Ryan P Bialaszewski, John M Gaddis, Blake Martin, Philippe Dentino, John Ronnau
Bridging Bone Health: Osteoporosis Disparities in the Rio Grande Valley
Cureus
supercritical water gasification scwg lignocellulosic biomass promising pathway production hydrogen however scwg complex thermochemical process modeling challenging via conventional methodologies therefore eight machine learning models linear regression lr gaussian process regression gpr artificial neural network ann support vector machine svm decision tree dt random forest rf extreme gradient boosting xgb categorical boosting regressor catboost particle swarm optimization pso genetic algorithm ga optimizer developed evaluated prediction h2 co co2 ch4 gas yields scwg lignocellulosic biomass total 12 input features scwg process conditions temperature time concentration pressure biomass properties c h n vm moisture ash real feed utilized prediction gas yields using 166 data points among machine learning models boosting ensemble tree models xgb catboost demonstrated highest power prediction gas yields psooptimized xgb best performing model h2 yield test r2 084 psooptimized catboost best prediction yields ch4 co co2 test r2 values 083 094 092 respectively effectiveness pso optimizer improving prediction ability unoptimized machine learning model higher compared ga optimizer gas yields feature analysis using shapley additive explanation shap based best performing models showed 2193 temperature 2485 c 1693 ash 2973 c dominant features prediction h2 ch4 co co2 gas yields respectively even though temperature dominant feature cumulative feature importance biomass characteristics variables c h n vm moisture ash real feed group higher scwg process condition variables temperature time concentration pressure prediction gas yields shap twoway analysis confirmed strong interactive behavior input features prediction gas yields