Design of smart liquid-1iquid extraction columns for downstream separations of biopharmaceuticals using deep Q-1earning algorithm

摘要

We propose smart liquid-1iquid extraction columns of biopharmaceuticals using deep Q-learning algorithm.In this contribution, we demonstrated the application of the tool for design of liquid-1iquid 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-1iquid segment activity coefficient model, 2) design the liquid-1iquid extraction process for different solvent candidates commonly used in pharma industries, 3) application of deep Q-learning algorithm to optimize liquid-1iquid 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.