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

• Autorzy: Deniz Vedat , Umucu Yakup , Deniz Orcun T.

Identyfikatory

DOI

10.37190/ppmp/150646

• Języki publikacji: EN

Abstrakty

EN

This 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.

Słowa kluczowe

EN

barite; tailing; flotation; recovery; grade; MLR model; ANN model

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2022
• Tom: Vol. 58, iss. 5
• Strony: art. no. 150646
• Opis fizyczny: Bibliogr. 36 poz., rys.

Twórcy

autor

Deniz Vedat

• Hitit University, Department of Polymer Material Engineering, 19100, Corum, Turkey

• https://orcid.org/0000-0003-4098-959X

autor

Umucu Yakup

• Eskisehir Osmangazi University, Department of Mining Engineering, 26040, Eskisehir, Turkey

• https://orcid.org/0000-0002-6317-4070

autor

Deniz Orcun T.

• Technical University of Clausthal, Department of Mining Engineering, 38678, Clausthal-Zellerfeld, Germany

• https://orcid.org/0000-0003-4262-165X

Bibliografia

• ANDREWS, P.R.A., COLLINGS, R.K., 1989. Beneficiation of barite: A review of processing studies at Canmet. Min. Eng., June, 145-150.
• APLAN, F.F., FUERSTENAU, D.W., 1962. Principles of nonmetallic mineral flotation. Froth flotation, AIME, New York, pp. 170-178.
• BHATTI, M.A., KAZMI, K.R., MEHMOOD, R., AHAD, A., TABBASSUM, A., AKRAM, A., 2017. Beneficiation study on barite ore of Duddar area, district Lasbela, Balochistan province, Pakistan. Pak. J. Sci. Ind. Res. Ser. A: Phys. Sci., 60, 9-22.
• BULATOVIC, S.M., 2015. Beneficiation of barite ore. Handbook of Flotation Reagents: Chemistry, Theory and Practice, 34(3), pp. 129-141.
• CHEN, Z., REN, Z., GAO, H., ZHENG, R., JIN, Y., NIU, C., 2019. Flotation studies of fluorite and barite with sodium petroleum sulfonate and sodium hexametaphosphate. J. Mater. Process. Technol., 8, 1267-1273.
• CYTEC, 2004. AERO® 845 promoter boosts phosphate recovery and cuts bulk reagent consumption helping to increase customer’s process efficiency. Case Study. 1 p.
• DENIZ, V., 2012. The effects of ball filling and ball diameter on kinetic breakage parameters of barite powder. Adv. Powder Technol., 23, 640-646.
• DENIZ, V., 2015. Dewatering of barite clay wastewater by inorganic coagulants and co-polymer flocculants. Physicochem. Probl. Miner. Process., 51, 351- 364.
• DENIZ, V., GULER, T., 2018. Production of white barite from barite concentrates of shaking tables by bleaching process after magnetic methods. Inz. Miner., Polish Mineral Engineering Society, 19(1), 77-82.
• DENIZ, V., 2020. Application of multiple linear regression (MLR) analysis for concentration of chromite tailings by the flotation. Physicochem. Probl. Miner. Process., 56(4), 579-589.
• DENIZ, V., 2021. Prediction of barite recovery and grade by multiple linear regression (MLR) analysis in concentrating of barite tailings by using multi-gravity separator (MGS). Part. Sci. Technol., 39 (6), 748-756.
• FENG, B., LUO, X.P., WANG, J.Q., Wang, P.C., 2015. The flotation separation of scheelite from calcite using acidified sodium silicate as depressant. Miner. Eng., 80, 45-49.
• GUIMARAES, R.C., PERES, A.E.C., 1998. Relevant aspects of barite separation of a phosphate ore via flotation. In: Atak, Onal and Celik (Eds.), Innovations in Mineral and Coal Processing. A.A. Balkema, Rotterdam, pp. 285-289.
• GULLETT, J.T., MALVERN, A., REDDIE, W.A., 1964. Flotation of barite. US3122500A, US Patent Office, Patented February 15.
• GURPINAR, G., SONMEZ, E., BOZKURT, V., 2004. Effect of ultrasonic treatment on flotation of calcite, barite and quartz. Miner. Process. Extract. Metall., 113, 91-95.
• HADJIEV, A., HADJIEV, P., GEORGIEV, R., 2000. Flotation of barite from complex iron ore. International of Symposium Processing Fines (PROF-2000), Bhattacharyya, Singh, Goswami, (eds.), NML Jamshedpur, India, pp. 222-230.
• HAGAN, M.T., DEMUTH, H.B., BEALE, M.H., 1996. Neural Network Design. PWS Publishing, Boston.
• HAIR, J.F.Jr., ANDERSON, R.E., TATHAM, R.L., BLACK, W.C., 1998. Multivariate Data Analysis. (5th ed.), Prentice Hall PTR., New York.
• HANNA, H., SOMASUNDARAN, P., 1976. Flotation of salt-type minerals. In: Fuerstenau (Ed.), Flotation: Gaudin Memorial, AIME, Vol. 1, pp. 197-272.
• HARRIS, M.J., 1988. Barite Flotation, New Mexico Bureau of Mines and Mineral Resources, Open-File Report #336, El Cuervo Butte, 1-17.
• KECIR, M., KECIR, A., 2016. Efficiency of barite flotation reagents a comparative study. Inz. Miner., Polish Mineral Engineering Society, 17(2), 269-278.
• KUTNER, M.H., NACHTSHEIM, C.J., NETER, J., LI, W., 2004. Applied Linear Statistical Models. (5th ed), McGraw-Hill Higher Education.
• LAMONT, W.E., SULLIVAN, G.V., 1982. Recovery of high-grade barite from waste pond materials. USBM RI 8673, USA.
• LENZO, R., SARQUIS, P.E., 1995. Flotation of fine-size barite from gravity separation tailings. Min. Metall. Expl., 12, 118-120.
• LU, Y., LIU, W., WANG, X., CHENG, H., CHENG, F., MILLER, J.D., 2020. Lauryl Phosphate Flotation Chemistry in Barite Flotation. Minerals, 10(3), 280.
• MARINAKIS, K.I., SHERGOLD, H.L., 1985. The mechanism of fatty acid adsorption in the presence of fluorite, calcite and barite. Int. J. Miner. Process., 14, 161-176.
• MARTINEZ, E., HAAGENSEN, R.B., KUDRYK, V., 1975. Application of new techniques in developing a barite flotation process, Trans. Soc. Min. Eng., AIME, 258(1), 27-30.
• NAKHAEI, F., MOSAVI, M.R., SAM, A., VAGHEI, Y., 2012. Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques. Int. J. Miner. Process., 110-111, 140-154.
• PAULSON, D.S., 2007. Handbook of regression and modelling. Chapman & Hall/CRC, Taylor&Francis Group, Florida.
• REN, Z., YU, F., GAO, H., CHEN, Z., PENG, Y., LIU, L., 2017. Selective separation of fluorite, barite and calcite with valonea extract and sodium fluosilicate as depressants. Minerals, 7(2), 24.
• SCHUBERT, H., SCHNEIDER, W., 1970. The function of non-polar and non-ionizable polar-non-polar additives during flotation, IX. Int. Miner. Process. Cong., Prague, Vol.1, pp. 189-196.
• SMITH, M., 1996. Neural Networks for Statistical Modeling, first ed. Itp New Media.
• SMITH, R.W., AKHTAR, S., 1976. Cationic flotation of oxides and silicates. Flotation, In: Fuerstenau, M.C. (Ed.), Flotation: Gaudin Memorial, AIME, Vol. 1, pp. 87-116.
• UMUCU, Y., DENIZ, V., BOZKURT, V., CAGLAR, F., 2016. The evaluation of grinding process using artificial neural network. Int. J. Miner. Process., 146, 46-53.
• ZHOU, J., LI, X.B., 2011. Evaluating the thickness of broken rock zone for deep roadways using nonlinear SVMs and Multiple Linear Regression model. Procedia Eng., 26, 972-981.
• ZHU, Q., JONES, J.M., WILLIAMS, A., THOMAS, K.M., 1999. The predictions of coal/char combustion rate using an artificial neural network approach. Fuel, 78, 1755-1762.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).