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Constrained Model Predictive Control Based on Event Triggering for the Rare Earth Extraction Process.

Zhiyong Zhang¹, Jianyong Zhu¹, Cong Xiong¹

¹School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, Jiangxi 330013, P.R. China.

|November 16, 2023

Summary

This study introduces an event-triggered constrained model predictive control for optimizing rare earth extraction. The new method accurately adjusts reagent dosage, improving process efficiency and reducing manual intervention.

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Area of Science:

Chemical Engineering
Process Control
Materials Science

Background:

Manual adjustment of reagent dosage in rare earth extraction is prone to errors and inefficiencies.
Optimizing reagent dosage is critical for maximizing yield and minimizing waste in rare earth processing.

Purpose of the Study:

To develop an automated control system for precise reagent dosage in rare earth extraction.
To improve the efficiency and accuracy of rare earth extraction processes through advanced control strategies.

Main Methods:

A linear predictive system based on a state-space model was established.
A feedback correction link was implemented to minimize prediction errors.
An event-triggering mechanism with a designated threshold was designed for controller updates.

Main Results:

The proposed model predictive control effectively calculated optimal reagent dosages.
Simulation results demonstrated the system's ability to reduce prediction errors.
The event-triggering mechanism ensured timely controller adjustments for sustained efficiency.

Conclusions:

The event-triggered constrained model predictive control offers a robust and efficient solution for rare earth extraction.
This automated approach enhances process stability and optimizes reagent utilization.
The findings support the adoption of advanced control systems in chemical processing for improved outcomes.