This file ReadMe was created on 2025-10-13 by: Pavel Praks (VSB-TUO, IT4Innovations, Ctr. Pavel.Praks@vsb.cz).Last update: 2025-10-13 ---------------------------Basic information---------------------------1. Dataset name: A Data-Driven Digital Model of Waste Gasification and Pyrolysis: One Tailored Approach for an Experimental Facility from the Czech Republic Full citationDejan Brkić, Pavel Praks, Judita Buchlovská Nagyová, Michal Běloch, Martin Marek, Jan Najser, Renáta Praksová, Jan Kielar (2025). A Data-Driven Digital Model of Waste Gasification and Pyrolysis: One Tailored Approach for an Experimental Facility from the Czech Republic 2. DOI: 10.5281/zenodo.17367400 3. Contact information Name: Pavel Praks Institution: IT4Innovations; VSB-Technical University of Ostrava; E-mail: Pavel.Praks@vsb.cz ORCID: https://orcid.org/0000-0002-3913-7800 4. Dataset archiving (publication) date: 2025-10-14 5. Place of archiving (publication): Ostrava 6. Dataset description: Appendices and electronic appendices from original research. Precisely, there are the following files: Appendix_A, Appendix_B, Electronic_Appendix_A, Electronic_Appendix_B, Electronic_Appendix_C Appendix_A.zip - Evaluation of gasification models using entropy.This Appendix A summarizes validation of the developed gasification models by approximation and sample entropy methods. Appendix_B.zip - A fuel cell script in the R language related to detection and prediction of purge and an electrolyzer, and the R script related to detection and prediction of pressurization and hydrogen generation. Electronic_Appendix_A.zip includes an MS Excel tool that integrates key thermochemical processes of the Centre for Energy and Environmental Technologies – Explorer (CEETe, https://ceet.vsb.cz/en/CEETe/), which is used as a basis for https://shinyenet.vsb.cz/. Electronic_Appendix_B.zip is given as the MS Excel table "CEET_raw_data_gasification.xlsx", which provides raw data sets from repeated gasification measurements. For example, for 750 oC gasification, results of four independent measurements are provided, in columns C, D, E, F. This article provides detailed regression analyses of the top six syngas components (O2, CO2, H2, CO, CH4, N2), which cover approximately 98% of syngas, the line ‘Sum of O2, CO2, H2, CO, CH4, N2’, i.e. B26 of the table. The absolute error of gasification measurements does not exceed 3.08 %, see the line ‘Absolute error (0 % in theory)’, the cell B28. Electronic_Appendix_C.zip is added to provide full reproducibility of results, which are related to bio-wood waste from the Czech Republic. However, the Python file "polynom.py", thanks to its general regression approach, can be applied to various alternative fuels for various countries. The Python file provides automated regression analyses and is accessible in the open repository, which automatically generates MS Word report with color plots and tables given as the separate Word file "report_polynoms_ceet.docx". The Python file "polynom.py" and the regression report "report_polynoms_ceet.docx" provide a detailed analysis of the important syngas components (CO2, H2, CO, CH4, and N2) for regression models of degree 2 to 6, where the total of 5 times 5, i.e. 25, regression models are constructed and analyzed. The performance of these models is analyzed by various statistical metrics: max. absolute error, max. percentage error and mean squared error (MSE). The Python code and report include the most valuable regression models according to MSE. Finally, the script "polynom_cross_validation.py" performs k-fold cross-validation for developed polynomial regression models to predict important syngas components (CO2, H2, CO, CH4, N2) and generates a MS Word document report. The document "report_polynoms_kfold_discussion.docx" includes both the results and discussion of these cross-validation results. 7. Funding: The authors received support from:1. the Ministry of Education, Youth and Sports of the Czech Republic through the e–INFRA CZ (ID: 90254) project, 2. the EU funds under the project “Increasing the resilience of power grids in the context of decarbonisation, decentralisation and sustainable socioeconomic development”, CZ.02.01.01/00/23_021/0008759, through the Operational Programme Johannes Amos Comenius, and3. the Technology Agency of the Czech Republic through the CEET project—“Center of Energy and Environmental Technologies” TK03020027.Dejan Brkić additionally wants to acknowledge: This work has been supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, grant number: 451-03-136/2025-03/200102.