Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods
The present study investigates the optimization and advanced simulation of the flotation process of coarse particles (–425 + 106) using micro-nanobubbles (MNBs). For this purpose, flotation experiments in the presence and absence of MNBs were performed on coarse quartz particles, and the results wer...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Minerals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-163X/13/1/128 |
| _version_ | 1797438416991813632 |
|---|---|
| author | Sabereh Nazari Alireza Gholami Hamid Khoshdast Jinlong Li Yaqun He Ahmad Hassanzadeh |
| author_facet | Sabereh Nazari Alireza Gholami Hamid Khoshdast Jinlong Li Yaqun He Ahmad Hassanzadeh |
| author_sort | Sabereh Nazari |
| collection | DOAJ |
| description | The present study investigates the optimization and advanced simulation of the flotation process of coarse particles (–425 + 106) using micro-nanobubbles (MNBs). For this purpose, flotation experiments in the presence and absence of MNBs were performed on coarse quartz particles, and the results were statistically analyzed. Methyl isobutyl carbinol (MIBC) was employed as a frother for generating MNBs through hydrodynamic cavitation. The significance of the operating variables, including impeller speed, air flow rate, together with the bubble size, and particle size on the flotation recovery was assessed using historical data (HD) design and analysis of variance (ANOVA). The correlation between the flotation parameters and process response in the presence and absence of MNBs was modeled using hybrid convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as the deep learning (DL) frameworks to automatically extract features from input data using a CNN as the base layer. The ANOVA results indicated that all variables affect process responses statistically and meaningfully. Significant interactions were found between air flow rate and particle size as well as impeller speed and MNB size. It was found that a CNN-RNN model could finally be used to model the process based on the intelligent simulation results. Based on Pearson correlation coefficients (PCCs), it was evident that particle size had a strong linear relationship with recovery. However, Shapley additive explanations (SHAP) was considerably more accurate in predicting relationships than Pearson correlations, even though the model outputs agreed well. |
| first_indexed | 2024-03-09T11:37:35Z |
| format | Article |
| id | doaj.art-dbf321b8cc4c4d1abc6fe9d726d12199 |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-09T11:37:35Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-dbf321b8cc4c4d1abc6fe9d726d121992023-11-30T23:40:10ZengMDPI AGMinerals2075-163X2023-01-0113112810.3390/min13010128Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) MethodsSabereh Nazari0Alireza Gholami1Hamid Khoshdast2Jinlong Li3Yaqun He4Ahmad Hassanzadeh5School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaThe Robert M. Buchan Department of Mining, Queen’s University, Kingston, ON K7L 3N6, CanadaDepartment of Mining Engineering, Higher Education Complex of Zarand, Zarand 7761156391, IranSchool of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaDepartment of Geoscience and Petroleum, Faculty of Engineering, Norwegian University of Science and Technology, 7031 Trondheim, NorwayThe present study investigates the optimization and advanced simulation of the flotation process of coarse particles (–425 + 106) using micro-nanobubbles (MNBs). For this purpose, flotation experiments in the presence and absence of MNBs were performed on coarse quartz particles, and the results were statistically analyzed. Methyl isobutyl carbinol (MIBC) was employed as a frother for generating MNBs through hydrodynamic cavitation. The significance of the operating variables, including impeller speed, air flow rate, together with the bubble size, and particle size on the flotation recovery was assessed using historical data (HD) design and analysis of variance (ANOVA). The correlation between the flotation parameters and process response in the presence and absence of MNBs was modeled using hybrid convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as the deep learning (DL) frameworks to automatically extract features from input data using a CNN as the base layer. The ANOVA results indicated that all variables affect process responses statistically and meaningfully. Significant interactions were found between air flow rate and particle size as well as impeller speed and MNB size. It was found that a CNN-RNN model could finally be used to model the process based on the intelligent simulation results. Based on Pearson correlation coefficients (PCCs), it was evident that particle size had a strong linear relationship with recovery. However, Shapley additive explanations (SHAP) was considerably more accurate in predicting relationships than Pearson correlations, even though the model outputs agreed well.https://www.mdpi.com/2075-163X/13/1/128quartz flotationmicro-nanobubbles (MNBs)operating variablesdeep learningconvolutional neural networksrecurrent neural networks |
| spellingShingle | Sabereh Nazari Alireza Gholami Hamid Khoshdast Jinlong Li Yaqun He Ahmad Hassanzadeh Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods Minerals quartz flotation micro-nanobubbles (MNBs) operating variables deep learning convolutional neural networks recurrent neural networks |
| title | Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods |
| title_full | Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods |
| title_fullStr | Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods |
| title_full_unstemmed | Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods |
| title_short | Advanced Simulation of Quartz Flotation Using Micro-Nanobubbles by Hybrid Serving of Historical Data (HD) and Deep Learning (DL) Methods |
| title_sort | advanced simulation of quartz flotation using micro nanobubbles by hybrid serving of historical data hd and deep learning dl methods |
| topic | quartz flotation micro-nanobubbles (MNBs) operating variables deep learning convolutional neural networks recurrent neural networks |
| url | https://www.mdpi.com/2075-163X/13/1/128 |
| work_keys_str_mv | AT saberehnazari advancedsimulationofquartzflotationusingmicronanobubblesbyhybridservingofhistoricaldatahdanddeeplearningdlmethods AT alirezagholami advancedsimulationofquartzflotationusingmicronanobubblesbyhybridservingofhistoricaldatahdanddeeplearningdlmethods AT hamidkhoshdast advancedsimulationofquartzflotationusingmicronanobubblesbyhybridservingofhistoricaldatahdanddeeplearningdlmethods AT jinlongli advancedsimulationofquartzflotationusingmicronanobubblesbyhybridservingofhistoricaldatahdanddeeplearningdlmethods AT yaqunhe advancedsimulationofquartzflotationusingmicronanobubblesbyhybridservingofhistoricaldatahdanddeeplearningdlmethods AT ahmadhassanzadeh advancedsimulationofquartzflotationusingmicronanobubblesbyhybridservingofhistoricaldatahdanddeeplearningdlmethods |