THE CSIRO says that its researchers have developed a process that may improve the understanding of different variables in a Bayer precipitation circuit.
The group says that managing the operation of gibbsite precipitation circuits for optimal performance is one of the most challenging aspects of running a Bayer alumina refinery.
CSIRO researchers (working through the Parker CRC for Integrated Hydrometallurgy Solutions) have developed a laboratory semi-continuous gibbsite precipitator (SCGP), which can significantly improve the understanding of the impact of different process variables in a Bayer precipitation circuit.
The group says that the SCGP allows steady operation to be achieved much more rapidly than in existing types of laboratory precipitators.
The laboratory-scale precipitator provides a sophisticated diagnostic tool for generating experimental information that closely matches the situation in the plant. Such information is essential to improve gibbsite precipitation process design, control and optimisation.
Project Leader Dr Iztok Livk says the experimental set-up provides an important way of studying gibbsite precipitation mechanisms and effects of different process parameters on precipitation yield and product quality.
'Building this experimental unit required development of customised control software to ensure continuous monitoring of main process variables and real-time process control,' Dr Livk says.
Researchers can use the precipitator to obtain experimental data under tightly controlled conditions. Using advanced numerical analysis tools, they can extract quantitative mechanistic information from experimental data and develop robust models suitable for plant situations.
The SCGP allows steady operation to be achieved much more rapidly than in existing types of laboratory precipitators. In order to achieve this performance, four main operational variables: supersaturation, solids concentration, total volume and temperature were controlled in a continuous manner.
Gibbsite precipitation kinetics, such as crystal growth, nucleation and agglomeration, estimated from preliminary SCGP data, were shown to be consistent with the precipitation kinetics estimated from batch precipitation data.
Dr Livk says the development of the SCGP has potential to provide new insight into the role of a number of variables in the precipitation process which could, in turn, lead to significant improvements in precipitation process efficiency.
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