Drinking water is vital for everyday life. We are dependent on water for everything from cooking to sanitation. Without water, it is estimated that the average, healthy human won’t live more than 3–5 days. The water is therefore essential for the productivity of our community. The water treatment process (WTP) may vary slightly at different locations, depending on the technology of the plant and the water it needs to process, but the basic principles are largely the same. As the WTP is complex, traditional laboratory methods and mathematical models have limitations to optimize this type of operations. These pose challenges for water-sanitation services and research community. To overcome this matter, deep learning is used as an alternative to provide various solutions in WTP optimization. Compared to traditional machine learning methods and because of its practicability, deep learning has a strong learning ability to better use data sets for data mining and knowledge extraction. The aim of this survey is to review the existing advanced approaches of deep learning and their applications in WTP especially in coagulation control and monitoring. Besides, we also discuss the limitations and prospects of deep learning.
Part of the book: Desalination and Water Treatment