This database studies the performance inconsistency on the biomass HHV proximate 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 models consist of eight regressions, four supervised learnings, and three neural networks.

An excel workbook, "BiomassDataSetProximate.xlsx," collects the research datasets in six worksheets. The first worksheet, "Proximate," contains 803 HHV data from 17 pieces of literature. The names of the worksheet column indicate the elements of the proximate 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 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 "SourceCodeProximate.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, "runStudyProximate.m," is the article's main program (Kijkarncharoensin & Innet, 2021) to analyze the performance consistency of the biomass HHV model through the proximate 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. (2021). Performance inconsistency of the Biomass Higher Heating Value (HHV) Models derived from Proximate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.

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.

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. (2021). The Performance Inconsistency of the Biomass Higher Heating Value (HHV) Models: The Ultimate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.

This database studies the performance inconsistency on the biomass HHV proximate 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 models consist of eight regressions, four supervised learnings, and three neural networks.

An excel workbook, "BiomassDataSetProximate.xlsx," collects the research datasets in six worksheets. The first worksheet, "Proximate," contains 803 HHV data from 17 pieces of literature. The names of the worksheet column indicate the elements of the proximate 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 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 "SourceCodeProximate.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, "runStudyProximate.m," is the article's main program (Kijkarncharoensin & Innet, 2021) to analyze the performance consistency of the biomass HHV model through the proximate 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. (2021). The Performance Inconsistency of the Biomass Higher Heating Value (HHV) Models: The Proximate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.