Ultimate_Analysis

This database studies the performance inconsistency on the biomass HHV ultimate analysis. The research null hypothesis is the consistency in the rank of a biomass HHV model. Fifteen biomass models are trained and tested in four datasets. In each dataset, the rank invariability of these 15 models indicates the performance consistency. The database includes the datasets and source codes to analyze the performance consistency of the biomass HHV. These datasets are stored in tabular on an excel workbook. The source codes are the biomass HHV machine learning model through the MATLAB Objected Orient Program (OOP). These machine learning models consist of eight regressions, four supervised learnings, and three neural networks. An excel workbook, "BiomassDataSetUltimate.xlsx," collects the research datasets in six worksheets. The first worksheet, "Ultimate," contains 908 HHV data from 20 pieces of literature. The names of the worksheet column indicate the elements of the ultimate analysis on a % dry basis. The HHV column refers to the higher heating value in MJ/kg. The following worksheet, "Full Residuals," backups the model testing's residuals based on the 20-fold cross-validations. The article (Kijkarncharoensin & Innet, 2021) verifies the performance consistency through these residuals. The other worksheets present the literature datasets implemented to train and test the model performance in many pieces of literature. A file named "SourceCodeUltimate.rar" collects the MATLAB machine learning models implemented in the article. The list of the folders in this file is the class structure of the machine learning models. These classes extend the features of the original MATLAB's Statistics and Machine Learning Toolbox to support, e.g., the k-fold cross-validation. The MATLAB script, name "runStudyUltimate.m," is the article's main program to analyze the performance consistency of the biomass HHV model through the ultimate analysis. The script instantly loads the datasets from the excel workbook and automatically fits the biomass model through the OOP classes. The first section of the MATLAB script generates the most accurate model by optimizing the model's higher parameters. It takes a few hours for the first run to train the machine learning model via the trial and error process. The trained models can be saved in MATLAB .mat file and loaded back to the MATLAB workspace. The remaining script, separated by the script section break, performs the residual analysis to inspect the performance consistency. Furthermore, the figure of the biomass data in the 3D scatter plot, and the box plots of the prediction residuals are exhibited. Finally, the interpretations of these results are examined in the author's article. Reference : Kijkarncharoensin, A., & Innet, S. (2022). Performance inconsistency of the Biomass Higher Heating Value (HHV) Models derived from Ultimate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.

本数据库针对生物质高位发热量（HHV，Higher Heating Value）元素分析的性能不一致性展开研究。本研究的原假设为生物质HHV模型的性能排序一致性。研究团队在4个数据集上训练并测试了15种生物质模型，在每个数据集内，这15种模型的排序不变性即表征其性能一致性。
本数据库包含用于分析生物质HHV性能一致性的数据集与源代码。数据集以表格形式存储于Excel工作簿中。源代码为基于MATLAB面向对象编程（OOP，Object Oriented Program）开发的生物质HHV机器学习模型，该模型包含8种回归模型、4种监督学习模型以及3种神经网络模型。
名为"BiomassDataSetUltimate.xlsx"的Excel工作簿在6个工作表中收录了本研究的数据集。第一个工作表"Ultimate"包含来自20篇文献的908组HHV数据，工作表列名对应以干基百分比计的元素分析组分，HHV列代表以MJ/kg为单位的高位发热量。第二个工作表"Full Residuals"备份了基于20折交叉验证得到的模型测试残差，Kijkarncharoensin与Innet（2021）通过这些残差验证了性能一致性。其余工作表则收录了用于在多篇文献中训练与测试模型性能的公开文献数据集。
名为"SourceCodeUltimate.rar"的文件收录了本研究中使用的MATLAB机器学习模型，该文件内的文件夹列表对应机器学习模型的类结构，这些类扩展了MATLAB原生统计与机器学习工具箱的功能，以支持k折交叉验证等操作。
名为"runStudyUltimate.m"的MATLAB脚本为本研究的主程序，用于通过元素分析探究生物质HHV模型的性能一致性。该脚本可直接从Excel工作簿加载数据集，并通过面向对象类自动拟合生物质模型。脚本的第一部分通过优化模型的超参数生成精度最优的模型，首次运行时需通过试错流程训练机器学习模型，耗时约数小时。训练完成的模型可保存为MATLAB .mat格式文件，并可重新加载至MATLAB工作区。脚本中以分段分隔符划分的其余部分用于执行残差分析以检验性能一致性，此外还会生成生物质数据的三维散点图以及预测残差的箱线图。最终，本研究的结果阐释已发表于作者的论文中。
参考文献：Kijkarncharoensin, A., & Innet, S. (2022). 基于元素分析的生物质高位发热量（HHV）模型性能不一致性[待投稿手稿]. 泰国商会大学。

搜集汇总

背景与挑战

背景概述

Ultimate_Analysis数据集专注于生物质高热值（HHV）模型的性能一致性研究，包含908条来自20篇文献的HHV数据，并提供MATLAB源代码用于训练和测试15种不同模型。数据集以Excel工作簿形式存储，支持生物质能源研究的机器学习和数据分析。