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首页> 外文期刊>Biotechnology Progress >Data-driven multi-objective optimization via grid compatible simplex technique and desirability approach for challenging high throughput chromatography applications
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Data-driven multi-objective optimization via grid compatible simplex technique and desirability approach for challenging high throughput chromatography applications

机译：通过网格兼容的单纯X技术和挑战高吞吐量色谱应用的可靠性方法数据驱动的多目标优化

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摘要

Recently, a grid compatible Simplex variant has been demonstrated to identify optima consistently and rapidly in challenging high throughput (HT) applications in early bioprocess development. Here, this method is extended by deploying it to multi-objective optimization problems. Three HT chromatography case studies are presented, each posing challenging early development situations and including three responses which were amalgamated by the adoption of the desirability approach. The suitability of a design of experiments (DoE) methodology per case study, using regression analysis in addition to the desirability approach, was evaluated for a large number of weights and in the presence of stringent and lenient performance requirements. Despite the adoption of high-order models, this approach had low success in identification of the optimal conditions. For the deployment of the Simplex approach, the deterministic specification of the weights of the merged responses was avoided by including them as inputs in the formulated multi-objective optimization problem, facilitating this way the decision making process. This, and the ability of the Simplex method to locate optima, rendered the presented approach highly successful in delivering rapidly operating conditions, which belonged to the Pareto set and offered a superior and balanced performance across all outputs compared to alternatives. Moreover, its performance was relatively independent of the starting conditions and required sub-minute computations despite its higher order mathematical functionality compared to DoE techniques. These evidences support the suitability of the grid compatible Simplex method for early bioprocess development studies involving complex data trends over multiple responses. (c) 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers
机译：最近，已经证明了网格兼容的单纯形式变体，以始终如一地识别Optima，并在早期的生物过程开发中挑战高吞吐量（HT）应用。这里，通过将其部署到多目标优化问题来扩展此方法。提出了三种HT色谱案例研究，每个案例研究挑战早期发育情况，包括通过采用可取性方法进行分摊的三种反应。使用回归分析除了期望方法之外，使用回归分析的实验设计（DOE）方法的适用性被评估了大量重量，并且在存在严格和宽容的性能要求中。尽管采用了高阶型号，但这种方法成功识别最佳条件。为了部署Simplex方法，通过将它们作为输入中的输入，避免了合并响应的权重的确定性规范，以这种方式促进了这种决策过程。这样，单纯x方法定位Optima的能力呈现出呈现的方法非常成功地在提供快速操作条件时，该方法属于帕累托集，并与替代方案相比，在所有输出中提供了优异的和平衡的性能。此外，尽管与DOE技术相比，其性能相对独立于起始条件和所需的子分钟计算。这些证据支持网格兼容的单纯克斯方法对早期生物过程开发研究的适用性，涉及复杂数据趋势的多重反应。 （c）2018年作者Biotechnology通过Wiley期刊，Inc。代表美国化学工程师研究所发布

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