A novel extraction model optimization with effective separation coefficient for rare earth extraction process using...

Area of Science:

• Chemical Engineering
• Process Optimization
• Computational Chemistry

Background:

• Mechanistic models for rare earth extraction often overlook agitator efficacy, leading to discrepancies with real-world data.
• Accurate modeling is crucial for optimizing rare earth element (REE) separation processes.

Purpose of the Study:

• To develop a model for accurately determining REE concentration in extraction stages.
• To introduce an effective separation coefficient to improve model accuracy.
• To create an optimized objective function for determining this coefficient.

Main Methods:

• An enhanced differential evolution algorithm, CC-LCTADE, incorporating chaotic sequences, covariance matrices, Cauchy perturbations, and dual-mutation strategies.
• Parameter-adaptive methods for optimizing algorithm control parameters (F, CR, NP).
• Testing against CEC2017 benchmark functions and application to a rare earth extraction simulation system.

Main Results:

• The CC-LCTADE algorithm demonstrated superior performance compared to other methods on benchmark functions.
• The model successfully determined the effective separation coefficient for the rare earth extraction process.
• CC-LCTADE proved effective for simulating and optimizing the actual rare earth extraction system.

Conclusions:

• The proposed CC-LCTADE algorithm enhances accuracy and avoids premature convergence in complex optimization problems.
• The developed model and effective separation coefficient significantly improve the prediction of REE concentrations in extraction.
• CC-LCTADE is a viable and effective tool for the simulation and optimization of industrial rare earth extraction processes.