Estimation of grade and recovery in the concentration of barite tailings by the flotation using the MLR and ANN analyses

Deniz V., UMUCU Y., Deniz O. T.

Physicochemical Problems of Mineral Processing, vol.58, no.5, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 58 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.37190/ppmp/150646
  • Journal Name: Physicochemical Problems of Mineral Processing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Metadex, Civil Engineering Abstracts
  • Keywords: barite, tailing, flotation, recovery, grade, MLR model, ANN model, CALCITE, SEPARATION
  • Eskisehir Osmangazi University Affiliated: Yes


© Wroclaw University of Science and TechnologyThis study aimed to find optimal models in a comparative framework to estimate the recovery and grade of barite concentrate obtained from the rougher flotation of the barite tailings. Therefore, firstly, the effect of four operating parameters (flotation time, pH, collector dosage, and depressant dosage) on the rougher flotation of the barite tailings containing 37.23% BaSO4 was experimentally investigated. Secondly, two models called the multivariable linear regression (MLR) and the artificial neural network (ANN) were used for the estimation of the recovery and grade of the barite concentrate for the rougher flotation optimization. The R2 values found from the MLR and ANN models were 0.828 and 0.995 for the concentrate recovery, and 0.977 and 0.960 for the barite concentrate grade, respectively. In the comparison of the models determined, it was found that the ANN model expressed quite well than the MLR models, especially for the recovery of the rougher concentrate