach for the detection and minimization of disturbance variables. After selection of the targeted disturbance class via risk assessment tools, data has to be generated and/or accumulated. Indications about disturbing variables/ descriptors can be generated by correlation analysis or – if possible – via mechanistic modelling. Obtained knowledge/ information has to be implemented in the design space to allow minimization of the identified disturbances.