How to Use Real-Time Data to Guide Raw Material Blending in Alumina Production
Release time:
2026-01-07
Challenge:
In alumina (Bayer process) production, the goal of “raw material blending” is not to achieve the highest grade but rather to consistently operate the digestion, settling, and calcination systems within their optimal performance windows at the lowest possible overall cost.
It’s not that the higher the Al₂O₃ content, the better—it’s rather:
A/S stability + stable soluble alumina + minimal process risk + lowest comprehensive cost per ton of alumina
Therefore, the core process indicator A/S (Al₂O₃ / Reactive SiO₂) for guiding ore blending is the top priority.
Regarding the impact on the process: low A/S ratio leads to a sharp increase in caustic soda consumption and a large volume of red mud.
A/S fluctuations, unstable dissolution system, drifting indicators, excessively high A/S levels—high ore prices don't necessarily translate into economic viability.
Therefore, the first principle of ore blending is: maintain a stable A/S ratio, rather than striving for the highest possible A/S ratio.
Next is available alumina (Available Al₂O₃), which determines how much alumina can be produced per unit of ore.
Reactive SiO₂ is silicon that genuinely “consumes alkali”; it determines the alkali consumption, red mud generation, and washing load.
What needs to be reduced in ore blending is “reactive silicon,” not total SiO₂.
The components iron, titanium, and LOI (which affects “hidden costs”) actually influence the amount of Fe₂O₃ red mud and the settling performance, as well as the risk of TiO₂ leaching and scaling.
Solution:
In production, alumina plants need to measure the ore grade online to determine the aforementioned indicators. During the slurry preparation process, we must adjust the caustic soda concentration and the liquid-solid ratio. Therefore, online measurement of the solid content in the feed material becomes critically important. Prolisens’ non-nuclear solid-content technology can provide valuable guidance. The resulting data can then be fed into the online RP measurement system at the end of the digestion stage, enabling real-time feedback and adjustments to the ore blending process. By using online analytical instruments for trend analysis and early warning—such as detecting a downward trend in the A/S ratio—we can proactively switch to ores with higher A/S ratios, thereby preventing sudden spikes in alkali consumption.
Slow transition during mine-source switching to prevent process shocks.
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