首页|Studies from York University in the Area of Machine Learning Published (Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction)
Studies from York University in the Area of Machine Learning Published (Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction)
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NETL
NSTL
Elsevier
A new study on artificial intelligence is now available. According to news reporting out of Toronto, Canada, by NewsRx editors, research stated, “The adhesive properties of microorganisms on the surface of minerals play an important role in the biooxidation efficiency of sulfidic refractory gold ores.” Financial supporters for this research include York University; Mitacs; Natural Sciences And Engineering Research Council of Canada; National Research Council Canada. Our news editors obtained a quote from the research from York University: “In this research, the simultaneous effects of monosaccharides, ore content, pyrite content, and time on the activity and growth rate of Ferroplasma acidiphilum-from native Acid Mine Drainage (AMD)- was investigated during biooxidization alongside finding the best machine learning approach for the prediction of process efficiency using the independent variables. The results revealed that the optimum condition for reaching the highest pyrite dissolution ( 75 %) is 15 days of operating time, pyrite content of 7.2 wt%, and ore content of 5 wt%, pH of 1.47, and D-+-sucrose, D-+-galactose, and D-+-fructose concentrations of 0.52, 0.09, and 0.12 wt%, respectively. The results of the model comparison indicated that the Artificial Neural Network (ANN) model was able to predict the experimental results of this study with acceptable accuracy and better than Genetic Programming (GP) and Polynomial Regression informed by Response Surface Methodology (PR-RSM) from experimental data.”
York UniversityTorontoCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
