Prediction of Rare Earth Elements in Neutral Alkaline Mine Drainage from Razi Coal Mine, Golestan Province, Northeast Iran, Using General Regression Neural Network

AbstractThe coal mining operation in the Razi mine of the Golestan Province, northeast Iran, has slightly disturbed the balance of the existing geowater system, which may have a long-term environmental impact if no attempt is made at considering such environmental problems. Prediction of rare earth elements (REEs) in mine drainages is a major task in developing an appropriate remediation strategy because high concentrations of REEs may be harmful to human beings and animals. Expert systems are broadly used in many applications. In this paper, a general regression neural network (GRNN) method has been chosen as an easy-to-use tool in order to predict REEs in neutral alkaline mine drainage (NAMD) of the Razi coal mine that contains a low concentration of REEs (varying between 2.0 and 3.09  μg/L) and high levels of sulphate (2,100  mg/L), bicarbonate (1,575  mg/L), and pH (about 9). The pH and SO42− concentration of NAMD were the main constituents used for REE prediction. To evaluate the accuracy of the GRNN...

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