论文标题
离散的元素模拟和机器学习，用于改善干催化剂连续浸渍过程的性能
Discrete Element Simulations and Machine Learning for Improving the Performance of Dry Catalyst Continuous Impregnation Processes
论文作者
论文摘要
在这项工作中，使用机器学习的离散元素方法（DEM）模拟用于研究干燥的过程。我们的结果表明，粒子床包含两个机制。制度1显示了较小的倾斜角和较大的质量固定，这意味着更多的力限制了粒子运动。政权2揭示了较大的倾斜角度和旋转速度以及较小的质量固定，这表明床高度较小。使用机器学习，我们找到了相对标准偏差（RSD）的一般函数，该函数是时间，倾斜度角度和旋转速度的函数，均匀的旋转速度和不均匀的流速，对于在Lasso算法中喂养的全范围的参数。机器学习可以深入了解这两种制度，并揭示了对于低RPM和低角度的喷涂，不均匀的喷涂给出了较低的RSD，这与我们对DEM研究的观察一致。
In this work, discrete element method (DEM) simulations coupled with machine learning are used to study the process of dry impregnation. Our results show that the particle bed contains two regimes. Regime 1 shows smaller inclination angles and a larger mass hold-up which implies more forces restricting the particle movement. Regime 2 reveals larger inclination angles and rotational speeds and a smaller mass holdup, which indicates a smaller bed height. Using Machine learning, we found a general function for the Relative Standard Deviation (RSD) as a function of time, angle of inclination and speed of rotation for both even and uneven flow rates for a full range of the parameters fed in the LASSO algorithm. Machine learning gives insight on both regimes and reveals that for low RPM and low angles, uneven spraying gives a lower RSD which is consistent with our observations of the DEM studies.
