Machine learning based decision-making model to determine size of micro nano bubble

Abstract

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.