Analysis of 27 supervised machine learning models for the co-gasification assessment of peanut shell and spent tea residue in an open-core downdraft gasifier

The producer gas (PG) obtained through thermochemical processing of the various renewable biomass types contributes to Sustainable Development Goal-7 (SDG-7). Hence, this study examined 27 supervised and multiple-input single-output ML models in terms of performance metrics and accuracy to predict CO, H2, CH4, and CO2 compositions, as well as PG's HHV in the co-gasification of peanut shell (PS) and spent tea residue (STR). Multiple trial experiments were tested with various equivalence ratios (ERs) and mixing ratios (MRs) on a 90 m3/h gas-yield open-core, down-draft gasifier with air as the gasification medium. About 18 models were chosen after evaluating their performance metrics in estimating relevant parameters. The accuracy of those models is assessed based on 12 sample runs with varying ER and MR. The findings revealed that 16 models from the linear, neural network, support vector machine, Gaussian process, tree, and ensemble categories were reliable ML models. The chosen 16 models' R2 values for CO, H2, CH4, CO2, and HHVPG are above 0.931, 0.937, 0.962, 0.806, and 0.971, respectively, with the exception of TRE. Additionally, the models demonstrated a prediction accuracy of >95 % for all of the investigated parameters in the co-gasification of PS and STR when compared to random experimental runs.