Data Analysis for Real Time Monitoring of Heat Exchangers
Swetha1, Piyush Jain2, Juhi Dadhich3, Hiya Choudhary4
1Swetha P, Asst. Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
2Piyush Jain, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
3Juhi Dadhich, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
4Hiya Choudhary, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
Manuscript received on 12 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 1953-1957 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F13500476S519/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: A Heat Exchanger is a device which is used to exchange heat between two or more fluids with and without a medium and can be used in both heating and cooling processes. There are many industries where these are used such as Space, Chemical, Biogas, Thermal, Sewage treatment, Supply chain, Power stations, Refrigeration, etc. All these industries require real-time monitoring of temperature, humidity, the pressure to ensure the stability and durability of the commodities present in it. The monitoring of these values is obtained by various sensors usually used in IOT. In our paper, an approach towards this situation is dealt where the parameters (temperature, humidity, pressure) are monitored on a real-time basis. The data collected from the IoT devices are continuously sent to the cloud platform. The analysis of this dataset will be done using a machine learning (linear regression) algorithm to predict the future parameter values. The analysis result is visualized as a graphical representation on the monitor which is accessible by the users. This predicted value will be compared with the threshold value set, and an actuator-based response will be generated and an SMS is then sent to the owner. Based on this the owner can take necessary actions. This paper has a greater impact in various industries through which the heat exchangers can be monitored regularly by avoiding major disasters which take away the lives of humans.
Keywords: Atmospheric Parameters, Heat Exchangers, Linear Regression, Sensors.
Scope of the Article: Data Analytics