首页|Researchers from University of Tyumen Report New Studies and Findings in the Area of Machine Learning (Application of Machine Learning To Fischer-tropsch Synthesis for Cobalt Catalysts)
Researchers from University of Tyumen Report New Studies and Findings in the Area of Machine Learning (Application of Machine Learning To Fischer-tropsch Synthesis for Cobalt Catalysts)
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NETL
NSTL
Amer Chemical Soc
A new study on Machine Learning is now available. According to news originating from Tyumen, Russia, by NewsRx correspondents, research stated, “Machine Learning was used to make a prediction model for six property parameters (COconv (%); CH4 (%); CO2 (%); C2-C4 (%); C5+ (%); and COconv*C5+ (%)) from 16 feature parameters of 169 Fischer-Tropsch synthesis experiments for cobalt catalyst. The Random Forest method was chosen as the 'black-box' prediction tool, and cross-validation tests revealed small mean average errors: 10.9 +/- 1.8; 3.7 +/- 0.7, 1.5 +/- 0.6, 3.6 +/- 0.8, 7.4 +/- 1.4 and 9.0 +/- 1.3, respectively.” Funders for this research include , University of Tyumen, Tyumen Oblast Government, as part of the West-Siberian Interregional Science and Education Center's project, University of Tyumen, Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the University of Tyumen, “The most important feature parameters were Brunauer-Emmett-Teller (BET) surface area, pressure (P), and temperature (T). The Decision Tree was chosen to build an explainable model, which revealed that BET in the range of [199.5, 529] leads to small COconv (%); C5+(%); and COcon*C5+(%) values. Outside this range, the samples tend to have large conversion and selectivity values. Most importantly, the BET effect is not linear or monotonic; it can be extracted using machine learning methods, and current work has shown how to use it. The resulting rules can be used for further experiments to maximize the efficiency and develop a new catalyst system.”
TyumenRussiaCobaltCyborgsEmerging TechnologiesMachine LearningTransition ElementsUniversity of Tyumen
NETL
NSTL
Amer Chemical Soc
