An Efficient Experimental Design Strategy for Modelling and Characterization of Processes

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2017Researchpeer-review

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Designing robust, efficient and economic processes is a main challenge for the biotech industries. To achieve a well-designed bioprocess, understanding the ongoing phenomena and the involved reaction kinetics is crucial. By development of advanced miniaturized reactors, a promising opportunity arises for parallel screening of multiple processes in reduced volumes within high throughput platforms. However, the level of accessible information from each set of experimental design remains to be one of the main issues particularly in the case of complex biosystems. This work introduces a novel generic Model-based Design of Experiments (M-DoE) routine with its main target being model development and system characterization. With the new M-DoE strategy, an improved set of informative experiments are suggested, which consequently reduces the demand for physical resources and analysis. The routine proposes a set of optimum experimental settings to support structural model definition, kinetic order estimation and parameter estimation during a model building procedure and process characterization.
Original languageEnglish
Title of host publicationProceedings of the 27th European Symposium on Computer Aided Process Engineering (ESCAPE 27)
EditorsAntonio Espuña, Moisès Graells, Luis Puigjaner
Publication date2017
ISBN (Print)9780444639653
ISBN (Electronic)9780444639707
Publication statusPublished - 2017
Event27th European Symposium on Computer Aided Process Engineering - Barcelona, Spain
Duration: 1 Oct 20175 Oct 2017
Conference number: 27


Conference27th European Symposium on Computer Aided Process Engineering
Internet address
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Model based Design of Experiments, Parallel experimental design, Model development, Electrochemical biosensor characterization

ID: 141974666