Rare Earth Extraction Content Control Using Elman Neural Prediction
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Solution Overview
Problem
The rare earth extraction process is complex, nonlinear, and strongly coupled, making it difficult to establish an accurate model for control, leading to suboptimal control of component contents and product quality due to the limitations of existing control algorithms such as PID, fuzzy control, and adaptive robust control.
Innovation Solution
A prediction control method using an Elman neural network model is established to predict output values, calculate optimal set values through steady-state optimization, and dynamically adjust extractant and detergent flow increments to control component contents, enabling adaptive and optimal control of the rare earth extraction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional control algorithms (PID, fuzzy control, adaptive robust control) are used, then the control system is simple to implement, but the control precision and adaptability are insufficient due to the complex nonlinear characteristics of the rare earth extraction process
Solution Approach 1:
The patent replaces conventional control algorithms with a neural network-based prediction control system. The neural network model learns the nonlinear relationships in the rare earth extraction process from historical data, substituting traditional mechanical control approaches with an intelligent adaptive system that can handle the process complexity while achieving superior control precision
Solution Approach 2:
The patent transforms the control approach by changing from fixed-parameter conventional controllers to adaptive neural network parameters that dynamically adjust based on process conditions. The prediction control uses learned parameters from training data to optimize component content control, adapting to the nonlinear and time-variant characteristics of the extraction process
2Measurement precision
If static modeling based on cascade extraction equilibrium theory is used, then the model is simple to establish, but it fails to capture dynamic characteristics and inter-stage interactions, resulting in large modeling errors
Solution Approach 1:
The patent replaces static equilibrium-based modeling with a dynamic neural network model. The neural network learns dynamic relationships and inter-stage interactions from process data, substituting the simplified static model with an intelligent system that captures the true dynamic behavior of the extraction process while maintaining computational efficiency
Solution Approach 2:
The patent performs preliminary training of the neural network model using historical process data before actual control operation. This preliminary learning phase allows the model to capture complex dynamic characteristics and interactions in advance, so that during actual operation, the model can accurately predict component contents without requiring complex real-time calculations
3Extent of automation
If the rare earth extraction process is controlled by seasoned operating personnel based on experience, then the control strategy can handle complex situations, but the automation level is low and cannot adjust parameters online in real-time
Solution Approach 1:
The patent implements a self-service control system where the neural network automatically learns from historical data and makes real-time control decisions without human intervention. The prediction control system serves itself by continuously monitoring process conditions and adjusting parameters online, replacing manual experience-based control with autonomous intelligent control that achieves both high automation and precision
Solution Approach 2:
The patent incorporates feedback mechanisms where the neural network model continuously receives process data, predicts component contents, and adjusts control parameters based on prediction errors. This closed-loop feedback system enables real-time adaptive control that maintains high precision while achieving full automation, learning from ongoing process variations
Data Source
AI summary
The present invention discloses a prediction control method and system for component contents in a rare earth extraction process. The prediction control method includes: establishing an Elman neural network model of a rare earth extraction process; obtaining a predicted output value of the rare earth extraction process through the Elman neural network model of the rare earth extraction process; calculating an optimal set value through steady-state optimization; dynamically predicting an extractant flow increment and a detergent flow increment based on the predicted output value and the optimal set value; and controlling component contents in the rare earth extraction process according to the extractant flow increment and the detergent flow increment. According to the present invention, an optimal setting problem of a set point is solved through steady-state optimization calculation, and then an optimal control effect is achieved in combination with a dynamic prediction control method, thereby achieving optimal setting control over the component contents in the rare earth extraction process, and ensuring the product quality of the rare earth extraction process.