首页|Department of Chemical Engineering and Applied Chemistry Reports Findings in Machine Learning (Predicting the occurrence of substituted and unsubstituted, polyc yclic aromatic compounds in coking wastewater treatment plant effluent using mac hine ...)
Department of Chemical Engineering and Applied Chemistry Reports Findings in Machine Learning (Predicting the occurrence of substituted and unsubstituted, polyc yclic aromatic compounds in coking wastewater treatment plant effluent using mac hine ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting out of Toronto, Canada, by Ne wsRx editors, research stated, “Organic contaminants such as polycyclic aromatic compounds (PACs) occurring in industrial effluents can not only persist in wast ewater but also undergo transformation into more toxic and mobile substituted he terocyclic products during their treatment. Thus, predicting the occurrence of PACs and their heterocyclic derivatives (HPACs) in coking wastewater is of utmost importance to reduce the environmental risks of receiving water bodies.”
TorontoCanadaNorth and Central Ameri caCyborgsEmerging TechnologiesMachine Learning
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