IoT Based monitoring and control of fluid transportation using machine learning

N. S, Sivakumar (2020) IoT Based monitoring and control of fluid transportation using machine learning. Computers & Electrical Engineering, 89. ISSN 00457906

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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.

Item Type: Article
Uncontrolled Keywords: DCS plant, LQR based PID controller, Fluid transportation system, K-means clustering, Pressure and Flow rateIoT
Subjects: Engineering > Computer engineering
Engineering > TK Electrical engineering
Depositing User: ePrints deposit
Date Deposited: 14 Jan 2021 13:13
Last Modified: 05 Dec 2022 07:48

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