Abstract Process intensification in the form of continuous processing is presently being adopted by the biopharmaceutical industry as it offers significant advantages over conventional processing. Chromatographic steps form the core separation steps of a typical biopharma process due to their high selectivity and robustness. To this end, this paper proposes a novel approach based on reinforcement learning (RL), wherein a maximization problem is formulated for cation exchange chromatography for separation of charge variants by optimization of the process flowrate. Chromatography analysis and design toolkit have been used for process simulation and the optimum flow rate at which the yield is maximum and purity constraints are satisfied has been estimated based on the reward policy of RL. Results were experimentally validated and indicate that the proposed RL based approach is superior to the conventional trial and error method of optimizing flowrate in terms of both optimality and computational aspects (3X faster).

中文翻译：

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基于强化学习的生物制药连续加工过程色谱优化

摘要 目前，生物制药行业正在采用连续加工形式的过程强化，因为它比传统加工具有显着的优势。由于其高选择性和稳定性，色谱步骤构成了典型生物制药工艺的核心分离步骤。为此，本文提出了一种基于强化学习 (RL) 的新方法，其中为阳离子交换色谱法制定了最大化问题，以通过优化工艺流速来分离电荷变体。色谱分析和设计工具包已被用于过程模拟，并基于 RL 的奖励策略估计了产量最大和纯度约束满足的最佳流速。