
This dataset supports the article “Mechanistic Machine Learning for Biomass Pyrolysis: Integrating Lignocellulosic Structure, Thermal Severity, and Multi-Objective Optimization for Organic Bio-Oil Yield.” It contains experimental and derived data related to biomass pyrolysis, including lignocellulosic structural characteristics, thermal severity parameters, and resulting product yields. The data were used to develop and validate mechanistic machine learning models and to perform multi-objective optimization aimed at maximizing organic bio-oil yield. The dataset is provided in Excel format (Data.xlsx) and is intended to facilitate reproducibility, model benchmarking, and further research in biomass conversion and data-driven process optimization. This is a derived dataset created from the original dataset published by https://doi.org/10.1016/j.fuel.2025.135000. The data were processed, additional features were added, and reformatted; no new data were collected.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
