Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives
Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives
复制标题
机器学习遇到连续流化学：针对多个目标的帕累托前沿的自动优化
DOI：
10.1016/j.cej.2018.07.031
复制
发表时间：
2018
影响因子：
15.1
通讯作者：
Schweidtmann A
中科院分区：
文献类型：
--
作者：
Schweidtmann A
化学工艺的自动化开发需要使用复杂的多目标优化算法，因为单目标优化无法识别相互冲突的性能标准之间的权衡。在此，我们报告了一种用于自优化的新多目标机器学习优化算法的实现，并在连续流中进行的两个示例化学反应中进行了演示。在这两种情况下，该算法成功地确定了与环境和经济目标之间的权衡曲线（帕累托前沿）相对应的一组最佳条件。因此，它揭示了完整的潜在权衡，并不像许多其他研究那样仅限于一种妥协。事实证明，机器学习算法具有极高的数据效率，与单目标优化相比，它可以通过更少的实验次数确定目标的最佳条件。多个目标之间完整的潜在权衡是在没有任意权重因子的情况下确定的，而是通过真正的多目标优化来确定的。
Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance criteria. Herein we report the implementation of a new multi-objective machine learning optimization algorithm for self-optimization, and demonstrate it in two exemplar chemical reactions performed in continuous flow. The algorithm successfully identified a set of optimal conditions corresponding to the trade-off curve (Pareto front) between environmental and economic objectives in both cases. Thus, it reveals the complete underlying trade-off and is not limited to one compromise as is the case in many other studies. The machine learning algorithm proved to be extremely data efficient, identifying the optimal conditions for the objectives in a lower number of experiments compared to single-objective optimizations. The complete underlying trade-off between multiple objectives is identified without arbitrary weighting factors, but via true multi-objective optimization.