The performance of surrogate models, data assimilation and hybrid models when applied to the water-cooled freeboard of a process converter.

This dataset contains the figures and tables underpinning a dissertation on incorporating sensor measurements using data assimilation and machine learning to improve the accuracy of thermal finite element methods, as well as the data that these figures and tables portray. The filenames of the figures and tables correspond to the numbering applied to them in the dissertation. The data portrayed by these figures and tables are provided by means of the following *.mat files:surrogate_cs1.mat contains the performance of different types of surrogate models of a simplified model of a process converter freeboard, on which Tables 8 and 9 are based.
data_assimilation_cs1.mat contains the performance of different data assimilation algorithms applied to a simplified model of a process converter freeboard, on which Figures 14-17 and 41-60 are based.
hybrid_cs1.mat contains the performance of hybrid models of a simplified model of a process converter freeboard, on which Figures 19, 20 and 21-23, as well as Table 13 are based.
surrogate_cs2.mat contains the performance of different types of surrogate model of a half model of a process converter freeboard, on which Table 15 is based.
data_assimilation_simulated_cs2.mat contains the performance of different data assimilation algorithms applied using simulated measurements of a process converter freeboard, on which Figures 27-29 and Table 17 are based.
hybrid_simulated_cs2.mat contains the performance of hybrid models trained using simulated measurements of a process converter freeboard, on which Figures 30-33 and Table 18 are based.
data_assimilation_real_cs2.mat contains the performance of different data assimilation parameters for an application using real measurements of a process converter freeboard, on which Figure 35 is based.
hybrid_real_cs2.mat contains the performance of hybrid models trained using real measurements of a process converter freeboard, on which Figures 37-40 and Table 20 are based.

Each *.mat file (a Matlab file format which can be used within other software environments like Octave and Python ) contains a variable called ‘description’ that provides a short description of each of the other variables contained in the file.

本数据集收录了有关融合传感器测量、采用数据同化和机器学习技术以提升热有限元方法精确度的论文中的图表与表格。所述图表与表格所描绘的数据，均由以下*.mat文件提供：surrogate_cs1.mat文件包含了不同类型代理模型在简化过程转换器自由板模型上的性能，该性能数据构成了第八表和第九表的基础；data_assimilation_cs1.mat文件包含了应用于简化过程转换器自由板模型的不同数据同化算法的性能，该性能数据构成了第十四图至第十七图以及第四十一图至第六十图的基础；hybrid_cs1.mat文件包含了混合模型在简化过程转换器自由板模型上的性能，该性能数据构成了第十九图、第二十图以及第二十一图至第二十三图，以及第十三表的基础；surrogate_cs2.mat文件包含了不同类型代理模型在简化过程转换器自由板半模型上的性能，该性能数据构成了第十五表的基础；data_assimilation_simulated_cs2.mat文件包含了使用模拟测量应用于过程转换器自由板的不同数据同化算法的性能，该性能数据构成了第二十七图至第二十九图以及第十七表的基础；hybrid_simulated_cs2.mat文件包含了使用模拟测量训练的混合模型在过程转换器自由板上的性能，该性能数据构成了第三十图至第三十三图以及第十八表的基础；data_assimilation_real_cs2.mat文件包含了使用真实测量应用于过程转换器自由板的不同数据同化参数的性能，该性能数据构成了第三十五图的基础；hybrid_real_cs2.mat文件包含了使用真实测量训练的混合模型在过程转换器自由板上的性能，该性能数据构成了第三十七图至第四十图以及第二十表的基础。每一*.mat文件（一种Matlab文件格式，可在诸如Octave和Python等软件环境中使用）均包含一个名为‘description’的变量，该变量为文件中包含的其他每个变量提供简要描述。