Prevent, Detect, Respond, Mitigate Liquid Sodium Leakage, and Fire Accidents using AI
Praveen Sankarasubramanian1, E. N. Ganesh2
1Praveen Sankarasubramanian*, Research Scholar, Vels Institute of Science, Technology and Advanced Studies, (VISTAS), Chennai, Tamilnadu, India.
2Dr. E. N. Ganesh, Dean School of Engineering, Vels Institute of Science, Technology and Advanced Studies, (VISTAS), Chennai, Tamilnadu, India.
Manuscript received on May 15, 2020. | Revised Manuscript received on May 27, 2020. | Manuscript published on June 30, 2020. | PP: 7-11 | Volume-9 Issue-5, June 2020. | Retrieval Number: D9113049420/2020©BEIESP | DOI: 10.35940/ijeat.D9113.069520
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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: Liquid /Fluid Sodium is very risky in nature[1]. It gets ignited thusly when introduced to air or water. They recall repressions for smothering administrators. Social wellbeing and affirmation are basic. In many impelled organizations and present-day nuclear force stations, liquid sodium utilized as a basic coolant. This technique offers challenges to nuclear powerhouses. The fire remains an overall supporter of intensity plants. Research on capable fire ID and cautioning structures has become a fervently discussed issue in ventures. In many force stations and manufacturing plants, PLC or microcontroller-based controllers are utilized. Most of the customary frameworks will be rule-based. Now and again, they give counterfeit cautions. There is a need to naturally recognize outpouring and fire utilizing most recent advances like man-made reasoning. These days, most of the enterprises are fixed with CCTV or other observing frameworks. Our point is to utilize Video and Traditional way to deal with distinguish and moderate fire. More secure force stations created utilizing authentic mishap information and encounters. This paper discusses liquid metal spillage disclosure frameworks and abbreviates its state of-data achievement. Different fire revelation techniques explored, and their characteristics and inadequacies are highlighted. An AI based methodology is suggested that will manage the fire recognizable proof. Proposed system consolidates new advances like Computer vision, CNN calculation with the customary sensor or micro-controller or PLC based application to identify the smoke and fire segments. Sensors recognize the spillages, spillages of the fluid metals. Computer Vision and CNN calculations recognizes the nearness of fire from a current video source. This will limit the opportunity of spillages and different dangers.
Keywords: CNN, NUMPY, PYTHON, OPENCV, AI.