Neural network model construction and analysis for simulating and predicting hazardous element allocation in copper...

Area of Science:

• Metallurgical Engineering
• Environmental Science
• Chemical Engineering

Background:

• Hazardous element distribution in copper smelting is complex, influenced by non-linear, time-lagged operating variables.
• The precise mechanisms controlling these elements remain poorly understood.

Purpose of the Study:

• To simulate hazardous element distribution using production data.
• To clarify multifactorial influences on hazardous element distribution.
• To identify regulatory patterns in actual production.

Main Methods:

• Simulation based on actual copper smelting production data.
• Analysis of operating variables including raw material grades and production conditions.

Main Results:

• Smelting and blowing units significantly impact element distribution.
• Blast volume, oxygen supply, and raw material composition are primary influencing factors.
• Arsenic and lead distribution proportions can fluctuate by up to 50% and 20%, respectively.
• Increasing blast volume recovers 30% more copper from slag to copper-sulfur phase.

Conclusions:

• Identified key operational factors influencing hazardous element distribution.
• Provided insights for optimizing copper smelting processes.
• Demonstrated potential for improved integrated management of hazardous elements and enhanced copper recovery.