结垢
无量纲量
悬挂(拓扑)
沉积(地质)
软传感器
生物系统
线性回归
集合(抽象数据类型)
人工智能
机器学习
算法
工艺工程
工程类
计算机科学
化学
机械
地质学
数学
过程(计算)
物理
生物化学
古生物学
膜
同伦
沉积物
纯数学
操作系统
生物
程序设计语言
作者
Niklas Jarmatz,Wolfgang Augustin,Stefan Scholl,Alberto Tonda,Guillaume Delaplace
标识
DOI:10.1016/j.fbp.2024.02.009
摘要
Fouling is the unwanted accumulation of material on a processing surface which is an especially problematic issue in the food industry. Characterizing or predicting fouling through traditional methods or models is a challenge due to the complexity of fouling mechanisms. Machine Learning techniques can overcome this challenge by creating models for prediction directly from experimental data. Unfortunately, the results can be hard to interpret depending on the algorithm. Here, a soft sensor is generated from an extensive data set to predict the fouling of a model particle material system. This is performed inside two different pipe fittings, an inaccessible and accessible fitting (e.g., for sensor measurements). Additionally, Dimensional Analysis is conducted to identify the correlations responsible for fouling while keeping descriptors with physical meaning. The resulting dimensionless numbers are further processed by three machine learning algorithms: Linear Regression, Symbolic Regression, and Random Forest. The soft sensor generated using a Random Forest outperformed the other two regressors for the dimensional (Q2=0.90±0.08) and for the dimensionless data (Q2=0.88±0.09). The parameter time and particle mass fraction were determined to be most influential. Furthermore, seven dimensionless numbers were obtained allowing a reduced experimental design.
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