Data and code for: Replicate-aware, chemistry-informed ensemble learning for uncertainty-aware and energy-conscious biodiesel process optimization
Year: 2026
Type: dataset
Abstract: Data set, code and figure data supporting the article "Replicate-aware, chemistry-informed ensemble learning for uncertainty-aware and energy-conscious biodiesel process optimization" (submitted to Journal of Cleaner Production). Contents:- 396 alkali-catalysed, energy-assisted transesterification runs (379 distinct operating conditions): molar ratio, catalyst loading, time, temperature, power, alcohol type, catalyst type and measured ester yield.- Executed Jupyter notebook reproducing every number, table and figure of the article (repeated group-k-fold validation, ReCla-PIE ensemble, conformal prediction intervals, energy-yield Pareto analysis).- Python scripts (pipeline, figures, LaTeX table generation) and pinned software versions (requirements.txt).- Results (results.json, fold-wise scores, out-of-fold predictions), figures (PDF/PNG) and an OriginPro-ready workbook of all figure data.- LaTeX source of the manuscript.
Authors: Attia, Moussa, Yahya, Ilham, Ennetta, Ridha
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221561913
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