Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal
Abstract
:1. Introduction
2. Background and Finding the Gaps
2.1. Automation in the Mining Industry and Opportunities for AI
2.2. Application of Soft Computing in Mineral Extraction and Processing
2.3. Applications of Soft Computing in the Mining Stage
- (a)
- Density: For ore deposits, density is an important variable that provides information about the composition and mineralogy of the deposit.
- (b)
- Water transmitting ability: Refers to the capacity of a rock or mineral to allow the flow of water through it. It is an important variable in understanding the material’s hydrogeological characteristics.
- (c)
- Fracture development degree: Corresponds to the extent and intensity of fractures or cracks within the rock or mineral. It can affect the permeability and fluid flow within the deposit.
- (d)
- Confined water pressure: Refers to the pressure exerted by water within confined spaces or pores in the deposit. It can influence the stability and behaviour of the deposit.
- (e)
- Watery property of the floor aquifer: This variable refers to the characteristics of the water present, such as its chemical composition, pH, and mineral content. It can impact the interaction between the aquifer and the ore deposit.
- (f)
- Aquifuge thickness and strength: Refers to the thickness of impermeable or low-permeability layers that prevent the flow of water. Strength refers to the resistance of these layers to deformation or failure. These variables can affect the hydrogeological conditions and water movement within the deposit.
- (g)
- Mining thickness and depth: This variable refers to the thickness of the ore body being extracted. Depth refers to the vertical distance from the surface to the ore body. These variables are important in determining the feasibility and logistics of mining operations.
- (h)
- Inclined productivity: Refers to the efficiency and productivity of mining operations in inclined or sloping deposits. It considers factors such as the angle of the deposit and the methods used for extraction.
2.4. Applications of Soft Computing in the Comminution Stage
- (a)
- Particle size: A critical variable in comminution processes, as it affects the efficiency of subsequent mineral extraction stages. Achieving the optimal particle size is essential for efficient extraction of the desired mineral.
- (b)
- Composition and hardness: The composition and hardness of the mineral being processed can significantly impact mill performance. Different minerals may require different grinding conditions to achieve the desired particle size.
- (c)
- Operational conditions: Factors such as the mill speed, feed rate, and grinding media size can influence the efficiency and effectiveness of comminution processes.
- (d)
- External disturbances: Changes in ore feed characteristics or variations in power supply can affect the stability and performance of comminution circuits.
- (e)
- Product size setpoint: This variable is the target size for the final product and is used as a control parameter in grinding circuits.
- (f)
- Rock types: These refer to different types of rocks or minerals present in the ore mixture. They are used as labels or classes for classification purposes.
2.5. Applications of Soft Computing in the Flotation Stage
- The design of flotation circuits is being simplified, which facilitates the regulation and control of the processes [76].
- The cell size has increased over time, going from 50 m3 to designs close to 600 m3 [77].
- The development of new technologies for online image analysis has made it possible to provide information on the status of the equipment involved in the process, such as stirring motors, valves, sensors, and pumps, and the quality of variable measurements such as air supply, pH, and bubble size [78].
- (a)
- Flotation performance variables: These include parameters such as float recovery, total copper recovery, acid-insoluble copper recovery, and concentrate and tail grades. These variables are used to evaluate the effectiveness of different control strategies and technologies in improving flotation performance.
- (b)
- Speed rate: An important parameter in flotation circuits, as it affects the residence time of particles in the circuit and can impact flotation performance. Operator control and MPC velocity control show the impact of different control strategies.
- (c)
- Bubble size and gas holdup: These variables are important in understanding the behaviour of froth flotation systems, as they impact froth stability, mass pull, and flotation performance. The relationship between the bubble size and air rate can be used to predict changes in the pulp height.
- (d)
- Reagent dosage: Reagents are used in flotation circuits to promote particle–bubble attachment and improve flotation performance. A relationship exists between the reagent dosage and froth surface appearance, and machine vision and predictive modelling can be used to control reagent dosage.
- (e)
- Advanced measurement technologies: The use of advanced measurement technologies, such as image processing, X-ray fluorescence, and diffused reflective spectroscopy, improves the measurement and control of key variables in mineral processing operations. These technologies enable the more accurate and reliable measurement of parameters such as the Cu grade, froth velocity, and concentrate and tail grades.
2.6. Applications of Soft Computing in the Hydrometallurgy Stage
- (a)
- pH: This variable plays a significant role in controlling the behaviour of chemical reactions. It is an important variable in the leaching process as it can affect the rate of element recovery.
- (b)
- Particle size: Refers to the size of the particles of the element of interest in the ore. It is an important factor that can affect the efficiency of the leaching process.
- (c)
- Temperature: A key variable that affects the efficiency and kinetics of chemical reactions, directly affecting the rate of element recovery
- (d)
- Time: Refers to the duration of processes such as leaching. It is a critical variable as it determines the amount of time available for the element of interest to dissolve into the leaching solution.
- (e)
- Element of interest’s grade: The grade or initial quantity of the element to recover is a relevant base variable considered in predicting models for copper and gold recovery. This variable can be complemented by the change in element concentration in solution during the process.
- (f)
- Reactive consumption: Reagents are used to increase the kinetics of chemical reactions. Here, the consumption of thiourea or agents such as ferric ions can impact the recovery efficiency.
- (g)
- Solid percentage: Corresponds to the proportion of solid material in a solution. It is a relevant parameter in solid–liquid processes such as stirring leaching that can influence the efficiency.
- (h)
- Stirring speed: Refers to the speed at which the leaching solution is agitated. It is a parameter that can influence the contact between the particles of the element of interest and the leaching solution, affecting the recovery rate.
2.7. Applications of Soft Computing in the Pyrometallurgy Stages
- (a)
- Blowing air variables: This includes parameters such as the blowing flow rate, airspeed, and oxygen enrichment.
- (b)
- Top gas variables: These variables are related to nitrogen and oxygen flow rates.
- (c)
- Temperature variables: These are related to parameters such as flame temperature and hot metal temperature.
- (d)
- Fuel variables: These include parameters such as coke and pulverised coal consumption rates.
- (e)
- Ore variables: These variables are related to parameters such as pellet, sinter, and iron ore consumption rates.
- (f)
- Hot metal variables: These variables are related to the content of hot metal in production and may include carbon, silicon, manganese, and phosphorus.
- (g)
- Slag variables: These variables are related to the production of slag and include slag basicity and volume.
3. Performance of Soft Computing Applied in Mineral Extraction and Processing
4. Proposed Approach for the Application of Soft Computing in Waste Disposal in Mineral Extraction and Processing
- Operating factors (input material, deposition rate, geometrical and geotechnical controls such as humidity and compaction).
- Deposit location (climate and geological factors that include the seismicity, ground foundation slope, and confinement of the land degree)
- Deposit type selected (type of the TSF, geometric configuration including height, volume, and slope angle)
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Ramirez, O. Digitization of Mining: Major Challenges and Motivations; SPT Mining: Stockholm, Sweden, 2021. [Google Scholar]
- European Parliament. Resource Efficiency: Moving towards a Circular Economy; European Parliament: Strasbourg, France, 2014. [Google Scholar]
- Ge, Z. Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemom. Intell. Lab. Syst. 2017, 171, 16–25. [Google Scholar] [CrossRef]
- DOMO. Data Never Sleeps 10.0. Available online: https://www.domo.com/es/data-never-sleeps (accessed on 24 October 2023).
- Taylor, P. Amount of Data Created, Consumed, and Stored 2010–2020, with Forecasts to 2025. Statista. Available online: https://www.statista.com/statistics/871513/worldwide-data-created/ (accessed on 24 October 2023).
- Duarte, F. Amount of Data Created Daily. Exploding Topics. 2023. Available online: https://explodingtopics.com/blog/data-generated-per-day (accessed on 24 October 2023).
- Usman, M.; Ma, Z.; Zafar, M.W.; Waheed, A.; Li, M. Analyzing the determinants of clean energy consumption in a sustainability strategy: Evidence from EU-28 countries. Environ. Sci. Pollut. Res. 2021, 28, 54551–54564. [Google Scholar] [CrossRef] [PubMed]
- Krzaklewski, M.; Van Laere, H. Opinion of the European Economic and Social Committee on “Digital Mining in Europe: New solutions for the sustainable production of raw materials”. European Economic and Social Committee, Consultative Commission on Industrial Change. 2020. Available online: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:52020IE1559 (accessed on 24 October 2023).
- Harris, S. Unearthing the Future: How Digital Is Revolutionizing the Mining Industry; Orange Business Services: Paris, France, 2021. [Google Scholar]
- Fundación Chile. ROADMAP: Digitalización para una Minería 4.0; Fundación Chile: Santiago, Chile, 2020. [Google Scholar]
- Qi, C.-c. Big data management in the mining industry. Int. J. Miner. Metall. Mater. 2020, 27, 131–139. [Google Scholar] [CrossRef]
- Flores, V.; Hadfeg, Y.; Bekios, J.; Quelopana, A.; Meneses, C. A method for automatic generation of explanations from a rule-based expert system and ontology. In Advances in Intelligent Systems and Computing; Springer: Berlin/Heidelberg, Germany, 2017; pp. 167–176. [Google Scholar] [CrossRef]
- Bergh, L.G.; Yianatos, J.B.; Leiva, C.A. Fuzzy supervisory control of flotation columns. Miner. Eng. 1998, 11, 739–748. [Google Scholar] [CrossRef]
- Sanchez, J.A. Control Avanzado de Procesos (Teoría y Práctica); Diaz de Santos S.A.: Madrid, Spain, 2003. [Google Scholar]
- Sbárbaro, D.; del Villar, R. Advanced Control and Supervision of Mineral Processing Plants; Springer: London, UK, 2010. [Google Scholar] [CrossRef]
- Nad, A.; Jooshaki, M.; Tuominen, E.; Michaux, S.; Kirpala, A.; Newcomb, J. Digitalization Solutions in the Mineral Processing Industry: The Case of GTK Mintec, Finland. Minerals 2022, 12, 210. [Google Scholar] [CrossRef]
- Argyropoulos, S.A. Artificial Intelligence in Materials Processing Operations: A Review and Future Directions. ISIJ Int. 1990, 30, 83–89. [Google Scholar] [CrossRef]
- Shean, B.J.; Cilliers, J.J. A review of froth flotation control. Int. J. Miner. Process. 2011, 100, 57–71. [Google Scholar] [CrossRef]
- Uusi-Hallila, S.; Paavola, M.; Leiviskä, K. Utilizing Froth Phase Behaviour and Machine Vision to Indicate Flotation Performance; University of Oulu: Oulu, Finland, 2014. [Google Scholar]
- Philip, T.P. Process Control in Metallurgical Plants—From an Xstrata Perspective. IFAC Proc. Vol. 2007, 40, 377–389. [Google Scholar] [CrossRef]
- Brooks, K.; Munalula, W. Flotation Velocity and Grade Control Using Cascaded Model Predictive Controllers. IFAC-PapersOnLine 2017, 50, 25–30. [Google Scholar] [CrossRef]
- Ai, M.; Xie, Y.; Tang, Z.; Zhang, J.; Gui, W. Deep learning feature-based setpoint generation and optimal control for flotation processes. Inf. Sci. 2021, 578, 644–658. [Google Scholar] [CrossRef]
- Aldrich’, C.; Moolman, D.W.; Gouws, F.S.; Schmitz, G.F. Machile Learning Strategies for Control of Flotation Plants. IFAC Proc. Vol. 1995, 28, 99–105. [Google Scholar] [CrossRef]
- Hadler, K.; Cilliers, J.J. The relationship between the peak in air recovery and flotation bank performance. Miner. Eng. 2009, 22, 451–455. [Google Scholar] [CrossRef]
- Ai, M.; Xie, Y.; Xie, S.; Zhang, J.; Gui, W. Fuzzy association rule-based set-point adaptive optimization and control for the flotation process. Neural Comput. Appl. 2020, 32, 14019–14029. [Google Scholar] [CrossRef]
- Valera, A.; Vallés, M.; Díez, J.L. Simulación y Control de Procesos Físicos de Forma Remota. In Revista Iberoamericana de Automatica e Informática Industrial; Universidad Politécnica de Valencia: Valencia, Spain, 2005; pp. 20–29. [Google Scholar]
- Romero, D.; Bernus, P.; Noran, O.; Stahre, J.; Berglund, Å.F. The operator 4.0: Human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems. In IFIP Advances in Information and Communication Technology; Springer: New York, NY, USA, 2016; pp. 677–686. [Google Scholar] [CrossRef]
- Liu, D.; Yuan, Y.; Liao, S. Artificial neural network vs. nonlinear regression for gold content estimation in pyrometallurgy. Expert Syst. Appl. 2009, 36, 10397–10400. [Google Scholar] [CrossRef]
- Estrada, F.; Cipriano, A. Hybrid Model Predictive Control for Grinding Plants. In Proceedings of the 19th World Congress the International Federation of Automatic Control, Cape Town, South Africa, 24–29 August 2014. [Google Scholar]
- Martin, V.; Eng, P.; Fontaine, D.; Cathcart, J. Challenges with conducting tailings dam breach studies. In Proceedings of the Tailings and Mine Waste 2015, Vancouver, BC, Canada, 26–28 October 2015. [Google Scholar]
- Fu, Y.; Yang, B.; Ma, Y.; Sun, Q.; Yao, J.; Fu, W.; Yin, W. Effect of particle size on magnesite flotation based on kinetic studies and machine learning simulation. Powder Technol. 2020, 376, 486–495. [Google Scholar] [CrossRef]
- McCoy, J.T.; Auret, L. Machine learning applications in minerals processing: A review. Miner. Eng. 2019, 132, 95–109. [Google Scholar] [CrossRef]
- Hoseinian, F.S.; Abdollahzade, A.; Mohamadi, S.S.; Hashemzadeh, M. Recovery prediction of copper oxide ore column leaching by hybrid neural genetic algorithm. Trans. Nonferrous Met. Soc. China (Engl. Ed.) 2017, 27, 686–693. [Google Scholar] [CrossRef]
- Sun, W.; Xue, Y. An Improved Fuzzy Comprehensive Evaluation System and Application for Risk Assessment of Floor Water Inrush in Deep Mining. Geotech. Geol. Eng. 2019, 37, 1135–1145. [Google Scholar] [CrossRef]
- Danish, E.; Onder, M. Application of Fuzzy Logic for Predicting of Mine Fire in Underground Coal Mine. Saf. Health Work 2020, 11, 322–334. [Google Scholar] [CrossRef]
- Li, M.; Wang, H.; Wang, D.; Shao, Z.; He, S. Risk assessment of gas explosion in coal mines based on fuzzy AHP and bayesian network. Process Saf. Environ. Prot. 2020, 135, 207–218. [Google Scholar] [CrossRef]
- Stange, W. Using Artificial Neural Networks for the Control of Grinding Circuits. Miner. Eng. 1993, 6, 479–489. [Google Scholar] [CrossRef]
- Tessier, J.; Duchesne, C.; Bartolacci, G. A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts. Miner. Eng. 2007, 20, 1129–1144. [Google Scholar] [CrossRef]
- Olivier, L.E.; Craig, I.K.; Chen, Y.Q. Fractional order and BICO disturbance observers for a run-of-mine ore milling circuit. J. Process Control. 2012, 22, 3–10. [Google Scholar] [CrossRef]
- Hamzeloo, E.; Massinaei, M.; Mehrshad, N. Estimation of particle size distribution on an industrial conveyor belt using image analysis and neural networks. Powder Technol. 2014, 261, 185–190. [Google Scholar] [CrossRef]
- Umucu, Y.; Deniz, V.; Bozkurt, V.; Fatih Çaʇlar, M. The evaluation of grinding process using artificial neural network. Int. J. Miner. Process. 2016, 146, 46–53. [Google Scholar] [CrossRef]
- Cai, W.; Dou, L.; Zhang, M.; Cao, W.; Shi, J.Q.; Feng, L. A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring. Tunn. Undergr. Space Technol. 2018, 80, 232–245. [Google Scholar] [CrossRef]
- Olivier, L.E.; Maritz, M.G.; Craig, I.K. Deep Convolutional Neural Network for Mill Feed Size Characterization. IFAC-PapersOnLine 2019, 52, 105–110. [Google Scholar] [CrossRef]
- Moolman, D.W.; Aldrich, C.; Van Deventer, J.S.J.; Stange, W.W. The classification of froth structures in a copper flotation plant by means of a neural net. Int. J. Miner. Process. 1995, 43, 193–208. [Google Scholar] [CrossRef]
- Ramasamy, M.; Narayanan, S.S.; Rao, C.D.P. Control of ball mill grinding circuit using model predictive control scheme. J Process Control 2005, 15, 273–283. [Google Scholar] [CrossRef]
- Chen, X.-s.; Zhai, J.-y.; Li, S.-h.; Li, Q. Application of model predictive control in ball mill grinding circuit. Miner. Eng. 2007, 20, 1099–1108. [Google Scholar] [CrossRef]
- Cortés, G.; Verdugo, M.; Fuenzalida, R.; Cerda, J.; Honeywell, E.C. Rougher Flotation Multivariable Predictive Control; Concentrator A-1 Division CODELCO Norte. In Proceedings of the V International Mineral Processing Seminar, Santiago, Chile, 22–24 October 2008. [Google Scholar]
- Aldrich, C.; Marais, C.; Shean, B.J.; Cilliers, J.J. Online monitoring and control of froth flotation systems with machine vision: A review. Int. J. Miner. Process. 2010, 96, 1–13. [Google Scholar] [CrossRef]
- Riquelme, A.; Desbiens, A.; Del Villar, R.; Maldonado, M. Identification of a non-linear dynamic model of the bubble size distribution in a pilot flotation column. Int. J. Miner. Process. 2015, 145, 7–16. [Google Scholar] [CrossRef]
- Ali, D.; Hayat, M.B.; Alagha, L.; Molatlhegi, O.K. An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal. Adv. Powder Technol. 2018, 29, 3493–3506. [Google Scholar] [CrossRef]
- Hoseinian, F.S.; Aliakbar, A.; Bahram, R. Semi-autogenous mill power prediction by a hybrid neural genetic algorithm. J. Cent. South Univ. 2018, 25, 151–158. [Google Scholar] [CrossRef]
- Shean, B.; Hadler, K.; Neethling, S.; Cilliers, J.J. A dynamic model for level prediction in aerated tanks. Miner. Eng. 2018, 125, 140–149. [Google Scholar] [CrossRef]
- Zhang, J.; Tang, Z.; Ai, M.; Gui, W. Nonlinear modeling of the relationship between reagent dosage and flotation froth surface image by Hammerstein-Wiener model. Miner. Eng. 2018, 120, 19–28. [Google Scholar] [CrossRef]
- Zhang, J.; Tang, Z.; Xie, Y.; Ai, M.; Zhang, G.; Gui, W. Data-driven adaptive modeling method for industrial processes and its application in flotation reagent control. ISA Trans. 2021, 108, 305–316. [Google Scholar] [CrossRef]
- Moreno, C.M.; Pérez-Correa, J.R.; Otero, A. Dynamic modelling of copper solvent extraction mixer-settler units. Miner. Eng. 2009, 22, 1350–1358. [Google Scholar] [CrossRef]
- Pang, Q.; Fu, P.; Zhong, X. Fuzzy control of pH value in rare-earth impurity leaching process. In Proceedings of the 2011 International Conference on Electronics, Communications and Control, ICECC 2011—Proceedings, Ningbo, China, 9–11 September 2011; pp. 2065–2067. [Google Scholar] [CrossRef]
- Azizi, A.; Ghaedrahmati, R.; Ghahramani, N.; Rooki, R. Modelling and simulation of the cyanidation process of Aghdareh gold ore using artificial neural network and multiple linear regression. Int. J. Min. Miner. Eng. 2016, 7, 139–154. [Google Scholar] [CrossRef]
- Gao, T.; Yin, S.; Qiu, J.; Gao, H.; Kaynak, O. A partial least squares aided intelligent model predictive control approach. IEEE Trans. Syst. Man Cybern. Syst. 2018, 48, 2013–2021. [Google Scholar] [CrossRef]
- Xu, R.; Nan, X.; Meng, F.; Li, Q.; Chen, X.; Yang, Y.; Xu, B.; Jiang, T. Analysis and prediction of the thiourea gold leaching process using grey relational analysis and artificial neural networks. Minerals 2020, 10, 811. [Google Scholar] [CrossRef]
- Gui, W.-H.; Wang, L.-Y.; Yang, C.-H. Transactions of Nonferrous Metals Society of China Intelligent Prediction Model of Matte Grade in Copper Flash Smelting Process. 2007. Available online: www.csu.edu.cn/ysxb/ (accessed on 24 April 2023).
- Deng, X.; Wang, X. Incremental learning of dynamic fuzzy neural networks for accurate system modeling. Fuzzy Sets Syst. 2009, 160, 972–987. [Google Scholar] [CrossRef]
- Liu, J.-h.; Gui, W.-h.; Xie, Y.-f.; Yang, C.-h. Dynamic modeling of copper flash smelting process at a Smelter in China. Appl. Math. Model. 2014, 38, 2206–2213. [Google Scholar] [CrossRef]
- Savic, M.V.; Djordjevic, P.B.; Mihajlovic, I.N.; Zivkovic, Z.D. Statistical modeling of copper losses in the silicate slag of the sulfide concentrate smelting process. Pol. J. Chem. Technol. 2015, 17, 62–69. [Google Scholar] [CrossRef]
- Puspita, A.N.G.; Surjandari, I.; Zulkarnain; Kawigraha, A.; Permatasari, N.V. Optimization of saprolite ore composites reduction process using artificial neural network (ANN). Procedia Comput. Sci. 2019, 161, 424–432. [Google Scholar] [CrossRef]
- Cardoso, W.; Di Felice, R.; Baptista, R.C. Artificial Neural Networks for Modelling and Controlling the Variables of a Blast Furnace. In Proceedings of the 6th International Forum on Research and Technology for Society and Industry, RTSI 2021—Proceedings, Virtual, 6–9 September 2021; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2021; pp. 148–152. [Google Scholar] [CrossRef]
- Qian, Q.; Fang, X.; Xu, J.; Li, M. Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process. J. Manuf. Syst. 2021, 61, 375–390. [Google Scholar] [CrossRef]
- Cardoso, W.; di Felice, R.; Baptista, R.C. Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke. Mater. Res. 2022, 25, e20210439. [Google Scholar] [CrossRef]
- Wang, X.; Hu, T.; Tang, L. A Multiobjective Evolutionary Nonlinear Ensemble Learning with Evolutionary Feature Selection for Silicon Prediction in Blast Furnace. IEEE Trans. Neural Netw. Learn. Syst. 2022, 33, 2080–2093. [Google Scholar] [CrossRef]
- Yang, C.; Jin, F.; Zhao, J.; Wang, W. A Deep-Convolution-Generative-Adversarial-Networks-based Missing Data Filling Method for Blast Furnace Gas System in Steel Industry. In Proceedings of the 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022, Chengdu, China, 3–5 August 2022; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2022; pp. 1183–1189. [Google Scholar] [CrossRef]
- Zhao, L.; Zhu, D.; Liu, D.; Wang, H.; Xiong, Z.; Jiang, L. Prediction and Optimization of Matte Grade in ISA Furnace Based on GA-BP Neural Network. Appl. Sci. 2023, 13, 4246. [Google Scholar] [CrossRef]
- Reitsma, F.; Woods, P.; Fairclough, M.; Kim, Y.; Tulsidas, H.; Lopez, L.; Zheng, Y.; Hussein, A.; Brinkmann, G.; Haneklaus, N.; et al. On the sustainability and progress of energy neutral mineral processing. Sustainability 2018, 10, 235. [Google Scholar] [CrossRef]
- Cisternas, L.A.; Ordóñez, J.I.; Jeldres, R.I.; Serna-Guerrero, R. Toward the Implementation of Circular Economy Strategies: An Overview of the Current Situation in Mineral Processing. Miner. Process. Extr. Metall. Rev. 2021, 43, 775–797. [Google Scholar] [CrossRef]
- Wang, L.; Peng, Y.; Runge, K.; Bradshaw, D. A review of entrainment: Mechanisms, contributing factors and modelling in flotation. Minerals Engineering 2015, 70, 77–91. [Google Scholar] [CrossRef]
- COCHILCO. Sulfuros Primarios: Desafíos y Oportunidades; COCHILCO: Santiago, Chile, 2017. [Google Scholar]
- Laurila, H.; Karesvuori, J.; Tiili, O. Strategies for instrumentation and control of flotation circuits. In Mineral Processing Plant Design, Practice, and Control Proceedings; Society for Mining, Metallurgy, and Exploration (SME): Englewood, CO, USA, 2002; pp. 2174–2195. [Google Scholar]
- Moilanen, J.; Remes, A. Control of the flotation process. In Proceedings of the V International Mineral Processing Seminar (Procemin 2008), Santiago, Chile, 22–24 October 2008; pp. 318–325. [Google Scholar]
- Dunne, R.C.; Lane, G.S.; Richmond, G.D.; Dioses, J. Flotation data for the design of process plants Part 1—Testing and design procedures. Trans. Inst. Min. Metall. Sect. C Miner. Process. Extr. Metall. 2010, 119, 199–204. [Google Scholar] [CrossRef]
- Vallejos, P.; Yianatos, J.; Matamoros, C.; Díaz, F. Mineral solids transport in a two-dimensional flotation froth. Miner. Eng. 2019, 138, 24–30. [Google Scholar] [CrossRef]
- Park, H.; Wang, L. Experimental studies and modeling of surface bubble behaviour in froth flotation. Chem. Eng. Res. Des. 2015, 101, 98–106. [Google Scholar] [CrossRef]
- Åkesson, B.M.; Toivonen, H.T. A neural network model predictive controller. J. Process Control 2006, 16, 937–946. [Google Scholar] [CrossRef]
- Wills, A.; Schön, T.B.; Ljung, L.; Ninness, B. Identification of Hammerstein-Wiener models. Automatica 2013, 49, 70–81. [Google Scholar] [CrossRef]
- Sun, K.; Qiu, J.; Karimi, H.R.; Gao, H. A Novel Finite-Time Control for Nonstrict Feedback Saturated Nonlinear Systems with Tracking Error Constraint. IEEE Trans. Syst. Man Cybern. Syst. 2021, 51, 3968–3979. [Google Scholar] [CrossRef]
- Kallioinen, J.; Heiskanen, K. Effective Flotation of a Difficult Nickel-Ore Based on Intelligent Mineral Technology. Miner. Eng. 1993, 6, 917–928. [Google Scholar] [CrossRef]
- Harbort, G.; de Bono, S.; Carr, D.; Lawson, V. Jameson cell fundamentals—A revised perspective. Miner. Eng. 2003, 16, 1091–1101. [Google Scholar] [CrossRef]
- Carr, D.; Dixon, A.; Tiili, O. Optimizing Large Flotation Cell Performance Through Advanced Instrumentation and Control. In Proceedings of the 10th Mill Operators Conference, Adelaide, Australia, 12–14 October 2009; pp. 299–304. [Google Scholar]
- Morari, M.; Lee, J.H. Model predictive control: Past, present and future. Comput. Chem. Eng. 1999, 23, 667–682. [Google Scholar] [CrossRef]
- Adetola, V.; DeHaan, D.; Guay, M. Adaptive model predictive control for constrained nonlinear systems. Syst. Control Lett. 2009, 58, 320–326. [Google Scholar] [CrossRef]
- Liu, L.; Liu, Y.-J.; Tong, S. Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems. IEEE Trans. Cybern. 2019, 49, 2536–2545. [Google Scholar] [CrossRef] [PubMed]
- Rosenfeld, A.; Tsotsos, J.K. Incremental Learning through Deep Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 2020, 42, 651–663. [Google Scholar] [CrossRef]
- Komulainen, T.; Pekkala, P.; Rantala, A.; Jämsä-Jounela, S.L. Dynamic modelling of an industrial copper solvent extraction process. Hydrometallurgy 2006, 81, 52–61. [Google Scholar] [CrossRef]
- Project, T.D. Sustainable Improvement in Safety of Tailings Facilities a European Research and Technological Development Project Report Tailings Management Facilities—Intervention Actions for Risk Reduction; University of Leeds: Leeds, UK, 2004. [Google Scholar]
- COCHILCO. Yearbook: Copper and Other Mineral Statistics 2001–2020; COCHILCO: Santiago, Chile, 2020. [Google Scholar]
- Palma, J.H. Operación y control de tranques de relave. In Seminario Minería Chilena y sus Desafíos: Una Visión Integral; Pontificia Universidad Católica de Chile: Santiago, Chile, 2016. [Google Scholar] [CrossRef]
- Kreft-Burman, K.; Saarala, J.; Anderson, R. Sustainable Improvement in Safety of Tailings Facilities. In TAILSAFE; Finnish Environment Institute (SYKE): Helsinki, Finland, 2005. [Google Scholar]
- The Mining Association of Canada. A Guide to the Management of Tailings Facilities VERSION 3.1; The Mining Association of Canada: Ottawa, ON, Canada, 2019. [Google Scholar]
- Parviainen, A.; Kauppila, T.; Loukola-Ruskeeniemi, K. Long-term lake sediment records and factors affecting the evolution of metal(loid) drainage from two mine sites (SW Finland). J. Geochem. Explor. 2012, 114, 46–56. [Google Scholar] [CrossRef]
- Zardari, M.A. Stability of Tailings Dams—Focus on Numerical Modelling. Licenciate Thesis, Luleå Tekniska Universitet, Luleå, Sweden, 2011. [Google Scholar]
- Rusu, A.A.; Rabinowitz, N.C.; Desjardins, G.; Soyer, H.; Kirkpatrick, J.; Kavukcuoglu, K.; Pascanu, R.; Hadsell, R. Progressive Neural Networks. June 2016. Available online: http://arxiv.org/abs/1606.04671 (accessed on 6 April 2022).
- French, R.M. Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference. In Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, Atlanta, GA, USA, 13–16 August 1994. [Google Scholar]
- Herrera, L.J.; Pomares, H.; Rojas, I.; Guillén, A.; Prieto, A.; Valenzuela, O. Recursive prediction for long term time series forecasting using advanced models. Neurocomputing 2007, 70, 2870–2880. [Google Scholar] [CrossRef]
- He, H.; Chen, S.; Li, K.; Xu, X. Incremental Learning from Stream Data. IEEE Trans. Neural Netw. 2011, 22, 1901–1914. [Google Scholar] [CrossRef]
Author | Operation | Soft Computing Application | |||||
---|---|---|---|---|---|---|---|
RF | EXS | FL | ANN | CNN | MPC | ||
Sun et al. [34] | Mining Stage | ● | |||||
Danish et al. [35] | ● | ||||||
Li et al. [36] | ● | ||||||
Stange et al. [37] | Comminution | ● | ● | ||||
Tessier et al. [38] | ● | ||||||
Olivier et al. [39] | ● | ||||||
Estrada et al. [29] | ● | ||||||
Hamzeloo et al. [40] | ● | ||||||
Umucu et al. [41] | ● | ||||||
Cai et al. [42] | ● | ||||||
Olivier et al. [43] | ● | ||||||
Aldrich et al. [23] | Flotation | ● | |||||
Moolman et al. [44] | ● | ||||||
Ramasamy et al. [45] | ● | ||||||
Chen et al. [46] | ● | ||||||
Cortes et al. [47] | ● | ||||||
Aldrich et al. [48] | ● | ||||||
Riquelme et al. [49] | ● | ||||||
Brooks et al. [21] | ● | ||||||
Ali et al. [50] | ● | ● | ● | ||||
Hoseinian et al. [51] | ● | ||||||
Shean et al. [52] | ● | ||||||
Zhang et al. [53] | ● | ||||||
Ai et al. [25] | ● | ||||||
Fu, Y. et al. [31] | ● | ||||||
Ai et al. [22] | ● | ||||||
Zhang et al. [54] | ● | ||||||
Komulainen et al. [55] | Hydrometallurgy | ● | |||||
Moreno et al. [55] | ● | ||||||
Pang et al. [56] | ● | ||||||
Azizi et al. [57] | ● | ||||||
Hoseinian et al. [33] | ● | ||||||
Gao et al. [58] | ● | ||||||
Xu et al. [59] | ● | ||||||
Gui et al. [60] | Pyrometallurgy | ● | |||||
Deng et al. [61] | ● | ||||||
D. Liu et al. [28] | ● | ||||||
J. Liu et al. [62] | ● | ||||||
Savic et al. [63] | ● | ||||||
Ghea Puspita et al. [64] | ● | ||||||
Cardoso et al. [65] | ● | ||||||
Qian et al. [66] | ● | ||||||
Cardoso et al. [67] | ● | ||||||
Wang et al. [68] | ● | ||||||
Yang et al. [69] | ● | ||||||
Zhao et al. [70] | ● |
Paper | Problematic | Method | Data Used | Specific Variables | Results |
---|---|---|---|---|---|
Sun et al. [34] | Risk assessment of floor water inrush in deep mining | Improved fuzzy comprehensive evaluation, Delphi method, and analytic hierarchy process | Hydrogeological data from six industrial mining faces | Density, water-transmitting ability, fracture development degree, confined water pressure, watery property of the floor aquifer, aquifuge thickness and strength, mining thickness and depth, inclined length | The approach provides a tool for the risk assessment of floor water inrush in deep mining, where the results are consistent with the field-observed results. |
Danish et al. [35] | Predicting mine fires in underground coal mines | Fuzzy logic model with Mamdani inference system | Data from 10 gas monitoring stations collected from sensors in an underground coal mine | Input variables: CO, O2, N2, temperature; Output variable: Fire intensity | The fuzzy logic system is reliable for decision making regarding fire intensity and assessing fire intensity with variables at the same time (validated using Graham’s index), and identified suspected areas for spontaneous combustion. |
Li et al. [36] | Quantitative risk assessment of gas explosions in underground coal mines | Combination of fuzzy analytic hierarchy process (FAHP) and Bayesian network (BN) | EXS application of risk factors related to gas explosions in underground coal mines | Flow rate, pressure, pipe diameter, pipe roughness coefficient, pump efficiency, energy consumption, cost | Inference to predict the probability of gas explosion risks with the determination of accident causes. The identification of the weight variables helps determine the optimal combination for flow rate, pressures, pipe diameter for each pipe segment, pump efficiency, and pipe roughness coefficient. |
Paper | Problematic | Method | Data Used | Specific Variables | Numeric Results |
---|---|---|---|---|---|
Stange et al. [37] | Control of grinding circuits, specifically autogenous milling | ANN for control strategies and exploration of various control approaches by EXS | No specific data mentioned; theoretical discussion of ANN use in grinding circuit control | No specific variables mentioned; exploration of various control approaches using ANNs | Proposes two control strategies using ANNs for the control of autogenous grinding circuits. ANNs have significant potential in developing a model of the hydrocyclone classifier. |
Tessier et al. [38] | Online estimation of rock composition for nickel mineral treatment | Machine vision approach for feature extraction, dimensionality reduction, and class boundary establishment using support vector machines | Digital images of five different mineral types and mixtures of them | Composition of rock mixtures: colour and textural features extracted from sub-images | Good estimation for mixture compositions for dry ore but some inaccuracies for wet ore mixtures due to light reflection. The proposed approach can be used for real-time monitoring of variations in run-of-mine ore composition. |
Olivier et al. [39] | Improving control performance in a milling circuit | MPC controller with a fractional order disturbance observer (FO-DOB) and a Bode ideal cut-off disturbance observer (BICO-DOB) | Simulation data generated from a non-linear MIMO plant model | Controlled variables: product particle size, fraction of the mill volume filled with material, slurry volume in the sump. Mill-manipulated variables: solid feed-rate, water feed-rate, steel balls feed-rate, water flowrate into the sump and slurry flowrate into the cyclone | The FO-DOB and BICO-DOB are useful tools for ROM ore milling circuit control. This addition to the normal PI controller gives better results than the PI controller alone because of the decrease in the ISE values. The BICO-DOB has poorer disturbance rejection performance than the other two DOB varieties but gives the best set-point tracking performance. |
Estrada et al. [29] | Develop an HMPC strategy for grinding circuits | Hybrid MPC controller identification procedure for two controlled variables | Data from industrial data-tuned grinding simulator | Conveyor feed rate, water feeding sump, SAG mill speed, product hardness, specific energy consumption, product particle size; Activation/deactivation of secondary grinding circuits and product granulometric distribution | A hybrid identification procedure for two controlled variables is correctly performed, with energy consumption minimization and the maintenance of particle size output. |
Hamzeloo et al. [40] | Estimate particle size distribution on an industrial conveyor belt in a copper concentrator | Image analysis and ANN | Images collected from an industrial conveyor belt in the crushing circuit of a copper concentrator | Particle size distribution, image pixel values, scaling factors, size features, eigenvectors and eigenvalues, cumulative passing %, volume of particles, metal ball diameter | Model estimations of particle size distribution achieve an overall RMSE of 6.11%. For area-based size estimations, the model obtains an RMSE of 4.45%. It obtains an RMSE of 18.54% for weight-based size estimations. Other size measures had RMSE values ranging from 5.45% to 37.11% for area-based size estimations and from 18.54% to 37.11% for weight-based size. |
Umucu et al. [41] | Grinding system modelling of calcite in mineral processing | ANN–MLPNN and RBFNN | Experimental data collected from laboratory conditions | Input variables: cumulative percentages of ball mill feed, ball mill conditions, and grinding time. Output variables: ball mill product cumulative percentages | The RBFNN model performs better than the MLPNN model, highlighting the importance of analysing data and using capable systems for fast decision-making. The study used different powder filling levels for the calcite sample to evaluate the statistical data obtained from the ANN models. |
Cai et al. [42] | Rock burst forecasting in underground coal mining | Fuzzy comprehensive evaluation model | Microseismic monitoring data from a coal mine and laboratory acoustic emission measurements of coal samples | Fault total area, space-time diffusivity, equivalent energy magnitude, seismicity degree, time information entropy, source concentration degree, seismic diffusivity | Importantly, the proposed methodology was successfully applied to a coal mine using a combination of indices for more accurate forecasting. Microseismic monitoring is a powerful tool for rock burst forecasting |
Olivier et al. [43] | Characterization of the feed ore size distribution in a milling circuit | Deep CNN application for classification | Feed ore images captured from an industrial conveyor belt with a vertically mounted camera | 223 images captured and categorised in four groups | The CNN achieved an overall accuracy of 96.4% in classifying into one of four categories based on size distribution, with an overall F1-score metric of 0.97. |
Paper | Problematic | Method | Data Used | Variables | Results |
---|---|---|---|---|---|
Aldrich et al. [23] | Classification of different froth structures in flotation cells | Decision tree and CNN algorithms for constructing knowledge-based systems | Surface froth images of two industrial flotation cells. Training data set consisting of 400 exemplars randomly sampled from the four classes | Froth image characteristics (e.g., statistical features) | The CNN system correctly classified unknown froth forms. The system improves profitability and reduces operating instability. The net classified froth structures with 96% accuracy, but limited training data sets reduced classification performance to 68%. |
Moolman et al. [44] | Grinding efficiency of dry ball mills | Machine vision application to the feed with a ANN model from a laboratory-scale ball mill | Grinding aids and stage efficiency | Grinding data as particle size distribution, specific surface area, and grinding efficiency | SGLDM and NGLDM analysis of digitised froth images shows feature extraction potential. Neural networks classify foam shapes well. Dry ball mills can grind 25% better with grinding aids. SGLDM outperformed NGLDM at 66.7% with a 90% classification rate. However, NGLDM classified froth conditions better than SGLDM features at 78.9% compared to 52.4%. |
Ramasamy et al. [45] | Comparison of predictive control schemes with detuned multi-loop PI controllers for controlling ball mill grinding circuits | MPC | Experiments conducted under five different operating conditions. Process model parameters such as the breakage rate function and hydrocyclone model parameters based on steady-state data collected from the circuit | Variables controlled: cyclone overflow fraction passing 104 μm and mill throughput. Fresh feed rate (0.375 to 0.5 kg/min), sump water addition rate, mill solids (67% to 74%), slurry pumping rate, hydrocyclone model parameters, breakage rate function, and sump level | Detuned multi-loop PI controllers oscillated and could not eliminate control loop interactions. The MPC system reached setpoints without overshoot or offset and decoupled well. Constrained MPC suppresses big input moves and is more resistant to operational conditions than PI controllers. PI controllers modify variables more than MPC. The mill throughput–sump water loop with PI controls is slower. |
Chen et al. [46] | Implementing a model predictive control in a ball mill grinding circuit | MPC | Ball mill grinding circuit in an iron ore concentrator plant process variable | Particle size, mill solids concentration, sump level, circulating load, fresh feed rate, mill feed water flow rate, dilution water flow rate, vibratory conveyor speed, mill feed water control valve’s opening, dilution water control valve’s opening, pump speed | The study shows that MPC improves grinding circuit performance, ensures operational stability, and greatly reduces overload situation warnings. MPC lowered overload condition warnings by 31.9% over PID controllers. In addition, MPC lowered overload condition warnings by 85.8%. The MPC technique showed its efficacy in the invested time period, reducing alarms and ensuring operational continuity. |
Cortes et al. [47] | Stabilise rougher flotation circuit operation | MPC application based in Honeywell’s Profit Controller | Rougher flotation circuit data from Concentrator A-1 at División Codelco Norte | Airflow rate set points, level control, pH, and tonnage variation | Profit FLOT enhanced control and improved process stability and copper recovery. Profit FLOT on daily shifts increased the A-1 concentrator recovery by 1.5%. Higher-profit FLOT use, practise enhancements, strategy revisions, and operator training can still capture marginal benefits. Performance and recovery rates may improve further with further optimisation. |
Aldrich et al. [48] | Control in froth flotation processes | Implementation of automated systems based on CNN using machine vision | Froth images obtained from laboratory and industrial flotation cells, operational data such as the air flow rate, pulp level, and concentrate grade | Bubble size and shape, air flow rate, pulp level, concentrate grade | Machine vision and image analysis techniques can be used to monitor froth stability, but fully automated control is not possible. |
Riquelme et al. [49] | Identify and measure bubble size distribution in flotation | Image processing and a parametric method with a circular Hough transform (CHT) and log-normal distribution for MPC | Bubble images from flotation columns obtained through a camera system conducted with different frother concentrations and superficial air velocities | Frother concentration, superficial air velocity, bubble size distribution parameters, bubble Sauter mean diameter, flotation recovery | CHT detects clustered bubbles better than other approaches. Log-normal distribution estimates BSD well. BSD parameter dynamics are explained well by the non-linear Wiener model. After experiments, static models were estimated using a non-linear least squares technique with R2 = 93.3% and 98.5%. |
Brooks et al. [21] | Optimization of a copper roughing circuit to improve recovery in an oxide rougher circuit | Application of sophisticated technologies such as image processing, X-ray fluorescence, diffused reflective spectroscopy, and cascaded MPC | Manipulated, disturbance and controlled variables in flotation cells | Feed flows, densities, air flows, pulp levels, feed, concentrate and tail Cu grades, and froth velocity | Successful MPC installation improves float and Cu recovery. Innovative measurement technology improved the accuracy and reliability of critical control parameters such as the Cu grade, froth velocity, and concentrate and tail grades. |
Ali et al. [50] | Predict the flotation behaviour of fine high-ash coal in the presence of a hybrid ash depressant | Random forest, ANN, fuzzy logic, and adaptive neuro-fuzzy inference system | Flotation experiments on fine high-ash coal with a training data set (80% of total data) and test dataset (20% of total data) containing five inputs and two outputs | Inputs: Al-PAM polymer dosage, pH, polymer conditioning time, dispersant dosage, and impeller speed. Outputs: combustible recovery and froth ash content | The models predict the performance of the coal flotation process. The fuzzy logic model had the best prediction performance in coal flotation. Overall accuracy: FL > ANN > ANFIS > RF > HyFIS. |
Hoseinian et al. [51] | Develop a model to predict SAG mill power | Hybrid ANN algorithm model application | SAG mill operation dataset from Aq Darreh gold processing plant (GA population size: 100, max generation: 450) | Feed moisture, mass flowrate, mill load cell mass, SAG mill solid percentage, inlet water flow rate, outlet water flow rate, work index, and mill power | Correlation coefficient (R) of the GANN model: 0.9127 in testing compared to ANN alone with an R of 0.7947. Obtained relationship input parameters for the work index, inlet and oulet water to the SAG mill, mill load cell mass, SAG mill solid percentage, mass flowrate and feed moisture. Mean squared error (MSE) of the GANN model: 0.0451 in training, 0.0430 in testing. MSE of the ANN model: 0.1549 in training, 0.4054 in testing |
Shean et al. [52] | Predicting changes in pulp height in froth flotation | Development of a dynamic model from a flotation laboratory test | Froth flotation mass balance and calibration using experimental data | Bubble size distribution, air flow rate, and pulp height under different conditions | The dynamic model predicts steady-state froth flotation pulp height variations. The experimental system responds slower to the reagent than the model because the model assumes the system is well mixed, while the experimental results show plug flow. The model can be adjusted to reflect industrial flotation control’s dynamic nature. |
Zhang et al. [53] | Simulating the relationship between the reagent dosage and froth surface appearance in a lead–zinc flotation plant | Hammerstein–Wiener-based model with the illumination modelling-based marker watershed method for EXS application | Reagent data and froth surface images collected from a lead–zinc flotation plant (149 pairs used for developing the Hammerstein–Wiener model and 60 pairs used for testing and validating the model) | Reagent variables (frother, activator, and collector dosage), froth surface variables (bubble size distribution, froth surface image, and highlight spot marker), model variables (Hammerstein–Wiener model) | The log-normal distribution can describe lead–zinc flotation plant bubble size distribution (BSD) in the quiet zone below the interface. The Hammerstein–Wiener model beats the Wiener model, LS-SVM model, and neural network model in fitting accuracy and performance with an RMSE of 0.0420 and an R-squared of 0.9721. The proposed approach can guide reagent dosage changes to manage mineral processing froth flotation and segment zinc froth image bubbles with 95.6% accuracy. |
Ai et al. [25] | Flotation reagent control | Reduction in extracted deep learning features using a stacked autoencoder, fuzzy association setpoint calculation, and offline Q-learning-based reagent control | Feed characteristics, froth grade, flotation reagents, and froth videos. RL benchmark environment data with 1,000,000 samples using a soft actor–critic (SAC) controller | Froth images to extract four features: bubble size, bubble shape, froth velocity, and froth color. | The method outperformed other existing methods in terms of the MAE and qualified ratio of the concentrate grade. It was effective and promising for practical flotation reagent control. |
Fu et al. [31] | Effect of particle size and process time on magnesite flotation using machine learning to predict flotation performance | EXS application and mathematical modelling | Flotation experiments performed on magnesite with seven size fractions and six flotation times | Feed particle size, flotation time, pulp pH, collector dosage, recovery rate of MgO and SiO2 | Optimal particle size range identified for magnesite flotation of 30 to 48 μm. The EXS method performs better than other models in predicting the MgO and SiO2 recovery, which increased from 54.18% to 95.12% and from 50 to 400 mg/L, respectively. The SC model is an effective tool for predicting the effects of flotation parameters. |
Ai et al. [22] | Set-point adaptive optimization and control strategy for antimony flotation process | Fuzzy logic functions, machine vision, and FAR mining to extract information and generate optimal set-points | Feed grade, reagent dosages, froth image, and concentrate grade. A total of 1000 groups of data in the desired concentrate grade range, with 950 groups to generate FARs and the rest for validation | Feed grade, reagent dosages, froth image, concentrate grade, and image features: froth height, froth colour, froth velocity, and bubble size | Better performance compared to manual manipulation and other automatic control methods with an FNN-based prediction accuracy for feed grade of RRMSE: 2.94% and MRE: 8.73%. Improved control performance in concentrate grade. |
Zhang et al. [54] | Develop an adaptive modelling method for froth flotation reagent control | An adaptive ANN auto-regressive model (A-NNARX) for dynamic froth flotation control. In a non-linear model, the model predicts the flotation reagent control technique | Flotation industry data. The data are categorised by feed grade: low zinc, normal, high zinc, and high lead | Concentrate quality, zinc and lead feed grade, froth videos, flotation reagents, hand-crafted image characteristics, bubble size distribution. Colour, texture, and foam velocity. PCA components (control inputs, process outputs), quadratic cost function, Euclidean distance (ED) between the target foam image features and control results, histogram bins, test samples, and training data | The A-NNARX model improved the qualified ratio by 0.1666 compared to manual control, achieved better performance in terms of Euclidean distance, and increased the qualified ratio under expert control from 0.7500 to 0.8194 while decreasing the MAE error by 0.1384. A weight-level regularization method improved the capacity for deformation evaluation. |
Paper | Problematic | Method | Data Used | Specific Variables | Results |
---|---|---|---|---|---|
Komulainen et al. [90] | Developing a dynamic process simulator for copper solvent extraction plants | Mechanistic models for MPC | One month of operating data from an industrial copper solvent extraction process | Input variables: PLS concentration, lean electrolyte concentration and rate, and flow rates. Output variables: Loaded and barren organic concentrations and rich electrolyte and raffinate concentrations | Mechanistic models accurately describe the SX process trends. The mean residual is well below 2% for organics and rich electrolyte and around 6% for raffinates, which is considered very good considering the poor measurement accuracy of these streams |
Moreno et al. [55] | Developing a dynamic model for mixer–settler units used in the solvent extraction (SX) process of copper plants | Dynamic modelling for MPC | Copper concentration, pH, SX operation variables, equilibrium isotherm calculations, and mass transfer expressions | Input variables: Aqueous inlet flowrate, pH, Cu+2 aqueous and organic inlet, organic inlet flowrate, mixer volume, settler volume, and free acidity in electrolyte. Output variables: Volumes of phases in the mixer, flowrates at the mixer exit, Cu+2 in aqueous phase at the mixer exit, and Cu+2 in organic phase at the mixer exit | By incorporating time delay and flexible model fitting parameters, a better fitting settler model was found, accurately reproducing changes in SX. The relative mean squared errors for outlet copper concentrations in the extraction unit were 0.03% (aqueous phase) and 6.76% (organic phase), while for the stripping unit they were 0.07% (aqueous phase) and 2.89% (organic phase). |
Pang et al. [56] | Improving control performance in leaching rare earths using a self-tuning PID control algorithm | Fuzzy parameter self-tuning PID control algorithm | Simulation data | pH and element contents in solution. Proportional, integration, and differential adjustment factors | Simulation results show that the fuzzy parameter self-tuning PID control algorithm outperforms traditional PID control algorithms in terms of control performance, response time, and accuracy. A relationship between the pH value and the amount of the initial solution is established |
Azizi et al. [57] | Predicting gold recovery in the cyanidation process | ANN and MLR | Cyanide leaching circuit of gold mine | Input includes pH, solid percentage, NaCN concentration, particle size and leaching time. Output: Au recovery | ANN provides efficient and cost-effective, with highly accurate predictions of 0.556 for the training and 0.67 compared to MLR. Leaching time and particle size are the most important factors affecting gold extraction. |
Hoseinian et al. [33] | Optimization of the copper oxide column leaching process | ANN and GANN | A database of 120 sets of copper leaching column tests; 96 sets were used to train the network and 24 sets were used to test the model | Particle size, column height, leaching time, and acid flow rates | Copper recovery has an inverse relation with the column height and particle size and a direct relation with the leaching time and the acid flow rate. The GANN model is more efficient than the ANN model for Cu recovery prediction with reasonable accuracy. The algorithm can be incorporated in the training phase of a network to improve prediction. |
Gao et al. [58] | Developing a data-driven model predictive control approach for dynamic systems | Data-driven MPC approach that combines modified partial least squares (PLS) and MPC | Multiple input and output variables from laboratory tests | Input variables: Input flows and heat to the tank. Output variables: Cooling water temperature, atmospheric temperature, steady state temperature, and steady state level | The proposed MPC approach has high prediction precision and an ability to cope with dynamics in the process, outperforming traditional MPC and MPC in a traditional PLS framework. This was tested on a continuous stirred tank heater system |
Xu et al. [59] | Using thiourea as an alternative to cyanide for gold leaching from refractory ores | Grey relational analysis and artificial neural network models | Results of leaching experiments conducted on a high-arsenic gold concentrate using A. ferrooxidans and TU as a leaching agent | Leaching time, initial pH, temperature, TU dosage, stirring speed, and ferric iron concentration | GRA and ANN models can efficiently reflect practice and provide effective suggestions for controlling optimum parameters in the leaching process. The absolute errors of gold recovery varied by 2.5% and the accuracy of the predictions was around 96%. The accuracy of the other predictions was around 97%. |
Paper | Problematic | Method | Data Used | Specific Variables | Results |
---|---|---|---|---|---|
Gui et al. [60] | Predicting matte grade in copper flash smelting | Integrating a multiphase and multi-component model with a fuzzy model | A total of 154 groups of industrial data collected from industrial production | Matte grade, copper concentrate content, pyrite content, S content, Fe content, SiO2 content, CaO content, oxygen volume, blast volume | Higher prediction precision, stronger generalization ability, reduced mean square root error, and decreased training time. |
Deng et al. [61] | Incremental learning approach for accurate system modelling | Dynamic fuzzy neural network (D-FL) using incremental learning algorithm (ILA) | Four datasets: Chaotic Mackey–Glass time series, Box–Jenkins gas furnace data, displacement prediction in the dam, and ionosphere delay prediction for the GPS satellite | Input variables: Air temperature, reservoir water level, and dam run time. Output variable: Displacement of point L3H291R. Other variables: Epoch number, times, residual error, MAPE, RMSE, and MAE | Good performance in modelling accuracy, learning convergence, and computation time. |
D. Liu et al. [28] | Estimation of gold content in slag | ANN and nonlinear regression | Small-scale experiments using a specific slag composition system to simulate industrial processes for gold extraction | Independent variables: Compositions of the soda–borax–silica glass–salt slag system. Dependent variable: Gold content in slag | The ANN method produces better estimations of gold content with higher precision compared to the traditional regression method. |
J. Liu et al. [62] | Predicting matte grade in the copper flash smelting process | MPC application based in dynamic mass balances with equilibrium relationships | Data collected at a copper smelter for 30 days | Matte grade, oxygen partial pressure, temperature, desulfurization ratio, Cu concentration, species mass balance (Cu, Fe, S, O2, SiO2, CaO, Al2O3, MgO), operational parameters, slag Cu losses, mechanical entrainment droplets of matte and dissolved Cu | The model is effective in providing guidance for controlling the copper flash smelting process with a maximum relative error of 3.3% and an average relative error of 0.54%. Regression equations for predicting the ratio of desulfurization and copper in slag were also presented. |
Savic et al. [63] | Predicting copper losses in silicate slag from the sulphide concentrate smelting process | MLR analysis, ANN, and ANFIS | Industrial data from a sulphide copper concentrate smelting process | Input variables: The percentage of copper, iron, and silica in the concentrate, percentage of coke and flux in the charge, and oxygen amount in the process. Output variable: Cu content in silicate slag | The ANFIS approach was found to be the most accurate, with a coefficient of determination of 0.989 in the training stage and 0.719 in the testing stage. |
Ghea Puspita et al. [64] | Optimization of the reduction process of saprolite ore composites | ANN | Results of the reduction process of saprolite ore composites collected from extractive metallurgical laboratories | Ratio of coal (%), process temperature (°C), time duration (hours), composite type | An optimal factor combination for the reduction process is established by ANN (composite SB15Ca10P2, 1200 °C and duration of 3 h). A validation of the results was possible through Fe, Al, and Si mass composition comparisons. |
Cardoso et al. [65] | Predicting production and quality control of hot metal in a blast furnace | ANN model based on a committee machine | Data obtained from the operation of a blast furnace at an industrial steelmaker | Input variables: Fuel, air volume, temperature. Output variables: Iron oxide, coke | Developed an artificial neural network model with a general correlation of 91.1%. |
Qian et al. [66] | Anomaly detection in a steelmaking process with multichannel profiles | Functional derivative MPC support with vector data description | Simulated data and industrial data from a steelmaking process | Multichannel profiles of the BOF steelmaking process | The proposed method outperforms all the compared models in terms of the anomaly detection rate. The method is time-efficient for online monitoring for industrial processes with a sampling frequency of no more than 96 Hz. |
Cardoso et al. [67] | Predicting silicon content in hot metal production in blast furnaces | ANN model with sigmoid activation function and the Levenberg–Marquardt algorithm | A database of 82,500 data points from 1100 operational days | A total of 75 input variables classified into different groups (blow air, temperature, top gas, others) and one output: silicon content | A neural network model with 30 hidden neurons outperformed other models in predicting silicon content in hot metal production, indicating that big data and database treatment can enhance modelling accuracy. |
Wang et al. [68] | Predicting the silicon content in hot metal in a blast furnace | MPC based on a multiobjective evolutionary algorithm (MOEA) and evolutionary feature selection (EFS) | Benchmark and actual industrial datasets | Twenty input features including former silicon content, hot air pressure, oxygen enrichment rate, hot air temperature, set amount of pulverised coal injection, blast kinetic energy, gas permeability, and dry dust removal inlet temperature | The MOENE-EFS method outperforms other ensemble soft computing methods for silicon content prediction. The proposed ELM_ENSEMBLE method is significantly superior to the two commonly used linear ensemble methods. The MOENE-EFS method achieves better performance than the MOEE-ELM, EL. NSDE1-ANN, and CNE-ELM methods. |
Yang et al. [69] | Filling missing data in a blast furnace gas system of the steel industry | Deep convolutional network (D-CNN) | Operation data from an iron and steel operation industry | Data related to the industrial blast furnace gas system operation data, specifically missing data from the hot blast stove in the gas system | The proposed method has higher data filling accuracy than existing methods and conforms to the actual distribution of samples. The network can accurately fill in the high proportion of random missing data. |
Zhao et al. [70] | Develop an optimised control model for the matte grade in the copper smelting process | ANN and GA-BP neural network prediction model | A dataset with 910 samples obtained from industrial operation | Input variables: S, Fe, SiO2, and CaO content in copper concentrate, oxygen volume, blast volume, and flux amount. Output variable: Term of matte grade | The ANN model was more accurate, with a matte-grade absolute error simulation of 0.51%, which is 56.41% lower than the matte-grade BP neural network prediction model. |
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Herrera, N.; Sinche Gonzalez, M.; Okkonen, J.; Mollehuara, R. Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal. Minerals 2023, 13, 1450. https://doi.org/10.3390/min13111450
Herrera N, Sinche Gonzalez M, Okkonen J, Mollehuara R. Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal. Minerals. 2023; 13(11):1450. https://doi.org/10.3390/min13111450
Chicago/Turabian StyleHerrera, Nelson, María Sinche Gonzalez, Jarkko Okkonen, and Raul Mollehuara. 2023. "Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal" Minerals 13, no. 11: 1450. https://doi.org/10.3390/min13111450