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Scientists Develop Automated Electrolyte Composition Analysis for Aluminum Production

Researchers from Siberian Federal University (SibFU) together with scientists from Siberian State Aerospace University, both in Russia, presented a new method for automatic composition analysis of samples from the electrolytic bath in potcells. This technology will provide for more accurate technological control and increase the efficiency of aluminum production.

To improve the technological properties of the electrolyte, aluminum fluoride, calcium fluoride, and sometimes magnesium and potassium fluoride may be added. Preservation of optimal bath composition is a key element of electrolysis technology — a challenge since the composition constantly changes and the ratio of components shifts during the process. This requires operators to constantly take and analyze electrolyte samples in as little time as possible, while maintaining accuracy.

Historically, there are two methods for performing such analyses. One express control method used in the aluminum industry is called x-ray diffraction quantitative phase analysis, which is based on studying x-ray images formed by the rays reflected from samples. In its traditional variant, such analysis has a considerable disadvantage, since it requires regular calibration using control materials with accurately determined phase compositions. It also fails to take the actual crystal structure of the phases into account.

An alternative method is called the Rietveld method, which provides quantitative analysis by specifying the atomic and crystal structure of component phases without using control samples. However, this method is interactive and difficult to automate, as one has to manually establish up to 100 initial system parameters and to manage the order of their programmed adjustment.

The researchers from SibFU have modernized the Rietveld method to make it applicable for automated analysis. This was achieved through the development of a self-configuring evolutionary genetic algorithm — a program that uses the principle of biological natural selection to find optimal parameter values when modeling an x-ray image. Using the Rietveld method, the algorithm is able to self learn and optimize the vast range of x-ray image and phase crystal structure parameters. Since the algorithm is self learning, it does not require tuning and therefore offers fully automatic analysis.

“Generally, our results meet the technological criteria for the accuracy of electrolyte analysis that are used at aluminum production facilities,” said Igor Yakimov, the head of the project and professor of the Institute of Non-Ferrous Metals and Material Studies of SibFU. “We can recommend our genetic algorithm to express control of electrolyte composition. The analysis shows a minor system error caused by over-estimation of cryolite concentration due to its uneven crystallization in the course of sampling. Before this method is implemented by the industry, we have to eliminate this error and also to improve the efficiency of the method.”

The results of the research, “Application of Evolutionary Rietveld Method Based XRD Phase Analysis and a Self-Configuring Genetic Algorithm to the Inspection of Electrolyte Composition in Aluminum Electrolysis Baths,” was published in Crystals journal.

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