Data mining assisted prediction of liquidus temperature for primary crystallization of different electrolyte systems

• Published in: Chemometrics and intelligent laboratory systems Vol. 196; p. 103885
• Main Authors: Lu, Hui, Hu, Xiaojun, Cao, Bin, Ma, Liang, Chai, Wanqiu, Yang, Yunchuan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2020
• Subjects: Aluminium electrolysis industry; Back-propagation artificial neural network; Electrolyte; Gradient boosting regression; Liquidus temperature; Random forest regression; Support vector machine; Aluminium electrolysis industry; Electrolyte; Back-propagation artificial neural network; Liquidus temperature; Random forest regression; Support vector machine; Gradient boosting regression
• ISSN: 0169-7439
• DOI: 10.1016/j.chemolab.2019.103885

Liquidus temperature for primary crystallization is an important physical and chemical property for electrolyte system. It plays a crucial role on the stability of the electric cell in electrolysis production process. So how to accurately predict the liquidus temperature for primary crystallization of electrolyte based on the composition of electrolyte is a meaningful research subject. In this work, data mining assisted prediction of liquidus temperature for primary crystallization of electrolyte systems was proposed. The essential differences between the complex industrial electrolyte system and electrolyte system prepared in laboratory were revealed by means of comparing the micro-morphology, phase composition and thermal analysis. To some extent, it was verified that the empirical formula has no versatility in the two different electrolyte systems. The prediction model of liquidus temperature for primary crystallization of different electrolyte systems was constructed by using SVM(support vector machine), BPANN(back-propagation artifical neural networks), RFR(random forest regression) and GBR(gradient boosting regression) algorithm, respectively. The electroyte system inculdes Na3AlF6(CR)-Al2O3–AlF3–CaF2, Na3AlF6(CR)-Al2O3–MgF2–CaF2–LiF, Na3AlF6(CR)-Al2O3-MgF2-CaF2-KF-LiF, and Na3AlF6(CR)-Al2O3-AlF3-CaF2-MgF2-LiF-KF-NaF. For different electrolyte systems, ANN, SVM, RFR and other models all have good performances, they can effectively predict the liquidus temperature for primary crystallization of each electrolyte systems. For some electrolyte systems, ANN, SVM, RFR models are obviously superior to the prediction level of empirical formula described in the literature. It can be seen that data mining has a good application prospect in the prediction of the liquidus temperature for primary crystallization of electrolyte systems. We provide a new method for predicting the liquidus temperature for primary crystallization of different electrolyte systems based on the electrolyte composition dataset in this work. •The ANN and SVM approaches as soft-measurement models perform well in predicting the liquidus temperature based on their chemical electrolyte compositions.•Sensitivity analysis of different electrolyte components to the liquidus temperature reveals that the lower liquidus temperature can be realized by adjusting their chemical electrolyte compositions.•This prediction method has an significant application value in the production of electrolytic aluminium industry.