Machine learning has been widely used for large data processing with varied scope of application aspects. In this paper machine learning is used to determine the size of air bubbles that can be generated in an optimal condition of various parameters such as gas flow rate, water temperature and operating pressure of the system. Air bubbles have significant role to play when it comes to water treatment. Bubbles having significance in volume are proportionally valued when it comes to extent of treatment. The research concludes with a conceptual model influenced by machine learning approach that can estimate best combination of the parameter that are feasible for generation of most efficient generation.
Micro Nano Bubble (MNB) have been researched widely for their potental use in different applications such as disinfection, removal of toxic material from water, separation etc. Various parameters such as water temperature, system operating pressure, gas types and gas flow rates contribute in the size of the MNB generated. This research illustrates the influence of various parameters and their significance in size of MNBs generated. Experiments conducted showed that feed gas as a vital parameter for generating bubbles in nano size
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