According to the
iron ore alkali
dissolution-
electrolysis intelligent collaborative
optimization system and the dynamic purification method provided by the invention,
artificial intelligence and a metallurgical process are fused, and full-process dynamic regulation and control are realized. The
system comprises: 1, an intelligent collaborative optimization module, which dynamically and collaboratively optimizes
dissolution-
electrolysis process parameters (NaOH concentration, liquid-
solid ratio, Fe < 3 + > concentration and
current density) based on
reinforcement learning (DQN
algorithm), predicts an
impurity dissolution trend and adds a specific inhibitor in a gradient manner in combination with
machine learning, and inhibits generation of Al2O3 / SiO2; the equipment health management module realizes
corrosion / blockage two-factor accurate early warning (the accuracy is greater than or equal to 92%) through
deep learning analysis of a
corrosion image and a vibration spectrum, and is linked with a maintenance decision; the energy-resource
coupling module integrates
waste heat recovery (greater than or equal to 85%) and a multi-effect
evaporation regeneration technology, so that the
energy consumption per
ton of iron is reduced to 2.9 GJ, and the alkali consumption is less than or equal to 0.95 kg; and 4, a self-adaptive process platform: dynamically updating the model based on a
federated learning framework, and adapting to a complex ore source with Fe greater than or equal to 35% and Al2O3 less than or equal to 8%. The embodiment verifies that the
system improves the
hematite dissolution rate to 94%, reduces the non-planned shutdown of equipment by 40%, reduces the comprehensive cost by 14%, and is suitable for efficient and clean utilization of low-grade iron ores.