The non-ferrous metals industry has long been a cornerstone of 

global industrial development, and zinc remains one of its most 

critical commodities. Zinc’s applications span galvanizing steel, 

die-casting alloys, and battery production, making efficient 

processing vital for sectors like construction, automotive, and 

renewable energy. However, traditional zinc processing methods 

face challenges such as energy inefficiency, environmental concerns, 

and operational variability. The integration of automation technologies 

is transforming this landscape, enhancing productivity, sustainability, 

and safety. This article explores how automation is reshaping zinc 

processing, focusing on key applications, benefits, and future trends.

1. The Zinc Processing Workflow: 

Challenges and Opportunities

Zinc production involves multiple stages, including ore concentration, 

roasting, leaching, purification, and electrolysis. Each step presents 

unique challenges:

• Ore Sorting and Concentration: Manual sorting of zinc ores

• (e.g., sphalerite) is labor-intensive and prone to errors, leading

• to suboptimal recovery rates.

• Roasting: Temperature control in roasting furnaces directly

• impacts zinc oxide quality. Inconsistent heat distribution can

• result in energy waste or incomplete conversion.

• Leaching and Purification: Chemical processes require precise

• pH and temperature adjustments. Human error in monitoring

• can cause impurities, reducing final product purity.

• Electrolysis: Maintaining stable current density and electrolyte

• composition is critical; fluctuations reduce cathode zinc quality.

These pain points highlight the need for automation to standardize 

processes, reduce waste, and improve yield.

2. Automation Technologies in

 Zinc Processing

2.1 Sensor-Based Ore Sorting

Modern zinc processing plants employ sensor-based ore sorting systems 

using X-ray fluorescence (XRF), near-infrared (NIR) spectroscopy, and 

machine vision. These technologies analyze ore composition in real time, 

enabling automated separation of high-grade zinc ores from waste rock. 

For example, Tomra’s XRT sensors have been deployed in mines to 

increase ore recovery rates by 15–20%, reducing reliance on manual labor.

2.2 Advanced Process Control (APC) Systems

APC systems optimize critical parameters in roasting and leaching. For instance:

• Roasting Automation: Machine learning algorithms adjust furnace

• temperatures based on real-time gas composition data from

• laser-based analyzers. This ensures optimal zinc oxide conversion

• while minimizing SO₂ emissions.

• Leaching Optimization: pH and temperature sensors feed data to

• programmable logic controllers (PLCs), which autonomously adjust

• reagent dosing. At the Skorpion Zinc refinery in Namibia, APC

• reduced acid consumption by 12% and improved zinc recovery by 8%.

2.3 Robotics in Electrolysis

Electrolytic zinc refining requires precise handling of cathodes and anodes. 

Collaborative robots (cobots) now perform tasks such as stripping zinc 

deposits from cathodes, reducing human exposure to acidic environments. 

ABB’s robotic solutions, integrated with vision systems, have cut downtime 

by 30% in pilot installations.

2.4 Digital Twin Technology

Digital twins—virtual replicas of physical processes—are gaining traction. 

By simulating roasting or leaching dynamics, engineers predict equipment 

wear, optimize energy use, and test process modifications. Boliden’s Kokkola 

zinc plant in Finland reported a 10% reduction in energy costs after 

implementing a digital twin for its roasting circuit.

2.5 IoT and Predictive Maintenance

Wireless IoT sensors monitor equipment health (e.g., pump vibrations, motor 

temperatures) in real time. Predictive analytics platforms like GE’s Predix alert 

operators to potential failures before they occur. At Hindustan Zinc’s smelters, 

this approach reduced unplanned downtime by 25% and extended equipment lifespan by 18%.

3. Benefits of Automation in Zinc 

Processing

3.1 Enhanced Efficiency and Yield

Automation minimizes human error and ensures consistent process 

parameters. For example, automated electrolyte circulation systems in 

electrolysis maintain uniform zinc deposition, boosting cathode purity 

from 99.5% to 99.95%. This improvement directly translates to higher 

market value.

3.2 Energy and Resource Savings

Smart energy management systems in roasting furnaces have cut fuel 

consumption by 20% at Nyrstar’s Auby plant. Similarly, automated water 

recycling in leaching circuits reduces freshwater usage by up to 40%, 

aligning with circular economy goals.

3.3 Improved Safety

Automation reduces worker exposure to hazardous environments. 

Remote-controlled drones inspect sulfuric acid storage tanks, while 

exoskeletons assist in manual tasks, lowering injury rates by 50% in trials.

3.4 Environmental Compliance

Continuous emission monitoring systems (CEMS) ensure compliance with 

regulations like the EU’s Industrial Emissions Directive. Automated 

scrubbers adjust SO₂ capture rates in real time, cutting emissions by 

30% at Glencore’s Canadian operations.

4. Challenges and Future Directions

4.1 Integration Costs

The upfront cost of automation remains a barrier, particularly for smaller 

producers. Modular, scalable solutions are emerging to address this. For 

instance, cloud-based APC systems now offer pay-per-use models.

4.2 Workforce Adaptation

Reskilling workers to manage automated systems is critical. Companies 

like Teck Resources have partnered with vocational institutes to develop 

AI-driven training platforms for operators.

4.3 Cybersecurity Risks

Connected systems are vulnerable to cyberattacks. Blockchain-based 

security protocols and air-gapped networks are being tested to 

protect sensitive data.

4.4 Next-Gen Innovations

Future advancements may include:

• AI-Driven Process Optimization: Deep learning models could

• predict zinc prices and adjust production schedules autonomously.

• Hydrogen-Based Roasting: Automated hydrogen furnaces may

• replace coal-fired systems, reducing carbon footprints.

• Autonomous Mining Fleets: Self-driving trucks and drills could

• integrate with processing plants for end-to-end automation.

5. Conclusion

Automation is no longer optional in zinc processing—it is a strategic 

imperative. By leveraging sensors, robotics, and AI, the industry is 

achieving unprecedented levels of efficiency, sustainability, and safety. 

While challenges like cost and workforce adaptation persist, collaborative 

efforts between tech providers and manufacturers are paving the way 

for a smarter, greener future. As zinc demand grows alongside renewable 

energy and electric vehicle markets, automation will remain the linchpin 

of competitive advantage in the non-ferrous metals sector.