IoT Based monitoring and control of fluid transportation using machine learning. (January 2021)

• Record Type: Journal Article
• Authors: Bhaskaran, Priyanka E.; Maheswari, C.; Thangavel, S.; Ponnibala, M.; Kalavathidevi, T.; Sivakumar, N.S.
• Is Part Of: Computers & electrical engineering. Volume 89(2021)
• Publication Date: 2021-01
• Subjects: DCS plant -- LQR based PID controller -- Fluid transportation system -- K-means clustering -- Pressure and Flow rate -- IoT; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106899
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: IoT and Machine learning incorporated online monitoring and control of fluid pipelines. Narrow Band IoT module to acquire selected sensor data for concise fluid tpipeline performance evaluaition. Performance of SCADA with IoT is compared with SCADA without IoT. Real-time results confirm LQR-PID controller assures perfect control on pressure and flowrate. Based on K-means clustering computing result online server initiates control action during leaks and cracks in the fluid pipelines. Abstract: It is important to concentrate on monitoring and control of the pipeline transportation system before the failure resulting in fatal accidents. To enhance the supervision performances, the SCADA (Supervisory Control and Data Acquisition) platform is incorporated with IoT by utilizing the NB-IOT module holding a high-level engineering interface. In the proposed methodology, SCADA with the LQR-PID controller serves as Local Intelligence. When the local intelligence fails to react proactively during risk occurrences, immediately its performance is deactivated by the webserver through the NB (Narrow Band)-IoT module. For experimental real-time validation of the proposed work, a lab-scale DCS (Distributed Control System) based fluid transportation system is undertaken where flow and pressure prevail to be the most influencing parameters during risk occurrences in the pipelines. Also, the performance analyses are validated experimentally using unsupervised K-means clustering to identify abnormality caused by blockage and crack in the pipeline on the cloud-stored data. Graphical abstract: Image, graphical abstract