With the zinc smelting industry facing multiple pressures
such as dual-control of energy consumption, tightening
of environmental protection, and fluctuations in raw
materials, the traditional “trial-and-error” production
model is no longer sustainable. Digital twin technology has
become the core engine for zinc smelting enterprises to break
through process bottlenecks and reshape their competitiveness
by constructing a 1:1 virtual mirror of the physical production
line and realizing real-time monitoring - dynamic prediction -
closed-loop control of autonomous optimization. This article
will deeply analyze the digital twin technology in the zinc
smelting process landing scenarios and value cracking path.
First, the zinc smelting “neck” problem: the
digital twin of the breakthrough pivot point
Zinc smelting process is complex, covering roasting, leaching,
purification, electrolysis and other links, there are four core pain points:
Black box process: chemical reactions in the roaster are not visible,
and zinc recovery fluctuates by ±5%;
High energy consumption: the current efficiency of traditional
electrolyzer is only 88%-92%, and the DC power consumption
of tons of zinc is more than 3000kWh;
Serious equipment loss: leaching tank corrosion, electrode plate
deformation and other frequent failures, annual maintenance
costs of more than 10 million yuan;
High environmental risks: flue gas SO₂ concentration control
lags behind, and fines for exceeding emission standards
account for 3%-5% of operating costs.
The disruptive value of digital twin:
Full element mirroring: real-time mapping of 2000+ sensor data,
building a “transparent body” of the production line;
Decision-making in seconds: simulation speed is 100 times faster
than the physical production line, predicting process deviation
15 minutes in advance;
Zero-cost trial and error: virtual environment simulation of
hundreds of parameter combinations to find the optimal solution.
Technological breakthrough: digital twin
reconstruction of the three core
capabilities of zinc smelting
1. Multi-scale modeling: from “empirical formula” to “atomic level simulation”.
Molecular dynamics simulation:
Establishing a multiphase reaction model of FeS₂-ZnS-O₂ in the zinc
concentrate roasting process, accurately predicting the ZnO
generation rate, and guiding the adjustment of the blower
volume to stabilize the zinc content of the roasted sand at 58%-60%.
Computational fluid dynamics (CFD) optimization:
Simulate the flow field of H₂SO₄ solution in the leaching tank,
adjust the angle and rotational speed of the stirring paddle,
and increase the zinc leaching rate from 94% to 97.5%.
Case: A zinc plant in Hunan reduced gas consumption in the
roasting section by 22% through digital twin modeling, saving
8.6 million RMB annually.
2. Real-time data-driven: dynamically calibrated “live model”.
5G+Industrial Internet base:
Deploy 200 edge computing devices to transmit data such as
electrolyzer voltage, temperature, pole distance, etc. with 10ms
latency, and the twin updates its status every 5 seconds.
Adaptive learning mechanism:
Based on LSTM neural network, the model recognizes current
density deviation caused by aging of electrode plates,
automatically compensates for deviation ±0.5A/m², and
current efficiency is increased to 95%.
Data verification: After the application in the electrolysis
process of a smelter in Yunnan, the power consumption
of zinc per ton was reduced to 2,800kWh, reaching the
industry leading level.
3. Cross-system synergy: from “single-point optimization”
to “global optimal”.
Energy-process joint control:
Integration of waste heat boiler, acid production system and
electrolysis workshop data, dynamic distribution of steam
and electricity, comprehensive energy consumption
decreased by 18%.
Material flow simulation:
Simulate the whole process of zinc ingot from casting to
warehousing, optimize the AGV path and overhead crane
scheduling, and improve logistics efficiency by 40%.
Scenario landing: four process innovations
driven by digital twins
1. Intelligent roasting: penetrate the “black box” chemical reaction
Virtual temperature measurement system:
Build a temperature field twin in the roaster, combined with infrared
camera data, real-time display of the temperature distribution in
different areas, automatically adjust the oxygen concentration,
and stabilize the conversion rate of ZnO at more than 96%.
Fault pre-diagnosis:
Monitoring the stress change of the furnace wall, warning the
refractory material breakage 48 hours in advance, and
shortening the overhaul time from 72 hours to 8 hours.
2. Precise leaching: “Digital microscope” for liquid-solid reaction.
PH value dynamic control:
The twin model calculates the acidity of the leaching solution in
real time, and links the acid pump to adjust the flow rate,
reducing the fluctuation of PH value at the end point from ±0.3 to ±0.05.
Impurity removal optimization:
Simulate the precipitation path of Cu, Cd and other impurity
ions, optimize the addition of zinc powder and reaction time,
and the purity of the solution reaches 99.99%.
3. High-efficiency electrolysis: the “virtual doctor” of the
electrode plate.
Plate deformation monitoring:
Construct a digital twin of the electrode plate through 3D
scanning, detect changes in curvature at the 0.1mm level,
predict life expectancy and automatically schedule replacement.
Electric field uniformity regulation:
Simulate the electric field distribution in the electrolyzer,
adjust the pole pitch and electrolyte circulation speed, reduce
the concentration polarization, and reduce DC power
consumption by 12%.
4. Green Acid Production: “Prophet System” for flue gas control.
SO₂ concentration prediction:
Based on the roaster operating conditions data, predict flue
gas SO₂ fluctuations 30 minutes in advance, dynamically
adjust the amount of hydrogen peroxide spray, and stabilize
the emission concentration within 50mg/m³.
Waste slag resourceization:
Simulate the enrichment process of In, Ge and other rare
metals in tailings, optimize magnetic separation parameters,
and increase the recovery rate of rare and precious metals by 25%.
Fourth, the implementation path: zinc
enterprises to build a digital twin of
the three-step strategy
Data foundation building:
Deploy intelligent sensors such as vibration, temperature,
composition, etc. to realize 100% data collection of
key equipment;
Model development:
Jointly develop a library of simulation algorithms specialized
in zinc smelting with research institutes to precipitate
process Know-How;
System integration:
Open the interface between DCS, MES and the twin
platform to establish a closed loop of “monitoring-optimization-control”.
Fifth, the future picture: digital twin and
AI big model of the depth of integration
Autonomous process evolution:
Access to industrial big model, automatically generate new additive
formula and process route;
Meta-universe collaboration:
Through AR glasses superimposed on the virtual production line,
engineers can “see through” the internal equipment for remote diagnosis;
Zero-carbon twin:
Integrate photovoltaic and energy storage data to simulate the
whole plant energy network, realizing 100% green power matching.
Conclusion
Digital twin technology is taking the zinc smelting industry from
“empirical zinc refining” to a new era of “computational zinc refining”.
By building intelligent production lines that integrate reality and reality,
enterprises can not only realize the ultimate reduction of energy
consumption and material consumption, but also open up the blue
ocean of value such as the recovery of rare precious metals and the
development of high-end zinc alloys. With the support of quantum
computing, AI modeling and other technologies, the digital twin of
zinc smelting will evolve its autonomous decision-making ability, and
promote the industry to move towards the ultimate goal of “zero
defects, zero waste, and zero carbon emissions”.