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.

获得清洁和饮用水正在成为主要的健康问题之一，因为在快速工业化和城市化的背景下，大多数天然水体现在都受到污染。此外，抗生素等大多数污染物都逃过了传统的废水处理，因此被排放到生态系统中，需要先进的废水处理技术。这里我们回顾一下利用人工智能和机器学习来优化制药废水处理系统，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过程参数。我们讨论技术可靠性、能源和废水管理、网络弹性、安全功能、自动化平台和分布式联盟的强大多维性能以及稳定水质参数异常波动方面的进步。