#5052. IoT fusion based model predictive pid control approach for oil pipeline infrastructure

August 2026publication date
Proposal available till 12-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Modeling and Simulation;
Safety, Risk, Reliability and Quality;
Information Systems and Management;
Computer Science Applications;
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Abstract:
“Pipeline integrity” implies the concepts of failure prevention, inspection and repair that help inspection engineer to maximize their safety. In the modern pipeline infrastructure, regulating the pressure and flow rate throughout transportation plays a crucial role with tedious data frameworks. To afford a suitable data fusion platform for IoT-based monitoring and control, Model Predictive Control is formulated. In the proposed work, data fusion is integrated with model predictive control to improve the robustness by tackling different scenario risk rates to formulate the final model for real-time implementation. The accurate models are selected based on the scheduled task identification and risk probability characteristics in the oil pipelines. The final control signal will be actuated based on the multi-criteria decision-making method using cloud server data with field PID control signal rates. Finally, the final output illustrates the effectiveness of the proposed controller with a simulation example, employing real fluid pipeline pressure and flow rate data under a variety of hard constraints.
Keywords:
Data fusion; Fluid transportation; IoT; Model predictive control

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