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MODELING AND OPTIMIZATION OF ZINC RECOVERY FROM ENYIGBA SPHALERITE IN A BINARY SOLUTION OF ACETIC ACID AND HYDROGEN PEROXIDE

Yıl 2020, Cilt: 38 Sayı: 2, 589 - 601, 01.06.2021

Öz

This work focused on the modeling and optimization of zinc recovery from sphalerite in a binary solution of acetic acid and hydrogen peroxide. The sphalerite sample was characterized using X-ray fluorescence (XRF), X-ray diffraction (XRD) and Scanning electron micrograph (SEM). The result revealed that the ore exists as zinc sulphide (ZnS). Levenberg-Marquardt (LM) back-propagation algorithm was employed for artificial neural network (ANN) modeling while central composite rotatable design (CCRD) was deployed for response surface methodology (RSM) modeling. RSM modeling gave optimum conditions of 90oC leaching temperature, 6M acid concentration, 540 rpm stirring rate, 120 minutes leaching time and 6M hydrogen peroxide concentration; at which about 89.91% zinc was recovered. Comparison of the two modeling techniques revealed that ANN (root mean square error, RMSE = 0.530, absolute average deviation, AAD = 0.681, coefficient of determination = 0.996) gave better predictions than RSM (root mean square error, RMSE = 0.755, absolute average deviation, AAD = 0.841, coefficient of determination = 0.991). Hence, ANN demonstrated higher predictive capability than RSM.

Kaynakça

  • [1] Nwoye C. I., Nwabanne J. T., Odo J. U., (2013) Open System Leaching of Sphalerite in Butanoic Acid Solution and Empirical Analysis of Zinc Extraction Based on Initial Solution pH, Leaching Time and Mass-Input, Research Journal of Chemical Sciences, 3(5), 25-31.
  • [2] Hasani M., Koleini S. M. J., Khodadadi A., (2016) Kinetics of Sphalerite Leaching by Sodium Nitrate in Sulphuric Acid, Journal of Mining and Environment, 7(1), 1–12.
  • [3] Lowicki D., Bas S., Mlynarski J., (2015) Chiral Zinc Catalysts for Asymmetric Synthesis, Tetrahedron, 71(9), 1339-1394.
  • [4] Onukwuli O. D., Nnanwube I. A., (2018) Hydrometallurgical Processing of a Nigerian Sphalerite Ore in Nitric Acid: Characterization and Dissolution Kinetics. The International Journal of Science and Technoledge, 6(3), 40-54.
  • [5] Betiku E., Okunsolawo S. S., Ajala S. O., Odelede O. S., (2015) Performance Evaluation of Artificial Neural Network Coupled with Genetic Algorithm and Response Surface Methodology in Modeling and Optimization of Biodiesel Production Process Parameters From Shea Tree (Vitellaria Paradoxa) Nut Butter, Renewable Energy, 76, 408 – 417.
  • [6] Ameer K., Bae S. W., Jo Y., Lee H. G., Ameer A., Kwon J. H., (2017a). Optimization of Microwave-Assisted Extraction of Total Extract, Stevioside and Rebaudioside-A From Stevia Rebaudiana (Bertoni) Leaves Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Modeling, Food Chemistry, 229, 198 – 207.
  • [7] Ponnusamy S. K., Subramaniam R., (2013) Process Optimization Studies of Congo Red Dye Adsorption onto Cashew Nut Shell Using Response Surface Methodology, International Journal of Industrial Chemistry, 4(17), 1-10.
  • [8] Chelgani S. C., Jorjani E., (2009) Artificial Neural Network Prediction of Al2O3 Leaching Recovery in the Bayer Process-Jajarm Alumina Plant (Iran), Hydrometallurgy, 97, 105-110.
  • [9] Esonye C., Onukwuli O. D., Ofoefule A. U., (2019) Optimization of Methyl Ester Production from Prunus Amygdalus Seed Oil Using Response Surface Methodology and Artificial Neural Networks, Renewable Energy, 130, 61-72.
  • [10] Ameer K., Chun B., Kwon J., (2017b). Optimization of Supercritical Fluid Extraction of Steviol Glycosides and Total Phenolic Content from Stevia Rebaudiana (Bertoni) Leaves Using Response Surface Methodology and Artificial Neural Network, Industrial Crops and Products, 109, 672 -685.
  • [11] Yetilmezsoy K., Demirel S., (2008). Artificial Neural Network (ANN) Approach for Modeling of Pb (III) Adsorption from Aqueous Solution by Antep Pistachio (Pistacia Vera L.) Shells, Journal of Hazardous Materials, 153, 1288 – 1300.
  • [12] Nnanwube I. A., Onukwuli O., D., Ajana S. U., (2018). Modeling and Optimization of Galena Dissolution in Hydrochloric Acid: Comparison of Central Composite Design and Artificial Neural Network, Journal of Minerals and Materials Characterization and Engineering, 6, 294-315.
  • [13] Guler E., (2015). Pressure Acid Leaching of Sphalerite Concentrate: Modeling and Optimization by Response Surface Methodology, Physicochemical Problems of Mineral Processing, 52(1),479-496.
  • [14] Xi J., Xue Y., Xu Y., Shen Y., (2013). Artificial Neural Network Modeling and Optimization of Ultrahigh Pressure Extraction of Green Tea Polyphenols, Food Chemistry, 141, 320-326.
  • [15] Pilkington J., Preston C., Gomes R. L., (2014). Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Towards Efficient Extraction of Artemisinin from Artemisia Annua, Industrial Crops and Products, 58, 15 – 24.
Yıl 2020, Cilt: 38 Sayı: 2, 589 - 601, 01.06.2021

Öz

Kaynakça

  • [1] Nwoye C. I., Nwabanne J. T., Odo J. U., (2013) Open System Leaching of Sphalerite in Butanoic Acid Solution and Empirical Analysis of Zinc Extraction Based on Initial Solution pH, Leaching Time and Mass-Input, Research Journal of Chemical Sciences, 3(5), 25-31.
  • [2] Hasani M., Koleini S. M. J., Khodadadi A., (2016) Kinetics of Sphalerite Leaching by Sodium Nitrate in Sulphuric Acid, Journal of Mining and Environment, 7(1), 1–12.
  • [3] Lowicki D., Bas S., Mlynarski J., (2015) Chiral Zinc Catalysts for Asymmetric Synthesis, Tetrahedron, 71(9), 1339-1394.
  • [4] Onukwuli O. D., Nnanwube I. A., (2018) Hydrometallurgical Processing of a Nigerian Sphalerite Ore in Nitric Acid: Characterization and Dissolution Kinetics. The International Journal of Science and Technoledge, 6(3), 40-54.
  • [5] Betiku E., Okunsolawo S. S., Ajala S. O., Odelede O. S., (2015) Performance Evaluation of Artificial Neural Network Coupled with Genetic Algorithm and Response Surface Methodology in Modeling and Optimization of Biodiesel Production Process Parameters From Shea Tree (Vitellaria Paradoxa) Nut Butter, Renewable Energy, 76, 408 – 417.
  • [6] Ameer K., Bae S. W., Jo Y., Lee H. G., Ameer A., Kwon J. H., (2017a). Optimization of Microwave-Assisted Extraction of Total Extract, Stevioside and Rebaudioside-A From Stevia Rebaudiana (Bertoni) Leaves Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Modeling, Food Chemistry, 229, 198 – 207.
  • [7] Ponnusamy S. K., Subramaniam R., (2013) Process Optimization Studies of Congo Red Dye Adsorption onto Cashew Nut Shell Using Response Surface Methodology, International Journal of Industrial Chemistry, 4(17), 1-10.
  • [8] Chelgani S. C., Jorjani E., (2009) Artificial Neural Network Prediction of Al2O3 Leaching Recovery in the Bayer Process-Jajarm Alumina Plant (Iran), Hydrometallurgy, 97, 105-110.
  • [9] Esonye C., Onukwuli O. D., Ofoefule A. U., (2019) Optimization of Methyl Ester Production from Prunus Amygdalus Seed Oil Using Response Surface Methodology and Artificial Neural Networks, Renewable Energy, 130, 61-72.
  • [10] Ameer K., Chun B., Kwon J., (2017b). Optimization of Supercritical Fluid Extraction of Steviol Glycosides and Total Phenolic Content from Stevia Rebaudiana (Bertoni) Leaves Using Response Surface Methodology and Artificial Neural Network, Industrial Crops and Products, 109, 672 -685.
  • [11] Yetilmezsoy K., Demirel S., (2008). Artificial Neural Network (ANN) Approach for Modeling of Pb (III) Adsorption from Aqueous Solution by Antep Pistachio (Pistacia Vera L.) Shells, Journal of Hazardous Materials, 153, 1288 – 1300.
  • [12] Nnanwube I. A., Onukwuli O., D., Ajana S. U., (2018). Modeling and Optimization of Galena Dissolution in Hydrochloric Acid: Comparison of Central Composite Design and Artificial Neural Network, Journal of Minerals and Materials Characterization and Engineering, 6, 294-315.
  • [13] Guler E., (2015). Pressure Acid Leaching of Sphalerite Concentrate: Modeling and Optimization by Response Surface Methodology, Physicochemical Problems of Mineral Processing, 52(1),479-496.
  • [14] Xi J., Xue Y., Xu Y., Shen Y., (2013). Artificial Neural Network Modeling and Optimization of Ultrahigh Pressure Extraction of Green Tea Polyphenols, Food Chemistry, 141, 320-326.
  • [15] Pilkington J., Preston C., Gomes R. L., (2014). Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Towards Efficient Extraction of Artemisinin from Artemisia Annua, Industrial Crops and Products, 58, 15 – 24.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Reviews
Yazarlar

Ikechukwu A. Nnanwube Bu kişi benim 0000-0002-0990-9566

Judith N. Udeaja Bu kişi benim 0000-0002-3821-1263

Okechukwu D. Onukwulı Bu kişi benim 0000-0002-0861-3536

Yayımlanma Tarihi 1 Haziran 2021
Gönderilme Tarihi 24 Kasım 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 38 Sayı: 2

Kaynak Göster

Vancouver Nnanwube IA, Udeaja JN, Onukwulı OD. MODELING AND OPTIMIZATION OF ZINC RECOVERY FROM ENYIGBA SPHALERITE IN A BINARY SOLUTION OF ACETIC ACID AND HYDROGEN PEROXIDE. SIGMA. 2021;38(2):589-601.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/