Closed-loop optimization of chromatography column sizing strategies in biopharmaceutical manufacture.
Closed-loop optimization of chromatography column sizing strategies in biopharmaceutical manufacture.
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DOI：
10.1002/jctb.4267
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发表时间：
2014-10
影响因子：
3.4
通讯作者：
Farid, Suzanne S.
中科院分区：
文献类型：
--
作者：
Allmendinger, Richard;Simaria, Ana S.;Turner, Richard;Farid, Suzanne S.
关键词：
本文考虑了一个现实世界的优化问题，涉及为纯化生物制药所采用的色谱步骤顺序确定具有成本效益的设备尺寸策略。解决这个问题需要解决受多个约束、不确定参数和耗时的适应度评估的组合优化问题。一个与工业相关的案例研究用于说明进化算法可以识别色谱尺寸策略，并在基本案例的基础上显着改进与工艺成本、时间和产品浪费相关的性能标准。结果还表明，当智能修复不可行的解决方案、适当设置种群大小以及将精英主义与少量蒙特卡罗试验相结合（需要考虑不确定性）时，进化算法表现最佳。事实证明，对于纯化过程可用时间较少的情况，采用此设置更为重要。最后，采用数据可视化工具来说明在选择要在实际工业环境中实施的规模调整策略时如何考虑用户偏好。这项工作表明，当正确调整并与详细的制造成本模型相结合时，闭环进化优化可以作为识别具有成本效益的纯化策略的强大决策工具。 © 2013 作者。 《化学技术与生物技术杂志》由 John Wiley & Sons Ltd 代表化学工业协会出版。
This paper considers a real-world optimization problem involving the identification of cost-effective equipment sizing strategies for the sequence of chromatography steps employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters, and time-consuming fitness evaluations. An industrially-relevant case study is used to illustrate that evolutionary algorithms can identify chromatography sizing strategies with significant improvements in performance criteria related to process cost, time and product waste over the base case. The results demonstrate also that evolutionary algorithms perform best when infeasible solutions are repaired intelligently, the population size is set appropriately, and elitism is combined with a low number of Monte Carlo trials (needed to account for uncertainty). Adopting this setup turns out to be more important for scenarios where less time is available for the purification process. Finally, a data-visualization tool is employed to illustrate how user preferences can be accounted for when it comes to selecting a sizing strategy to be implemented in a real industrial setting. This work demonstrates that closed-loop evolutionary optimization, when tuned properly and combined with a detailed manufacturing cost model, acts as a powerful decisional tool for the identification of cost-effective purification strategies. © 2013 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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影响因子：
4.3
作者：
Farid, Suzanne S.;Washbrook, John;Titchener-Hooker, Nigel J.
通讯作者：
Titchener-Hooker, Nigel J.
影响因子：
4.2
作者：
George, Edmund D.;Farid, Suzanne S.
通讯作者：
Farid, Suzanne S.
影响因子：
2.9
作者：
George, Edmund D.;Farid, Suzanne S.
通讯作者：
Farid, Suzanne S.
DOI：
10.1016/s0045-7825(99)00381-3
发表时间：
2000-01-01
影响因子：
7.2
作者：
Rechenberg, I
通讯作者：
Rechenberg, I
影响因子：
3.4
作者：
Sivapathasekaran, Chandrasekaran;Sen, Ramkrishna
通讯作者：
Sen, Ramkrishna