论文标题
Viskositas：多组分化学系统的粘度预测
Viskositas: Viscosity Prediction of Multicomponent Chemical Systems
论文作者
论文摘要
冶金和玻璃行业的粘度在其生产过程中也起着基本作用，在地球物理领域也起着基本作用。由于其实验测量在财务上也很昂贵，因此在时间上也建立了几种数学模型，以提供粘度结果，这是在线性和非线性模型中的几个变量（例如化学成分和温度）的函数。建立数据库是为了通过超参数的变化来通过人工神经网络产生非线性模型，从而可靠地预测了与化学系统和温度有关的粘度。与文献和1个商业模型的不同模型相比，与测试数据库相关的平均绝对误差，标准偏差和确定系数的统计评估表现出更好的统计评估，提供了较低的误差，较小的可变性和较小的发出物的产生差异。
Viscosity in the metallurgical and glass industry plays a fundamental role in its production processes, also in the area of geophysics. As its experimental measurement is financially expensive, also in terms of time, several mathematical models were built to provide viscosity results as a function of several variables, such as chemical composition and temperature, in linear and nonlinear models. A database was built in order to produce a nonlinear model by artificial neural networks by variation of hyperparameters to provide reliable predictions of viscosity in relation to chemical systems and temperatures. The model produced named Viskositas demonstrated better statistical evaluations of mean absolute error, standard deviation and coefficient of determination in relation to the test database when compared to different models from literature and 1 commercial model, offering predictions with lower errors, less variability and less generation of outliers.
