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，模仿动物体内自然产生的激素（如雌激素或睾酮）的作用并阻碍其作用。这些化学物质的影响范围广泛，包括生殖障碍和发育问题。不幸的是，多种化合物都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）和制造原材料（例如双酚）。虽然这些化合物对人类健康的全面影响尚不清楚，但从饮用水中去除它们是水处理中一个新出现的问题，而且随着更多 EDC 的发现，这个问题只会变得更加重要。从饮用水中去除特定 EDC 的最佳方法并不总是显而易见的，标准做法是筛选不同的可能方法以找到最佳方法。这在金钱和时间方面都非常昂贵，而且最适合一种饮用水源的方法可能并不总是适合另一种成分不同的水源。该项目利用了计算化学和机器学习的力量(ML) 加快寻找可去除 EDC 的材料，从原子模拟开始，结合实验室实验的结果，在计算机中研究水净化。这项工作的高潮将是开发一个高效、强大的机器学习框架，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，通过建议重点关注的候选材料来节省大量实验时间，并允许水管理行业迅速采取行动处理新发现的 EDC。