摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-研究人员详细介绍了机器学习的新数据。根据NewsRx记者在北达科他州格兰德福克斯的新闻报道，研究表明：“由于影响膜性能的各种因素的复杂相互作用，改变反渗透(RO)膜性能是具有挑战性和耗时的。为了应对这一挑战，我们已经探索了使用机器学习(ML)在反渗透膜表面接枝聚酰胺(PA)的潜力，以增加透水性并克服透水性/选择性权衡的局限性。这项研究的财政支持者包括北达科他州大福克斯市、美国膜技术协会(AMTA)、美国垦殖局。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Grand Forks, North Dakota, by NewsRx journalists, research stated, “Modifying reverse-osmosis (RO) membrane performa nce is challenging and time-consuming due to the complex interplay of various fa ctors that influence the membrane’s performance. To address this challenge, we h ave explored the potential of using machine-learning (ML) to graft the polyamide (PA) surface of an RO membrane to increase water permeability and overcome the limitations of the permeability/selectivity tradeoff.” Financial supporters for this research include City of Grand Forks, State of Nor th Dakota, American Membrane Technology Association (AMTA), United States Bureau of Reclamation.
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