Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

IF 15 2区环境科学与生态学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Environmental Chemistry Letters Pub Date : 2024-05-21 DOI:10.1007/s10311-024-01748-w

Voravich Ganthavee, Antoine Prandota Trzcinski

{"title":"Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review","authors":"Voravich Ganthavee, Antoine Prandota Trzcinski","doi":"10.1007/s10311-024-01748-w","DOIUrl":null,"url":null,"abstract":"<div><p>The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters.</p></div>","PeriodicalId":541,"journal":{"name":"Environmental Chemistry Letters","volume":"22 5","pages":"2293 - 2318"},"PeriodicalIF":15.0000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10311-024-01748-w.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Chemistry Letters","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10311-024-01748-w","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters.

Abstract Image

查看原文本刊更多论文

优化制药废水处理系统的人工智能和机器学习：综述

在快速工业化和城市化的背景下，大多数天然水域都受到了污染，因此，获得清洁的饮用水正成为主要的健康问题之一。此外，大多数污染物（如抗生素）都逃不过传统的废水处理方法，从而被排放到生态系统中，这就要求采用先进的废水处理技术。在此，我们将回顾利用人工智能和机器学习优化制药废水处理系统的情况，重点关注水质、消毒、可再生能源、生物处理、区块链技术、机器学习算法、大数据、网络物理系统和自动化智能电网配电网络。人工智能可以监测污染物、促进数据分析、诊断水质、简化自主决策以及预测工艺参数。我们讨论了技术可靠性、能源资源和废水管理、网络复原力、安全功能、自动化平台和分布式联盟的稳健多维性能以及水质参数异常波动的稳定性等方面的进展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Environmental Chemistry Letters 环境科学-工程：环境

CiteScore

32.00

自引率

7.00%

发文量

175

审稿时长

2 months

期刊介绍： Environmental Chemistry Letters explores the intersections of geology, chemistry, physics, and biology. Published articles are of paramount importance to the examination of both natural and engineered environments. The journal features original and review articles of exceptional significance, encompassing topics such as the characterization of natural and impacted environments, the behavior, prevention, treatment, and control of mineral, organic, and radioactive pollutants. It also delves into interfacial studies involving diverse media like soil, sediment, water, air, organisms, and food. Additionally, the journal covers green chemistry, environmentally friendly synthetic pathways, alternative fuels, ecotoxicology, risk assessment, environmental processes and modeling, environmental technologies, remediation and control, and environmental analytical chemistry using biomolecular tools and tracers.