Experimental Data on Biomass Combustor Design, Operating Conditions and Performance: A Multi-Study Dataset for Machine Learning and Optimization

This dataset comprises an extensive compilation of experimental data on biomass gasification reactor design characteristics, operating conditions, and performance indicators, extracted from approximately 570 published research articles. It was developed to provide a structured, literature-derived data resource for data-driven modelling, performance prediction, design-space exploration, and optimization of biomass gasification systems. The dataset captures a range of reactor design and operational parameters reported in the reviewed literature, including reactor height, reactor diameter, biomass feed rate, gas flow rate, bed particle size, bed material type, and biomass feedstock type, among other relevant variables. The corresponding performance indicators include biomass conversion efficiency (BCE), carbon conversion efficiency (CCE), and thermal efficiency (TE). The compiled data are intended to support the development, training, validation, and evaluation of machine-learning models capable of establishing relationships between reactor design and operating parameters (input variables) and gasification performance indicators (output variables). Such predictive models can subsequently be employed to investigate the effects of individual and combined design variables, explore feasible reactor operating spaces, identify favourable operating conditions, and determine optimal parameter combinations for improved gasification performance. The dataset also provides a foundation for comparative studies, further machine-learning research, sensitivity analysis, and the development of data-driven approaches to biomass gasification reactor design and optimization. By consolidating information dispersed across numerous published studies into a structured data resource, it seeks to facilitate access to existing research data, reduce duplication of data extraction efforts, and encourage further investigation and reproducibility in biomass gasification research. Data provenance and attribution: The data were extracted from previously published scientific articles. The original publications and their corresponding bibliographic references are provided alongside the extracted data to facilitate source verification, proper attribution, and traceability of individual data entries. Users are encouraged to consult the original publications when interpreting or reusing the data, particularly where differences in experimental configurations, measurement procedures, definitions, and reporting conventions may affect comparability. This dataset is intended for academic and research purposes, particularly in the fields of biomass gasification, reactor engineering, computational modelling, artificial intelligence, machine learning, and process optimization.