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ISBN 9783843933964

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978-3-8439-3396-4, Reihe Technische Chemie

Fabian B. Thygs
Automation Techniques to Support Experimental Investigation During Systematic Downstream Process Development

270 Seiten, Dissertation Technische Universität Dortmund (2017), Softcover, A5

Zusammenfassung / Abstract

During systematic and experimental based downstream process (DSP) synthesis, expert knowledge is used to generate process alternatives for a given purification problem. Depending on the material system under investigation and the alternatives generated, experiments are necessary to evaluate their applicability and efficiency. To support these investigations and to reduce the manual effort, automation techniques are implemented in the design methodology which execute various unit operations for DSP in miniaturized scale and automated manner. The data generated are finally used to evaluate process alternatives by applying classical process parameters (yield, purity, selectivity) or Key Performance Indicators (purification performance index, separation cost indicator). Process synthesis presented in this thesis is significantly supported by automated experiments, which could be executed without any manual intervention. To increase the functionality and the field of application of a robotic platform, new standardized and automated (unit) operations are realized such as solubility measurements, precipitation or solid/liquid-extractions for kinetic and multi stage leaching experiments. The precision and accuracy of these methods could be proven with model material systems and a genuine fermentation broth. Furthermore, instead of executing single operations one after another, a proof of concept is shown where automated methods are connected resulting in a complete downstream process route performed completely automatically on a robotic platform. By this the potential of automation in DSP is increased, and experimental data of a complete DSP-alternative are generated under consideration of the performance of each step in dependency to the previous one(s). This may be the basis for a methodology of systematic, robot based, and KPI-driven downstream process development.