Machine Learning for Computational Water Treatment

用于计算水处理的机器学习

• 批准号: EP/X033244/1
• 负责人: David Wilkins
• 金额: $ 49.91万
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX033244%2F1
• 关键词: Machine; Learning; Computational; Water; Treatment

Endocrine-disrupting chemicals (EDCs) affect the hormone systems of animals, mimicking the effects of naturally occurring hormones (such as oestrogen or testosterone) in animals and blocking their action. The effects of these chemicals are wide-ranging and include reproductive failure and developmental problems. Unfortunately, a huge variety of compounds have the potential to disrupt the endocrine system, including pharmaceuticals (e.g. antibiotics), personal care products (e.g. deodorants) and raw materials for manufacturing (e.g. bisphenols). While the full effect of these compounds on human health is not yet known, their removal from drinking water is an emerging problem in water treatment, and one that only becomes more important as more EDCs are discovered. The best way to remove a given EDC from drinking water is not always obvious, and the standard practice is to screen different possible methods to find the optimum. This can be very costly in terms of both money and time, and the method that is best for one source of drinking water may not always be best in another source whose composition is different.This project harnesses the power of computational chemistry and machine-learning (ML) to speed up the search for materials for EDC removal, beginning with atomistic simulations to study water decontamination in silico, in tandem with the results of laboratory experiments. The culmination of this work will be the development of an efficient and robust ML framework that can predict the ability of a material to remove an endocrine disruptor from drinking water, saving a significant amount of experimental time by suggesting candidate materials to focus on, and allowing the water management industry to act quickly to deal with newly discovered EDCs.

内分泌干​​扰化学物质（EDC）会影响动物的激素系统，模仿动物自然存在的激素（例如雌激素或睾丸激素）的影响并阻止其作用。这些化学物质的作用是广泛的，包括生殖衰竭和发育问题。不幸的是，各种各样的化合物有可能破坏内分泌系统，包括药品（例如抗生素），个人护理产品（例如除臭剂）和制造原料（例如双酚）。尽管这些化合物对人类健康的全部作用尚不清楚，但它们从饮用水中取出是一个新兴的水处理问题，并且随着发现更多的EDC的发现，这种问题才变得越来越重要。从饮用水中删除给定的EDC的最佳方法并不总是很明显，标准做法是筛选不同可能的方法以找到最佳。 This can be very costly in terms of both money and time, and the method that is best for one source of drinking water may not always be best in another source whose composition is different.This project harnesses the power of computational chemistry and machine-learning (ML) to speed up the search for materials for EDC removal, beginning with atomistic simulations to study water decontamination in silico, in tandem with the results of laboratory experiments.这项工作的结晶将是开发高效且可靠的ML框架，该框架可以预测材料从饮用水中去除内分泌干扰物的能力，从而通过建议候选材料专注于饮用水，并允许水管理行业迅速采取行动来处理新发现的EDC，以节省大量的实验时间。