[1] Salazar J.L, Magne L, Acuna G, Cubillos F, Dynam- ic modelling and simulation of semi-autogenous mills. Minerals Engineering. 2009; 22(1): 70-7.
[2] Silva D.A, Tapia L.A, Experiences and lessons with advanced control systems for the SAG mill control in Minera Los Pelambres, IFAC Proceedings Volumes. 2009; 42(23): 25-30.
[3] Morrell S, A new autogenous and semi-autogenous mill model for scale-up, design and optimisation. Minerals Engineering. 2004; 17(3): 437-45.
[4] Ruel M, Fuzzy Logic Control on a SAG Mill, IFAC Proceedings Volumes. 2013; 46(16): 282-7.
[5] Hoseinian F.S, Faradonbeh R.S, Abdollahzadeh A, Rezai B, Soltani-Mohammadi S, Semi-autogenous mill power model development using gene expression programming, Powder Technology. 2017; 308: 61-9.
[6] Hadizadeh M, Farzanegan A, Noaparast M, A plantscale validated MATLAB-based fuzzy expert system to control SAG mill circuits, Journal of Process Control. 2018; 70: 1-1.
[7] Hadizadeh M, Farzanegan A, Noaparast M, Supervisory fuzzy expert controller for sag mill grinding circuits: Sungun copper concentrator, Mineral Processing and Extractive Metallurgy Review. 2017; 38(3): 168-79.
[8] Chai T, Zhai L, Yue H, Multiple models and neural networks based decoupling control of ball mill coal-pulverizing systems, Journal of Process Control. 2011; 21(3): 351-66.
[9] Shi S, Xiong H, A hybrid immune genetic algorithm with tabu search for minimizing the tool switch times in CNC milling batch-processing. Applied Intelligence. 2022; 52(7): 7793-807.
[10] Blasco X, Herrero J.M, Sanchis J, Martínez M, A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization, Information Sciences. 2008; 178(20): 3908-24.
[11] Chen X.S, Zhai J.Y, Li S.H, Li Q, Application of model predictive control in ball mill grinding circuit. Minerals Engineering. 2007; 20(11): 1099-108.
[12] Apelt T.A, Thornhill N.F, Inferential measurement of SAG mill parameters V: MPC simulation, Minerals Engineering. 2009; 22(12): 1045-52.
[13] Wu Y, Jin W, Ren J, Sun Z, A multi-perspective architecture for high-speed train fault diagnosis based on variational mode decomposition and enhanced multi-scale structure, Applied Intelligence. 2019; 49(11): 3923-37.
[14] Flament F, Thibault J, Hodouin D, Neural network based control of mineral grinding plants, Minerals engineering. 1993; 6(3): 235-49.
[15] Conradie A.V, Aldrich C, Neurocontrol of a ball mill grinding circuit using evolutionary reinforcement learning, Minerals engineering. 2001; 14(10): 1277-94.
[16] Bouché C, Brandt C, Broussaud A, Advanced control of gold ore grinding plants in South Africa, Minerals Engineering. 2005; 18(8): 866-76.
[17] Soleymani M, Fooladi M, Rezaeizadeh M, Bahiraie M, Experimental study of mill speed, charge filling, slurry concentration, and slurry filling on the wear of lifters in tumbling mills, Modares Mechanical Engineering. 2015; 15(4): 265-71.
[18] Omar Q.R, Valenzuela M.A, Estimation of Lifters Wear in Ball and SAG Mills using Neuro-Fuzzy Modeling, In 2018 IEEE Industry Applications Society Annual Meeting (IAS). 2018: 1-7.
[19] Soleymani M.M, Fooladi M, Rezaeizadeh M, Experimental investigation of the power draw of tumbling mills in wet grinding, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2016; 230(15): 2709-19.
[20] Apelt T.A, Asprey S.P, Thornhill N.F, Inferential measurement of SAG mill parameters, Minerals engineering. 2001; 14(6): 575-91.
[21] Morrell S, A method for predicting the specific energy requirement of comminution circuits and assessing their energy utilisation efficiency, Minerals Engineering. 2008; 21(3): 224-33.
[22] Moys M.H, Grinding to nano-sizes: Effect of media size and slurry viscosity, Minerals Engineering. 2015; 74: 64-7.
[23] Soleymani M.M, Fooladi M, Rezaeizadeh M, Effect of slurry pool formation on the load orientation, power draw, and impact force in tumbling mills, Powder Technology. 2016; 287: 160-8.
[24] McCaffery K.M, Katom M, Craven J.W, Ongoing evolution of advanced SAG mill control at Ok Tedi. Mining, Metallurgy & Exploration. 2002; 19(2): 72.
[25] Fernandes M, Corchado J.M, Marreiros G, Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review, Applied Intelligence. 2022: 1-35.
[26] Jiang Y, Chen J, Zhou H, Yang J, Xu G, Residual learning of the dynamics model for feeding system modelling based on dynamic nonlinear correlate factor analy sis, Applied Intelligence. 2021; 51(7): 5067-80.
[27] Holland J.H, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press. 1992. [28] Deb K, Pratap A, Agarwal S, Meyarivan T.A, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE transactions on evolutionary computation. 2002; 6(2): 182-97.
[29] Deb K, Multi-objective optimization, InSearch methodologies. 2014: 403-449.
[30] Rezaeizadeh M, Fooladi M, Powell M.S, Mansouri S.H, Weerasekara N.S, A new predictive model of lifter bar wear in mills, Minerals Engineering. 2010; 23(15): 1174-81.
[31] Tavares L.M, Breakage of single particles: quasi-static, Handbook of powder technology. 2007; 12: 3-68.
[32] Arora J.S, Introduction to optimum design, Elsevier; 2004.
[33] Jnr W.V, Morrell S, The development of a dynamic model for autogenous and semi-autogenous grinding, Minerals engineering. 1995; 8(11): 1285-97.
[34] Konak A, Coit D.W, Smith A.E, Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety. 2006; 91(9): 992-1007.
[35] Wang Z, Lu J, Chen C, Ma J, Liao X, Investigating the multi-objective optimization of quality and efficiency using deep reinforcement learning, Applied Intelligence. 2022:1-5.
[36] Li Y, Li W, Zhao Y, Li S, Hybrid multi-objective optimization algorithm based on angle competition and neighborhood protection mechanism, Applied Intelligence. 2022: 1-23.
[37] Vanderplaats, G.N, Numerical optimization techniques for engineering design: with applications. 1984; 1.
[38] Liu G, Zhou X, Xu X, Wang L, Zhang W, Fault diagnosis of diesel engine information fusion based on adaptive dynamic weighted hybrid distance-taguchi method (ADWHD-T), Applied Intelligence. 2022; 13:1-23.
[39] Mandal D, Pal S.K, Saha P, Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II, Journal of Materials Processing Technology. 2007; 186(1): 154-162.
[40] Mohammadi Soleymani M and Mirzadeh S, Multi-objective optimization of operating parameters in tumbling mill with Neuro-Fuzzy network, Modares Mechanical Engineering. 2020; 20(9): 2331-2341.
