Machine Learning Based Decision-Making Model to Determine Size of Micro Nano Bubble
Pinisetti Swami Sairam1, Ravi Gunupuru2, Jitendra K Pandey3
1Pinisetti Swami Sairam, Research Fellow, University of Petroleum and Energy Studies, Bidholi, Dehradun, Uttarakhand.
2Ravi Gunupurur, Department of Chemistry, University of Petroleum and Energy Studies, Bidholi, Dehradun, Uttarakhand.
3Jitendra K Pandey, University of Petroleum and Energy Studies, Bidholi, Dehradun, Uttarakhand,
Manuscript received on 03 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 8062-8064 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6432098319/2019©BEIESP | DOI: 10.35940/ijrte.C6432.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Aeration, Machine Learning, Micro Nano Bubbles, Water Treatment.
Scope of the Article: Machine Learning