论文标题
关于突破曲线预测的机器学习方法的性能
On the Performance of Machine Learning Methods for Breakthrough Curve Prediction
论文作者
论文摘要
反应流是众多技术和环境过程的重要组成部分。相比之下，通常无法监测域内的流量和物种浓度是昂贵的，或者是昂贵的，出口浓度很容易测量。与多孔介质中的反应性流有关，术语突破曲线用于表示出口浓度在入口处有规定条件的时间依赖性。在这项工作中，我们应用了几种机器学习方法来预测从给定的一组参数中的突破性曲线。在我们的情况下，参数是Damköhler和Peclet编号。我们对一维情况进行了彻底的分析，并为三维情况提供了结果。
Reactive flows are important part of numerous technical and environmental processes. Often monitoring the flow and species concentrations within the domain is not possible or is expensive, in contrast, outlet concentration is straightforward to measure. In connection with reactive flows in porous media, the term breakthrough curve is used to denote the time dependency of the outlet concentration with prescribed conditions at the inlet. In this work we apply several machine learning methods to predict breakthrough curves from the given set of parameters. In our case the parameters are the Damköhler and Peclet numbers. We perform a thorough analysis for the one-dimensional case and also provide the results for the three-dimensional case.
