As a critical strategic rare metal, tellurium finds important applications in solar cells, thermoelectric materials, and infrared detection. Traditional purification processes face challenges such as low efficiency, high energy consumption, and limited purity improvement. This article systematically introduces how artificial intelligence technologies can comprehensively optimize tellurium purification processes.

1. Current Status of Tellurium Purification Technology

1.1 Conventional Tellurium Purification Methods and Limitations

Main Purification Methods:

• Vacuum distillation: Suitable for removing low-boiling-point impurities (e.g., Se, S)
• Zone refining: Particularly effective for removing metallic impurities (e.g., Cu, Fe)
• Electrolytic refining: Capable of deep removal of various impurities
• Chemical vapor transport: Can produce ultra-high-purity tellurium (6N grade and above)

Key Challenges:

• Process parameters rely on experience rather than systematic optimization
• Impurity removal efficiency reaches bottlenecks (especially for non-metallic impurities like oxygen and carbon)
• High energy consumption leads to elevated production costs
• Significant batch-to-batch purity variations and poor stability

1.2 Critical Parameters for Tellurium Purification Optimization

Core Process Parameter Matrix:

2. AI Application Framework for Tellurium Purification

2.1 Overall Technical Architecture

Three-tier AI Optimization System:

1. Prediction layer: Machine learning-based process outcome prediction models
2. Optimization layer: Multi-objective parameter optimization algorithms
3. Control layer: Real-time process control systems

2.2 Data Acquisition and Processing System

Multi-source Data Integration Solution:

• Equipment sensor data: 200+ parameters including temperature, pressure, flow rate
• Process monitoring data: Online mass spectrometry and spectroscopic analysis results
• Laboratory analysis data: Offline testing results from ICP-MS, GDMS, etc.
• Historical production data: Production records from past 5 years (1000+ batches)

Feature Engineering:

• Time-series feature extraction using sliding window method
• Construction of impurity migration kinetic features
• Development of process parameter interaction matrices
• Establishment of material and energy balance features

3. Detailed Core AI Optimization Technologies

3.1 Deep Learning-Based Process Parameter Optimization

Neural Network Architecture:

• Input layer: 56-dimensional process parameters (normalized)
• Hidden layers: 3 LSTM layers (256 neurons) + 2 fully connected layers
• Output layer: 12-dimensional quality indicators (purity, impurity content, etc.)

Training Strategies:

• Transfer learning: Pre-training using purification data of similar metals (e.g., Se)
• Active learning: Optimizing experimental designs via D-optimal methodology
• Reinforcement learning: Establishing reward functions (purity improvement, energy reduction)

Typical Optimization Cases:

• Vacuum distillation temperature profile optimization: 42% reduction in Se residue
• Zone refining rate optimization: 35% improvement in Cu removal
• Electrolyte formulation optimization: 28% increase in current efficiency

3.2 Computer-Aided Impurity Removal Mechanism Studies

Molecular Dynamics Simulations:

• Development of Te-X (X=O,S,Se, etc.) interaction potential functions
• Simulation of impurity separation kinetics at different temperatures
• Prediction of additive-impurity binding energies

First-Principles Calculations:

• Calculation of impurity formation energies in tellurium lattice
• Prediction of optimal chelating molecular structures
• Optimization of vapor transport reaction pathways

Application Examples:

• Discovery of novel oxygen scavenger LaTe₂, reducing oxygen content to 0.3ppm
• Design of customized chelating agents, improving carbon removal efficiency by 60%

3.3 Digital Twin and Virtual Process Optimization

Digital Twin System Construction:

1. Geometric model: Accurate 3D reproduction of equipment
2. Physical model: Coupled heat transfer, mass transfer, and fluid dynamics
3. Chemical model: Integrated impurity reaction kinetics
4. Control model: Simulated control system responses

Virtual Optimization Process:

• Testing 500+ process combinations in digital space
• Identification of critical sensitive parameters (CSV analysis)
• Prediction of optimal operating windows (OWC analysis)
• Process robustness validation (Monte Carlo simulation)

4. Industrial Implementation Pathway and Benefit Analysis

4.1 Phased Implementation Plan

Phase I (0-6 months):

• Deployment of basic data acquisition systems
• Establishment of process database
• Development of preliminary prediction models
• Implementation of key parameter monitoring

Phase II (6-12 months):

• Completion of digital twin system
• Optimization of core process modules
• Pilot closed-loop control implementation
• Quality traceability system development

Phase III (12-18 months):

• Full-process AI optimization
• Adaptive control systems
• Intelligent maintenance systems
• Continuous learning mechanisms

4.2 Expected Economic Benefits

Case Study of 50-ton Annual High-Purity Tellurium Production:

5. Technical Challenges and Solutions

5.1 Key Technical Bottlenecks

1. Data Quality Issues:
   • Industrial data contains significant noise and missing values
   • Inconsistent standards across data sources
   • Long acquisition cycles for high-purity analysis data
2. Model Generalization:
   • Raw material variations cause model failures
   • Equipment aging affects process stability
   • New product specifications require model retraining
3. System Integration Difficulties:
   • Compatibility issues between old and new equipment
   • Real-time control response delays
   • Safety and reliability verification challenges

5.2 Innovative Solutions

Adaptive Data Enhancement:

• GAN-based process data generation
• Transfer learning to compensate for data scarcity
• Semi-supervised learning utilizing unlabeled data

Hybrid Modeling Approach:

• Physics-constrained data models
• Mechanism-guided neural network architectures
• Multi-fidelity model fusion

Edge-Cloud Collaborative Computing:

• Edge deployment of critical control algorithms
• Cloud computing for complex optimization tasks
• Low-latency 5G communication

6. Future Development Directions

1. Intelligent Material Development:
   • AI-designed specialized purification materials
   • High-throughput screening of optimal additive combinations
   • Prediction of novel impurity capture mechanisms
2. Fully Autonomous Optimization:
   • Self-aware process states
   • Self-optimizing operational parameters
   • Self-correcting anomaly resolution
3. Green Purification Processes:
   • Minimum energy path optimization
   • Waste recycling solutions
   • Real-time carbon footprint monitoring

Through deep AI integration, tellurium purification is undergoing a revolutionary transformation from experience-driven to data-driven, from segmented optimization to holistic optimization. Companies are advised to adopt a “master planning, phased implementation” strategy, prioritizing breakthroughs in critical process steps and gradually building comprehensive intelligent purification systems.