Abstract
Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Athens, Ohio, by NewsRx correspondents, research stated, “We have examined performance of various machine learning (ML) methods (artificial neural network, random forest, support vector-machine regression, and K nearest neighbors) in predicting the kinetics of hydrothermal carbonization (HTC) of cellulose, poplar, and wheat straw performed under two different conditions: first, isothermal conditions at 200, 230, and 260 degrees C, and second, with a linear temperature ramp of 2 degrees C/min from 160 to 260 degrees C. The focus of this study was to determine the predictability of the ML methods when the biomass type is not known or there is a mixture of biomass types, which is often the case in commercial operations. In addition, we have examined the performance of ML methods in interpolating kinetics results when experimental data is available for only a handful of time-points, as well as their performance in extrapolating the kinetics when the experimental data from only a few initial time-points is available.” Financial supporters for this research include United States Department of Agriculture (USDA), NSF XSEDE grant.
NETL
NSTL
Springer Nature