International Scientific Journal


At present, the underground brine deposit of Lop Nor salt lake in Xinjiang, while is rich in solar energy resource and can be one high efficient solar thermal utilization area, has become an important potash production base in China. During the development of salt lake brine, the collection methods are different according to the concentration of ions in different locations. In order to solve the problems of low accuracy and high calculation cost in prediction of salt field ion concentration, a data mining method based on random forest is applied in this paper. To build the model, we collected K+, SO42–, Cl–, and other two kinds of ions, among which the features included the collection time, collection locality and the number of salt pond. We used several methods to train and test the sample data, evaluated the experimental results using a variety of performance metrics and compared it with other methods at the same time. The results revealed that the optimal random forest model yielded the mean square error and coefficient of determination values of 0.073 and 0.940, which performed relatively better than support vector machine and extremely randomized trees.
PAPER REVISED: 2019-01-25
PAPER ACCEPTED: 2019-02-05
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THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Issue 5, PAGES [2623 - 2630]
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