How advanced digital technologies are creating a safer, more efficient future for coal processing
Nestled in the heart of China's coal country, the Jintaoyuan preparation plant in Shanxi Province represents a quiet revolution in an industry often perceived as traditional and slow to change.
Coal preparation—the critical process of transforming raw mined coal into a clean, consistent product—has long been both an art and a science. For decades, plants relied heavily on manual operations, experienced workers, and reactive maintenance, leading to inefficiencies, quality fluctuations, and safety challenges 1 . Today, a remarkable transformation is underway, powered by an integrated intelligent control system that synergizes human expertise with artificial intelligence, real-time data analytics, and automated precision.
At its core, the intelligent control system at Jintaoyuan functions much like a digital nervous system, continuously gathering information from throughout the plant and enabling coordinated, intelligent responses . This system is structured in four distinct but integrated layers:
The system's sensory apparatus, comprising an extensive network of sensors, monitors, and instruments.
Functions as the backbone, providing computational power and connectivity.
Serves as the central nervous system where data is analyzed and transformed into insights.
Acts as the command center with intuitive dashboards for both automated control and human oversight .
Maintains optimal density for separation with fluctuations of less than 0.005 g/cm³ .
Reduces daily equipment failures from 55 minutes to under 10 minutes .
Integrates environmental monitoring with AI recognition for comprehensive hazard prevention .
Dust in coal plants poses serious health risks to workers and can create explosion hazards 6 . Traditional approaches often prove inadequate, reacting to dust after it has already spread.
To address this, engineers designed a positive-negative pressure composite dust removal system that contains dust at its source 6 .
"The optimized system maintained effectiveness even when coal quality deteriorated, demonstrating the robustness of the design." 6
The research team employed Computational Fluid Dynamics (CFD) to model the complex movement of air and dust particles 6 . The experiment followed a meticulous four-stage process:
Created detailed virtual model of screening workshop
Established conditions without dust control system
Virtually implemented pressure composite system
Used orthogonal experimental design
| Condition | Average Dust Concentration (mg/m³) | Reduction Efficiency |
|---|---|---|
| Original System | 28.5 | Baseline |
| Optimized System (Normal Conditions) | 9.8 | 65.6% |
| Optimized System (Deteriorated Coal) | 15.2 | 46.7% |
Collect real-time data on equipment performance, product quality, and environmental conditions 4 .
Simulate fluid flow, particle movement, and chemical processes in virtual environments 6 .
Create virtual replicas of physical systems for testing and optimization 4 .
Through automation and optimization
Through precise monitoring and adjustment
Through environmental monitoring and hazard prevention
Operators per shift reduced from 15 to 6, with productivity increased to 139 tons per worker .
Ash content of clean coal now fluctuates within ±0.5 percentage points .
Continuous coal washing time increased from 65% to over 90% .
The precise control of the separation process has reduced consumption of medium and reagents, saving approximately 125,000 yuan monthly in electricity, medium, and reagent costs .
The intelligent control system at Jintaoyuan represents more than just a technical upgrade—it signals a fundamental transformation in how coal preparation plants can and will operate in the future.
As the global energy landscape evolves, such technological innovations demonstrate that even traditional industries like coal processing can achieve remarkable gains in efficiency, safety, and environmental performance through strategic digitalization.
"The success at Jintaoyuan demonstrates that technological progress need not come at the expense of jobs, but rather transforms them—shifting workers from hazardous, routine tasks to higher-value technical roles."
The lessons from Jintaoyuan extend far beyond Shanxi Province, offering a blueprint for coal preparation plants worldwide. As these intelligent systems continue to evolve, we can expect to see further improvements in automation precision and predictive capabilities.