Efficient sea water Purification using Hybrid Nanofiltration system and ML for Optimization

The Earth has an abundance of water, about 70 percent of the globe is covered with water, wherein only 2.5 percent of freshwater is available for human usage. Due to the major issue of over-population and lack of pure water bodies, the problem of pure water scarcity has reached it’s peak. Hence, there is a demand of a system wherein efficiently pure water can be processed and there is a smooth flow of pure water. We have proposed a model which is cost-effective, environmental friendly, and responsive to the limitations of existing desalination and filtration plants making it an absolute system. The proposed model is 3 layer hybrid system, which is interconnected and is sequential. The system is a combination of sedimentation, amyloid carbon hybrid membranes and graphene oxide technology for complete purification of seawater. This paper presents a comparison between the existing techniques with our proposed model resolving better aspects. Additionally, the paper consists of the laboratory tested results of seawater, groundwater and tap water and by the analysis of that result we have shown the amount of purification required for seawater. As membranes are very sensitive and it is needed to change with time, we have proposed the machine learning approach which will look after the saline water which is coming inside the system and will keep track on water quality of incoming water. Also, we will use supervised algorithms and computer vision which will keep watch on membranes and will give alert when there is need to clean the membrane which will reduce the chance of changing them frequently. And hence this ai technology will increase the efficiency of the model.