Abstract

An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML.

摘要

开发了一个基于人工智能的系统，以使用由 2726 个样本组成的大型数据集来有效地预测沉降速度 (SV)。粒径和流体粘度的范围分别为 0.212 - 98.59 mm 和 0.02 - 92800 mPa.s。粒子和流体的属性作为输入输入到模型中，以获得 SV 作为输出。测试了六种机器学习算法的预测。随机森林 (RF) 的性能优于其他算法，确定系数为 0.98，均方误差为 0.0027。使用 RF 模型开发了一个简单的决策支持系统。目前的研究展示了使用 ML 对 SV 建模的完整方法。