Design of smart liquid-liquid extraction columns for downstream separations of biopharmaceuticals using deep Q-learning algorithm

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2019Researchpeer-review

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We propose smart liquid-liquid extraction columns of biopharmaceuticals using deep Q-learning algorithm. In this contribution, we demonstrated the application of the tool for design of liquid-liquid extraction process for concentration of API from fermentation broth. To this end, we present the following;1) development of property model to describe solubility of API in different solvents using the nonrandom two-liquid segment activity coefficient model, 2) design the liquid-liquid extraction process for different solvent candidates commonly used in pharma industries, 3) application of deep Q-learning algorithm to optimize liquid-liquid extraction control, and 4) perform sensitivity analysis to study effect of feed fraction of API on the performance. We have validated the developed property process modelling by comparing the existing experimental data and the characteristics of diverse solvents and using sensitivity analysis. We expect that the results from this study would contribute to further development the general framework of downstream separation for the future by extending to more downstream separation processes.
Original languageEnglish
Title of host publicationProceedings of the 29th European Symposium on Computer Aided Process Engineering
EditorsKiss Anton, Edwin Zondervan, Richard Lakerveld, Leyla Özkan
Publication date2019
ISBN (Print)9780128186343
Publication statusPublished - 2019
Event29th European Symposium on Computer Aided Process Engineering - Eindhoven, Netherlands
Duration: 16 Jun 201919 Jun 2019


Conference29th European Symposium on Computer Aided Process Engineering
SeriesComputer Aided Chemical Engineering
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Property process modelling, Liquid-liquid extraction, Deep Q-learning, Control, Biopharmaceuticals

ID: 189364791