@article{HASIDI2024103198,
title = {Data-driven system for intelligent monitoring and optimization of froth flotation circuits using Artificial Neural Networks and Genetic Algorithms},
journal = {Journal of Process Control},
volume = {137},
pages = {103198},
year = {2024},
issn = {0959-1524},
doi = {https://doi.org/10.1016/j.jprocont.2024.103198},
url = {https://www.sciencedirect.com/science/article/pii/S0959152424000386},
author = {Oussama Hasidi and El Hassan Abdelwahed and Moulay Abdellah {El Alaoui-Chrifi} and Rachida Chahid and Aimad Qazdar and Sara Qassimi and Fatima Zahra Zaizi and François Bourzeix and Intissar Benzakour and Ahmed Bendaouia},
keywords = {Froth flotation process, Intelligent control and monitoring, Artificial Intelligence, Mixture of Experts, Genetic algorithms, Smart connected mine},
abstract = {In minerals processing, the froth flotation is one of the widely used process that separates valuable mineral components from their associated gangue materials. The efficiency of this process relies on several factors, such as feed characteristics, particle size, pulp flow rate, pH, conditioning time, aeration, reagents system and many other affecting parameters. These processing parameters significantly impact the overall performance of the flotation process and influence the quality of the final concentrate. For instance, improper pulp flow and reagent dosing systems can result in metal loss and waste, particularly when dealing with frequently changing ore compositions. In this work, we established an Artificial Intelligence-based system which goal is to intelligently monitor flotation circuits and to recommend set-points for the process’s manipulated variables in order to achieve optimal performance. The system has been developed and evaluated within an industrial flotation plant that processes complex Pb-Cu-Zn sulfide ores. Leveraging an Artificial Neural Network-based Mixture of Experts (MoEs) predictive model, the system accurately estimates the mineral grades in the final concentrate and tailing of the flotation circuit. Moreover, using a Genetic Algorithms-based optimization pipeline, the system recommends set-points for the manipulated variables of the process for a maximum recovery and optimal product quality. The industrial validation of the predictive component demonstrated a 94% accuracy with a rapid 3s response time. Furthermore, the hypothetical simulation of the optimization component indicated a potential 5% increase in circuit recovery and a 4% increase of lead (Pb) grade in the circuit’s final concentrate. This developed system aims to enhance the control of froth flotation process, stabilize the product quality, and improve the overall economic benefits of production efficiency. This research contributes to the field of manufacturing systems by providing practical data-driven application for the advanced monitoring, optimization and control of industrial processes with a specific emphasis on the froth flotation process.}
}

{"_id":"EM67P87pRLRLcQueZ","bibbaseid":"hasidi-abdelwahed-elalaouichrifi-chahid-qazdar-qassimi-zaizi-bourzeix-etal-datadrivensystemforintelligentmonitoringandoptimizationoffrothflotationcircuitsusingartificialneuralnetworksandgeneticalgorithms-2024","author_short":["Hasidi, O.","Abdelwahed, E. H.","El Alaoui-Chrifi, M. A.","Chahid, R.","Qazdar, A.","Qassimi, S.","Zaizi, F. Z.","Bourzeix, F.","Benzakour, I.","Bendaouia, A."],"bibdata":{"bibtype":"article","type":"article","title":"Data-driven system for intelligent monitoring and optimization of froth flotation circuits using Artificial Neural Networks and Genetic Algorithms","journal":"Journal of Process Control","volume":"137","pages":"103198","year":"2024","issn":"0959-1524","doi":"https://doi.org/10.1016/j.jprocont.2024.103198","url":"https://www.sciencedirect.com/science/article/pii/S0959152424000386","author":[{"firstnames":["Oussama"],"propositions":[],"lastnames":["Hasidi"],"suffixes":[]},{"firstnames":["El","Hassan"],"propositions":[],"lastnames":["Abdelwahed"],"suffixes":[]},{"firstnames":["Moulay","Abdellah"],"propositions":[],"lastnames":["El Alaoui-Chrifi"],"suffixes":[]},{"firstnames":["Rachida"],"propositions":[],"lastnames":["Chahid"],"suffixes":[]},{"firstnames":["Aimad"],"propositions":[],"lastnames":["Qazdar"],"suffixes":[]},{"firstnames":["Sara"],"propositions":[],"lastnames":["Qassimi"],"suffixes":[]},{"firstnames":["Fatima","Zahra"],"propositions":[],"lastnames":["Zaizi"],"suffixes":[]},{"firstnames":["François"],"propositions":[],"lastnames":["Bourzeix"],"suffixes":[]},{"firstnames":["Intissar"],"propositions":[],"lastnames":["Benzakour"],"suffixes":[]},{"firstnames":["Ahmed"],"propositions":[],"lastnames":["Bendaouia"],"suffixes":[]}],"keywords":"Froth flotation process, Intelligent control and monitoring, Artificial Intelligence, Mixture of Experts, Genetic algorithms, Smart connected mine","abstract":"In minerals processing, the froth flotation is one of the widely used process that separates valuable mineral components from their associated gangue materials. The efficiency of this process relies on several factors, such as feed characteristics, particle size, pulp flow rate, pH, conditioning time, aeration, reagents system and many other affecting parameters. These processing parameters significantly impact the overall performance of the flotation process and influence the quality of the final concentrate. For instance, improper pulp flow and reagent dosing systems can result in metal loss and waste, particularly when dealing with frequently changing ore compositions. In this work, we established an Artificial Intelligence-based system which goal is to intelligently monitor flotation circuits and to recommend set-points for the process’s manipulated variables in order to achieve optimal performance. The system has been developed and evaluated within an industrial flotation plant that processes complex Pb-Cu-Zn sulfide ores. Leveraging an Artificial Neural Network-based Mixture of Experts (MoEs) predictive model, the system accurately estimates the mineral grades in the final concentrate and tailing of the flotation circuit. Moreover, using a Genetic Algorithms-based optimization pipeline, the system recommends set-points for the manipulated variables of the process for a maximum recovery and optimal product quality. The industrial validation of the predictive component demonstrated a 94% accuracy with a rapid 3s response time. Furthermore, the hypothetical simulation of the optimization component indicated a potential 5% increase in circuit recovery and a 4% increase of lead (Pb) grade in the circuit’s final concentrate. This developed system aims to enhance the control of froth flotation process, stabilize the product quality, and improve the overall economic benefits of production efficiency. This research contributes to the field of manufacturing systems by providing practical data-driven application for the advanced monitoring, optimization and control of industrial processes with a specific emphasis on the froth flotation process.","bibtex":"@article{HASIDI2024103198,\ntitle = {Data-driven system for intelligent monitoring and optimization of froth flotation circuits using Artificial Neural Networks and Genetic Algorithms},\njournal = {Journal of Process Control},\nvolume = {137},\npages = {103198},\nyear = {2024},\nissn = {0959-1524},\ndoi = {https://doi.org/10.1016/j.jprocont.2024.103198},\nurl = {https://www.sciencedirect.com/science/article/pii/S0959152424000386},\nauthor = {Oussama Hasidi and El Hassan Abdelwahed and Moulay Abdellah {El Alaoui-Chrifi} and Rachida Chahid and Aimad Qazdar and Sara Qassimi and Fatima Zahra Zaizi and François Bourzeix and Intissar Benzakour and Ahmed Bendaouia},\nkeywords = {Froth flotation process, Intelligent control and monitoring, Artificial Intelligence, Mixture of Experts, Genetic algorithms, Smart connected mine},\nabstract = {In minerals processing, the froth flotation is one of the widely used process that separates valuable mineral components from their associated gangue materials. The efficiency of this process relies on several factors, such as feed characteristics, particle size, pulp flow rate, pH, conditioning time, aeration, reagents system and many other affecting parameters. These processing parameters significantly impact the overall performance of the flotation process and influence the quality of the final concentrate. For instance, improper pulp flow and reagent dosing systems can result in metal loss and waste, particularly when dealing with frequently changing ore compositions. In this work, we established an Artificial Intelligence-based system which goal is to intelligently monitor flotation circuits and to recommend set-points for the process’s manipulated variables in order to achieve optimal performance. The system has been developed and evaluated within an industrial flotation plant that processes complex Pb-Cu-Zn sulfide ores. Leveraging an Artificial Neural Network-based Mixture of Experts (MoEs) predictive model, the system accurately estimates the mineral grades in the final concentrate and tailing of the flotation circuit. Moreover, using a Genetic Algorithms-based optimization pipeline, the system recommends set-points for the manipulated variables of the process for a maximum recovery and optimal product quality. The industrial validation of the predictive component demonstrated a 94% accuracy with a rapid 3s response time. Furthermore, the hypothetical simulation of the optimization component indicated a potential 5% increase in circuit recovery and a 4% increase of lead (Pb) grade in the circuit’s final concentrate. This developed system aims to enhance the control of froth flotation process, stabilize the product quality, and improve the overall economic benefits of production efficiency. This research contributes to the field of manufacturing systems by providing practical data-driven application for the advanced monitoring, optimization and control of industrial processes with a specific emphasis on the froth flotation process.}\n}\n\n","author_short":["Hasidi, O.","Abdelwahed, E. H.","El Alaoui-Chrifi, M. A.","Chahid, R.","Qazdar, A.","Qassimi, S.","Zaizi, F. 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