Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm

• Autorzy: Tian Niu , Huang Songwei , He Lifang , Du Lingpan , Yang Sheping , Huang Bin

Identyfikatory

DOI

10.37190/ppmp/169942

• Języki publikacji: EN

Abstrakty

EN

The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link.The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansionfactor. The control performance is directly related to the contraction-expansionfactor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Itssimulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.

Słowa kluczowe

EN

fuzzy control; contraction-expansion factor; particle swarm optimization

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2023
• Tom: Vol. 59, iss. 3
• Strony: art. no. 169942
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Tian Niu

• Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

autor

Huang Songwei

• Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

autor

He Lifang

• Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

autor

Du Lingpan

• Yunnan Phosphate Chemical Group Co., Ltd.(National Engineering Research Center of Phosphate Resources Development and Utilization), Kunming 650600, China

autor

Yang Sheping

• Key Laboratory of Sanjiang Metallogeny and Resource Exploration and Utilization, MNR, Kunming 650051, China
• Yunnan Provincial Bureau of Geology and Mineral Exploration and Development Center Laboratory, Kunming 650051, China

autor

Huang Bin

• Key Laboratory of Sanjiang Metallogeny and Resource Exploration and Utilization, MNR, Kunming 650051, China
• Yunnan Provincial Bureau of Geology and Mineral Exploration and Development Center Laboratory, Kunming 650051, China

Bibliografia

• AJIT KRISSHNA N L, DEEPAK V K, MANIKANTAN K, et al. Face recognition using transform domain feature extraction and PSO-based feature selection. Applied Soft Computing, 2014, 22,141-161.
• ALEXANDROV A, PALENOV M.Self-Tuning PID-I Controller. IFAC Proceedings Volumes, 2011, 44(1),3635-3640.
• BLANCHETT T P, KEMBER G C, DUBAY R. PID gain scheduling using fuzzy logic. ISA Transactions, 2000, 39(3),317-325.
• CAMACHO N A, MERMOUD M D, CONCHA M O. Fractional order controllers for throughput and product quality control in a grinding mill circuit. European Journal of Control, 2020.
• CHANG W-D. Multimodal function optimizations with multiple maximums and multiple minimums using an improved PSO algorithm. Applied Soft Computing, 2017, 60,60-72.
• CHEN X-S, LI Q, FEI S-M. Constrained model predictive control in ball mill grinding process. Powder Technology, 2008, 186(1),31-39.
• DHAL P, AZAD C. A multi-objective feature selection method using Newton’s law based PSO with GWO. Applied Soft Computing, 2021, 107, 107394.
• DUONG H Q, NGUYEN Q H, NGUYEN D T, et al. PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor. Emerging Science Journal, 2022, 6(2),201-216.
• ELLOUMI W, EL ABED H, ABRAHAM A, et al. A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP[J]. Applied Soft Computing, 2014, 25,234-241.
• ELITH J, BURGMAN M A, REGAN H M. Mapping epistemic uncertainties and vague concepts in predictions of species distribution[J]. Ecological Modelling, 2002, 157(2),313-329.
• EUZEBIO T A M, BARROS P R. Iterative Procedure for Tuning Decentralized PID Controllers. IFAC-PapersOnLine, 2015, 48(8): 1180-1185.
• GU Q, WANG Q, CHEN L, etal. A dynamic neighborhood balancing-based multi-objective particle swarm optimization for multi-modal problems.Expert Systems with Applications, 2022, 205,117713.
• ISWANTO, MA’ARIF A, MAHARANI RAHARJA N, et al. PID-based with Odometry for Trajectory Tracking Control on Four-wheel Omnidirectional Covid-19 Aromatherapy Robot. Emerging Science Journal, 2021, 5, 157-181.
• JUANG Y-T, CHANG Y-T, HUANG C-P. Design of fuzzy PID controllers using modified triangular membership functions. Information Sciences, 2008, 178(5),1325-1333.
• KUMAR N, KUMAR H. A fuzzy clustering technique for enhancing the convergence performance by using improved Fuzzy c-means and Particle Swarm Optimization algorithms.Data & Knowledge Engineering, 2022, 140.
• LI H-X, MIAO Z-H, LEE E S. Variable universe stable adaptive fuzzy control of a nonlinear system[J]. Computers & Mathematics with Applications, 2002, 44(5),799-815.
• LIU X, ZHAO B, LIU D. Fault tolerant tracking control for nonlinear systems with actuator failures through particle swarm optimization-based adaptive dynamic programming. Applied Soft Computing, 2020, 97,106766.
• LV L, DENG Z, LIU T, et al. Intelligent technology in grinding process driven bydata: A review. Journal of Manufacturing Processes, 2020, 58,1039-1051.
• MAO X, SONG S, DING F. Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with Levy flight[J]. Journal of Energy Storage, 2022, 49, 104139.
• MARTINO F D, SESSA S. PSO image thresholding on images compressed via fuzzy transforms.Information Sciences, 2020, 506: 308-324.
• MUISYO I N, MURIITHI C M, KAMAU S I. Enhancing low voltage ride through capability of grid connected DFIG based WECS using WCA-PSO tuned STATCOM controller. Heliyon, 2022, 8(8), e09999.
• MURUGANANDHAM A, WAHIDA BANU R S D. Adaptive fractal image compression using PSO[J]. Procedia Computer Science, 2010, 2,338-344.
• NAJIM K, HODOUIN D, DESBIENS A. Adaptive control: state of the art and an application to a grinding process[J]. Powder Technology, 1995, 82(1),59-68.
• PANG H, LIU F, XU Z. Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization. Neurocomputing, 2018, 306,130-140.
• PRAJAPATI A. A particle swarm optimization approach for large-scale many-objective software architecture recovery[J]. Journal of King Saud University -Computer and Information Sciences, 2021.
• PRASAD Y, BISWAS K K, HANMANDLU M. A recursive PSO scheme for gene selection in microarray data. Applied Soft Computing, 2018, 71,213-225.
• REZNIK L, GHANAYEM O, BOURMISTROV A. PID plus fuzzy controller structures as a design base for industrial applications[J]. Engineering Applications of Artificial Intelligence, 2000, 13(4),419-430.
• RIGHETTINI P, STRADA R. Driving Technologies for the Design of Additive Manufacturing Systems. HighTech and Innovation Journal, 2021, 2(1), 20-28.
• SUN J, CHEN W, FANG W, et al. Gene expression data analysis with the clustering method based on an improved quantum-behaved Particle Swarm Optimization[J]. Engineering Applications of Artificial Intelligence, 2012, 25(2),376-391.
• UGUZ S, SAHIN U, SAHIN F. Edge detection with fuzzy cellular automata transition function optimized by PSO[J]. Computers & Electrical Engineering, 2015, 43,180-192.
• WAN J, JIANG Q, LIAO L, et al. A neural-network based variable universe fuzzy control method for power and axial power distribution control of large pressurized water reactors. Annals of Nuclear Energy, 2022, 175,109241.
• WANG T, ZOU W, XU R, et al. Assessing load in ball mill using instrumented grinding media. Minerals Engineering, 2021, 173,107198.
• XING Z, ZHU J, ZHANG Z, et al. Energy consumption optimization of tramway operation based on improved PSO algorithm. Energy, 2022, 258: 124848.
• YU J, WANG S, XI L. Evolving artificial neural networks using an improved PSO and DPSO[J]. Neurocomputing, 2008, 71(4),1054-1060.
• YUAN A G, WANG Q F, HAO X U. analysis of actuality and approaches to the supplying capacity of domestic metal mineral resources. Resources & Industries, 2007.
• ZADEH L A.Fuzzy sets. Information and Control, 1965, 8(3),338-353.
• ZHAO P, ZHAO B, PAN J, et al. Nano-grinding process of single-crystal silicon using molecular dynamics simulation: Nano-grinding parameters effect. Materials Science in Semiconductor Processing, 2022, 143,106531.
• ZOU M, YI J, YANG C, et al. Adaptive Fuzzy Logic Control for Grinding Process Based on Grinding Sound Trend. IFAC-PapersOnLine, 2022, 55(21),120-125.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).