How far are we from “smart mineral processing”?

With the continued growth of global demand for mineral resources and increasing environmental, safety, and cost pressures, traditional mining production models face unprecedented challenges. The wave of digital transformation is sweeping across all industries, including the mining sector. "Smart mineral processing," as a core component of intelligent mining, is becoming an industry consensus and development direction. It represents not only technological innovation but also profound changes in production methods, management models, and even the industry ecosystem. So, how close are we to achieving "smart mineral processing"?

01 Automation: The cornerstone of smart mineral processing01 Automation: The cornerstone of smart mineral processing

Automation is the foundation of smart mineral processing. Its core is to replace manual labor in repetitive, dangerous, or precision-critical operations through various control systems and equipment, thereby improving production efficiency, ensuring safety, and reducing labor intensity.

1. Current Application of Automation in Mineral Processing Plants

Currently, the vast majority of modern mineral processing plants have widely adopted automation technology, primarily in the following areas:

Crushing and Grinding Automation:

• Crusher Automation: Load sensors and level meters monitor the material status within the crushing chamber, automatically adjusting the feed rate and discharge opening to achieve the optimal goal of "more crushing, less grinding."
• Grinding Mill Automation: Utilizing sonar systems, power sensors, bearing temperature sensors, and other sensors, combined with online analytical instruments such as grinding concentration meters and slurry pH meters, closed-loop control of mill feed rate, water volume, and speed is achieved, ensuring stable grinding product particle size and maximizing grinding efficiency. For example, intelligent feed control systems based on mill acoustic signals are widely used.
• Automatic Sampling and Online Analysis: Automatic samplers are installed at key points in the grinding and flotation circuits. Combined with online X-ray fluorescence analyzers (such as the Courier series from Finland's Outotec) and ultrasonic concentration meters, key parameters such as slurry grade, concentration, and particle size are monitored in real time, providing a basis for subsequent control.

Flotation Automation:

• Automatic Flotation Cell Level Control: Level sensors and electric valves automatically adjust the flotation cell level to maintain a stable froth layer.
• Automatic Air Volume and Agitator Speed ​​Control: Based on slurry properties and flotation performance, the air volume and agitator speed are automatically adjusted to optimize mineralization.
• Automatic Reagent Dosing System: Based on slurry grade, pH, and other data from online analyzers, a peristaltic or metering pump automatically and precisely adds flotation reagents such as collectors, frothers, and regulators. This enables "on-demand dosing," avoids overdosing or underdosing, improves reagent utilization, and reduces costs. For example, some concentrators have implemented intelligent reagent control based on online grade analysis results.

Concentration and Filtration Automation:

• Thickener Automation: Utilizing an underflow concentration meter and interface detector, the underflow pump speed and flocculant dosage are automatically adjusted to ensure stable underflow concentration and clear overflow.
• Filter Automation: Parameters such as vacuum level and filter cake moisture content are automatically monitored and adjusted to ensure filtration efficiency and product quality.

Conveying and Stockpiling Automation:

• Belt Conveyor Remote Control and Interlocking Protection: Enables remote start, stop, and speed adjustment, and includes fault protection features for deviation, tearing, and blockage.
• Stacker and Reclaimer Automation: Enables unmanned, automated stacking and reclaiming operations in the stockpile yard.

2. Benefits of Automation

The widespread application of automation technology in mineral processing plants has significantly improved production efficiency, stability, safety, and economic benefits:

• Improved production efficiency: A continuous and stable production process reduces downtime and fluctuations caused by human intervention.
• Optimized product quality: Precise control of key parameters ensures stable concentrate grade and recovery rate.
• Reduced production costs: Reduced reagent and energy consumption, labor costs, and maintenance costs.
• Improved working environment: Replacing manual work in harsh environments improves safety.

Although automation has made significant progress, its essence is "rigid" control based on preset rules and fixed models. When production conditions (such as ore properties and equipment wear) change significantly, automated systems often struggle to adapt and still require manual intervention and adjustment. This is precisely the problem that intelligentization aims to solve.

02 Intelligence: The Leap Towards Smart Mineral Processing

Intelligence is an advanced stage of automation. Its core is to enable the mineral processing system to have the ability of autonomous learning, autonomous decision-making, autonomous optimization and self-adaptation by introducing advanced technologies such as big data, cloud computing, artificial intelligence (AI), Internet of Things (IoT), and digital twins, thereby achieving flexibility, optimization and coordination of the production process.

1. Core Technology System of Smart Mineral Processing

(1) Industrial Internet of Things (IIoT) and Data Collection:

• Deploy massive sensors, intelligent instruments and edge computing devices to collect physical quantities (temperature, pressure, flow, liquid level, current, voltage, vibration, etc.), chemical quantities (grade, pH value, redox potential, etc.) and equipment operating status data of the entire mineral processing production process in real time and with high precision.
• Use communication technologies such as industrial Ethernet and wireless sensor networks to build high-speed and reliable data transmission channels and aggregate massive data to the cloud or local data center.
• Practical Case: Using Machine Vision Technology to Monitor Foam Status in Real Time

(2) Big data platform and data mining:

• Build a unified mining big data platform to clean, integrate, store and manage data from different equipment, different systems and different time dimensions.
• Use big data analysis technology (such as association rule mining, cluster analysis, regression analysis, etc.) to discover potential laws, abnormal patterns and optimization opportunities in the production process from massive historical data, such as predicting equipment failures and analyzing process bottlenecks.

(3) Artificial intelligence (AI) and machine learning (ML):

Intelligent identification and prediction based on deep learning:

• Intelligent identification of ore properties: Use machine vision and spectral analysis technology to identify and classify the grade, mineral composition, and embedded characteristics of the selected raw ore in real time, providing accurate basis for grinding and flotation.
• Equipment fault prediction and health management (PHM): By analyzing the equipment's vibration, temperature, current and other big data, use deep learning models to predict the remaining life and potential failures of equipment (such as mills, flotation machines, pumps), implement preventive maintenance, and avoid sudden downtime.

Reinforcement Learning and Adaptive Control:

• Intelligent Grinding Circuit Optimization: Using a reinforcement learning algorithm, the grinding system autonomously finds the optimal combination of feed rate, water volume, and mill speed through trial and error, achieving optimal product particle size and minimizing energy consumption.
• Intelligent Flotation Reagent Control: A reinforcement learning-based intelligent flotation reagent decision-making system is built. Based on real-time slurry properties, online grade analysis results, and flotation indicators, the system dynamically adjusts reagent type, dosage, and addition point, achieving adaptive optimization of the flotation process.

Expert System and Knowledge Graph: The ore dressing engineers' experience and knowledge are digitized and structured to create a mineral processing knowledge graph. This assists AI models in decision-making and provides intelligent guidance for novices.

2. Practical Path for Intelligent Mineral Processing

1. Top-level Design and Planning: Develop a smart mineral processing development blueprint aligned with the company's strategy, clearly defining intelligent goals, technical routes, and implementation phases.
2. Data Infrastructure Development: Improve automation systems, deploy the Industrial Internet of Things (IIoT), ensure high-quality, comprehensive data collection and transmission, and build a unified data management platform.
3. Core Algorithm and Model Development: Develop or introduce AI and big data algorithms and models based on the specific characteristics of mineral processing processes to address key issues such as grinding particle size control, flotation reagent optimization, and equipment failure prediction.
4. Digital Twin Platform Development: Gradually establish a digital twin model of the mineral processing plant to enable visual monitoring, simulation optimization, and predictive warnings.
5. Talent Development and Organizational Transformation: Cultivate interdisciplinary talent with big data analysis and AI application capabilities, and promote the shift to a flatter, more intelligent, and collaborative management model.
6. Pilot First and Gradual Expansion: Select key production lines for pilot projects to verify technical feasibility and economic benefits, and then gradually expand to the entire mineral processing plant and even the mining group.

03 Challenges and Outlook

1. Challenges

Although smart mineral processing holds great promise, its development is not without its challenges. It faces numerous challenges:

1. Data Quality and Standardization: The mineral processing process is complex, resulting in a wide variety of data types. Data formats vary across different equipment and systems, and data loss and noise are common, making data cleaning and integration difficult.
2. Shortage of Multidisciplinary Talent: A shortage of multidisciplinary talent who are both proficient in mineral processing technology and AI, big data, and industrial Internet technologies is a bottleneck hindering the development of smart mineral processing.
3. High Initial Investment: Deploying advanced sensors, communication networks, computing platforms, and software systems requires substantial capital investment, placing a heavy burden on some mining companies.
4. Data Security and Privacy: Industrial big data involves core corporate production secrets, making data security and privacy protection paramount.
5. Compatibility with Existing Systems: The control systems and equipment of older mineral processing plants often lack intelligent interfaces, making retrofitting difficult and leading to significant compatibility issues.

2. Outlook: The Future of Smart Mineral Processing

Looking ahead, "smart mineral processing" will develop in the following directions, becoming increasingly accessible:

1. Full-process collaborative optimization and self-healing: This will enable intelligent perception, real-time decision-making, collaborative control, and adaptive optimization throughout the entire process from ore to concentrate, even with the ability to self-heal in the event of emergencies.
2. Cross-regional and multi-mine collaborative production: Cloud computing and digital twins will enable optimized resource allocation and production coordination among different mineral processing plants, and even within mining groups.
3. Virtual reality/augmented reality (VR/AR) applications: Combined with digital twins, these applications will provide mineral processing plants with immersive remote operation, maintenance guidance, and personnel training.
4. Green, low-carbon, and circular economy: Smart mineral processing will more precisely control energy, water, and chemical consumption, realize waste resource utilization, and promote the green and sustainable development of the mineral processing industry.

04 Conclusion: The Road Ahead is Long, But the Way Will Come

Achieving "smart mineral processing" is a long and complex process, one that cannot be achieved overnight. It is not a simple accumulation of technologies, but rather a systematic engineering transformation. From automation to intelligence, we have taken a solid first step and are now moving towards deeper levels of intelligence.

We are currently at a critical juncture in the transition from "automation" to "intelligence." While fully "unmanned" or "fully intelligent" mineral processing plants will still take time, intelligent applications in some processes have gradually been implemented and demonstrate significant potential. Mining companies should actively embrace change, increase investment in technological R&D, cultivate multifaceted talent, deepen industry-university-research collaboration, and progressively advance the development of smart mineral processing.

"Smart mineral processing" not only significantly improves production efficiency, reduces costs, and ensures safety, but is also the only way to promote high-quality development and achieve green and sustainable development in the mining industry. With unwavering conviction, continued investment, and in-depth practice, we believe that the grand blueprint of "smart mineral processing" will eventually become a reality, ushering in a new chapter in the development of the mining industry.