Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction
The adhesive properties of microorganisms on the surface of minerals play an important role in the biooxidation efficiency of sulfidic refractory gold ores. In this research, the simultaneous effects of monosaccharides, ore content, pyrite content, and time on the activity and growth rate of Ferropl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Current Research in Biotechnology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590262824000054 |
| _version_ | 1797341488873472000 |
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| author | Mohammad Hossein Karimi Darvanjooghi Usman T. Khan Sara Magdouli Satinder Kaur Brar |
| author_facet | Mohammad Hossein Karimi Darvanjooghi Usman T. Khan Sara Magdouli Satinder Kaur Brar |
| author_sort | Mohammad Hossein Karimi Darvanjooghi |
| collection | DOAJ |
| description | The adhesive properties of microorganisms on the surface of minerals play an important role in the biooxidation efficiency of sulfidic refractory gold ores. In this research, the simultaneous effects of monosaccharides, ore content, pyrite content, and time on the activity and growth rate of Ferroplasma acidiphilum-from native Acid Mine Drainage (AMD)- was investigated during biooxidization alongside finding the best machine learning approach for the prediction of process efficiency using the independent variables. The results revealed that the optimum condition for reaching the highest pyrite dissolution (∼75 %) is 15 days of operating time, pyrite content of 7.2 wt%, and ore content of 5 wt%, pH of 1.47, and D-+-sucrose, D-+-galactose, and D-+-fructose concentrations of 0.52, 0.09, and 0.12 wt%, respectively. The results of the model comparison indicated that the Artificial Neural Network (ANN) model was able to predict the experimental results of this study with acceptable accuracy and better than Genetic Programming (GP) and Polynomial Regression informed by Response Surface Methodology (PR-RSM) from experimental data. Finally, the results showed that the change in D-+-fructose and D-+-galactose concentration has no significant effect on ferric ions concentration and pyrite dissolution content, while the influence of alteration in D-+-sucrose concentration is significantly high. |
| first_indexed | 2024-03-08T10:19:10Z |
| format | Article |
| id | doaj.art-d7f882cc7d504d56a3de53de8ae638b6 |
| institution | Directory Open Access Journal |
| issn | 2590-2628 |
| language | English |
| last_indexed | 2024-03-08T10:19:10Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Biotechnology |
| spelling | doaj.art-d7f882cc7d504d56a3de53de8ae638b62024-01-28T04:21:46ZengElsevierCurrent Research in Biotechnology2590-26282024-01-017100179Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based predictionMohammad Hossein Karimi Darvanjooghi0Usman T. Khan1Sara Magdouli2Satinder Kaur Brar3Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Ontario M3J 1P3, CanadaDepartment of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Ontario M3J 1P3, CanadaDepartment of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Ontario M3J 1P3, CanadaCorresponding author at: Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Ontario M3J 1P3, Canada.; Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Ontario M3J 1P3, CanadaThe adhesive properties of microorganisms on the surface of minerals play an important role in the biooxidation efficiency of sulfidic refractory gold ores. In this research, the simultaneous effects of monosaccharides, ore content, pyrite content, and time on the activity and growth rate of Ferroplasma acidiphilum-from native Acid Mine Drainage (AMD)- was investigated during biooxidization alongside finding the best machine learning approach for the prediction of process efficiency using the independent variables. The results revealed that the optimum condition for reaching the highest pyrite dissolution (∼75 %) is 15 days of operating time, pyrite content of 7.2 wt%, and ore content of 5 wt%, pH of 1.47, and D-+-sucrose, D-+-galactose, and D-+-fructose concentrations of 0.52, 0.09, and 0.12 wt%, respectively. The results of the model comparison indicated that the Artificial Neural Network (ANN) model was able to predict the experimental results of this study with acceptable accuracy and better than Genetic Programming (GP) and Polynomial Regression informed by Response Surface Methodology (PR-RSM) from experimental data. Finally, the results showed that the change in D-+-fructose and D-+-galactose concentration has no significant effect on ferric ions concentration and pyrite dissolution content, while the influence of alteration in D-+-sucrose concentration is significantly high.http://www.sciencedirect.com/science/article/pii/S2590262824000054Gold recoveryBiooxidationExperimental dataMchine learningGenetic programmingArtificial neural network model |
| spellingShingle | Mohammad Hossein Karimi Darvanjooghi Usman T. Khan Sara Magdouli Satinder Kaur Brar Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction Current Research in Biotechnology Gold recovery Biooxidation Experimental data Mchine learning Genetic programming Artificial neural network model |
| title | Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction |
| title_full | Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction |
| title_fullStr | Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction |
| title_full_unstemmed | Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction |
| title_short | Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction |
| title_sort | biooxidation of refractory sulfide bearing ore using feroplasma acidophilum efficiency assessment and machine learning based prediction |
| topic | Gold recovery Biooxidation Experimental data Mchine learning Genetic programming Artificial neural network model |
| url | http://www.sciencedirect.com/science/article/pii/S2590262824000054 |
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