Real-time High-fidelity Reconstruction of CSTR Dynamics with Sparsed Sensor Data.

Abstract Submission: Accurate real-time monitoring of continuous stirred-tank reactor (CSTR) dynamics is crucial for optimizing chemical processes and ensuring operational safety. Traditional modeling methods often struggle to capture the complex behaviors within a CSTR, especially when limited by sparse sensor data. This research introduces a novel approach for real-time, high-fidelity reconstruction of CSTR dynamics using strategically placed sparse sensors. By analyzing comprehensive flow data and investigating the optimal number and placement of sensors, the study enhances reconstruction accuracy while minimizing instrumentation costs. This methodology enables improved monitoring, control, and optimization of CSTR operations under practical constraints, advancing the effectiveness of this system.

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