Automation in Mineral Processing Plants: Connotation and Implementation

1. Core Connotation of Automation in Mineral Processing Plants

Automation in mineral processing plants refers to the technical system that realizes digital monitoring and intelligent control of the entire process, including ore crushing, grinding, flotation, and concentration, through computer technology, industrial control systems, and intelligent equipment. Its essence is to use comprehensive automation technologies to achieve real-time data collection, process parameter optimization, equipment collaborative control, and safety early warning, thereby improving processing efficiency, ensuring product quality, reducing energy consumption costs, and minimizing manual intervention. For example, lead-zinc ore processing plants can adjust parameters such as grinding concentration and flotation reagent dosage in real time through automation systems, increasing concentrate grade by 3%-5% and reducing tailings metal loss by over 20%.

2. Implementation Paths of Automation in Mineral Processing Plants

(1) Architecture and Application of DCS (Data Control System)

As the core framework of automation, the DCS system adopts a "three-layer architecture" for hierarchical control:

• Application  layer: Processes production data through industrial software to generate control instructions;

• Management  layer: Coordinates equipment management, energy  scheduling, and process optimization;

• Network  layer: Achieves data transmission and equipment  linkage via optical fiber or wireless communication technologies.
       During system selection, attention should be paid to the real-time  performance of controllers (e.g., high-precision CPUs), compatibility of      sensors (temperature, pressure sensors), and reliability of the network   (fiber redundancy design) to ensure stable operation under complex      conditions.

(2) Intelligent Empowerment of Mineral Processing Expert System

The mineral processing expert system realizes process optimization through multi-module collaboration:

• Data  collection module: Acquires real-time parameters such  as ore density and particle size;

• Model  establishment module: Predicts separation effects based on machine learning algorithms;

• Decision  support module: Automatically generates optimal  parameters for reagent dosage and flotation time;

• Safety  protection module: Warns of risks such as equipment  failures and reagent leaks.
       Take the flotation process as an example: the system can automatically  adjust the air inflow and stirring speed according to mineral surface      properties, increasing lead-zinc metal recovery by 5%-8%.

(3) Construction of On-line Detection System for Key Parameters

Intelligent instruments are deployed to achieve full-process dynamic monitoring:

• Concentration and particle size detection: Uses ultrasonic densimeters and laser particle size analyzers to monitor grinding slurry status in real      time;

• Process  control instruments: Regulates reagent addition and pulp pH using flowmeters and pH meters;

• Safety monitoring equipment: Prevents equipment overload through temperature sensors and pressure transmitters.
       The on-line detection data (interacts) with the      DCS system to form a "monitoring-analysis-control" closed loop,      reducing production fluctuations by over 15%.

3. Core Value of Automation Practice

Automation in mineral processing plants achieves dual improvements in production efficiency and economic benefits through "equipment interconnection, data interoperability, and intelligent decision-making": it not only reduces manual intervention by over 30% and labor intensity but also reduces power consumption per ton of ore by 10%-15% through energy optimization, while minimizing reagent waste and tailings discharge, aligning with the needs of green mine construction. In the future, with the deep integration of artificial intelligence and 5G technologies, mineral processing automation will continue to evolve toward "unmanned and intelligent" operations.