2026-10-15T23:39:28Z https://data.mendeley.com/oai

oai:data.mendeley.com/2w3kzgkmzj.1 2026-10-09T13:15:13Z

Mahpara Progoti, Qazi Riti Exploratory Machine-Learning Prediction of Hard-to-Remove Water within a Treated Bleached Hardwood Kraft Pulp Series Mendeley Data Reproducibility package for the manuscript "Exploratory Machine-Learning Prediction of Hard-to-Remove Water within a Treated Bleached Hardwood Kraft Pulp Series" (submitted to RSC Advances). The study evaluates whether fiber-morphology descriptors can predict hard-to-remove water (HRW) within a single never-dried northern bleached hardwood kraft pulp treatment series. All data were obtained from experimentally measured values reported in peer-reviewed literature. The five supervised HRW conditions and their morphology come from Barrios et al. (2025), Biotechnology for Biofuels and Bioproducts 18:48, https://doi.org/10.1186/s13068-025-02646-1. The package contains: data: the 69-observation descriptive dataset (used for PCA), the five-condition predictor matrix, value-level provenance for every supervised value, and a data dictionary. notebooks: six executed Jupyter notebooks (PCA, nested leave-one-out screening, WRV reference model, Ridge sensitivity, exact permutation tests, and predictor-uncertainty sensitivity analysis). outputs: all numerical results, including out-of-fold predictions, fold selections, Ridge hyperparameters, permutation results, and sensitivity analyses. Length-weighted fiber length was selected in all five outer folds (R² = 0.807; exact permutation p = 0.025), but the result is not robust to the reporting precision of the source data and should be interpreted only as exploratory. To reproduce the results, install the packages in requirements.txt and run the notebooks locally or in Google Colab. See README.txt for details. Machine Learning Algorithm Cellulose Fiber Drying Kinetics Pulp Uddin, H.M. Wasi Rahman, A.F.M. Mustafizur Salem, Khandoker Samaher Dataset doi:10.17632/2w3kzgkmzj.1 oai:data.mendeley.com/2w3kzgkmzj.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/2w3kzgkmzj 2026-10-09T13:15:13Z