Adaptive Neuro-Fuzzy Inference System-Based Pulverizing Capability Model for Running Time Assessment of Ball Mill Pulverizing System

• Autorzy: Cao H. , Wang Y. , Jia L.

Warianty tytułu

PL

Modelowanie proszkowania w szacowaniu czasu pracy urządzenia do proszkowania w młynie kulowym – zastosowanie adaptacyjnego wnioskowania neuro-rozmytego

• Języki publikacji: EN

Abstrakty

EN

Ball mill pulverizing system (BMPS) of thermal power plant has high energy consumption and the running time assessment of BMPS is of important theoretical significance and practical motivation for the energy saving. In the paper, an adaptive neuro-fuzzy inference system-based pulverizing capability model (ANFIS-PCM) for running time assessment of BMPS is proposed. The proposed model integrates of the artificial neural network and the Takagi-Sugeno type fuzzy rule to construct an input-output mapping based on both human knowledge and stipulated input-output data pair. For the proposed method, the subtractive clustering algorithm is used to obtain the initial rules, and the membership functions and the rules could be determined by the learning ability. The proposed model is performed on the field data under different work conditions. The experiments results verify that the proposed model has higher prediction precision. Moreover, the proposed model has been put into practice and the field operation curve verifies that the pulverizing capability could be predicted correctlly and the running time assessment of BMPS would be realized.

PL

W artykule przedstawiono model szacowania czasu pracy urządzenia do proszkowania w młynie kulowym, opracowany w oparciu o system wnioskowania neuro-rozmytego. W systemie zintegrowano sztuczną sieć neuronową oraz model rozmyty Takagi-Sugeno. Proponowany model zbudowano na podstawie pomierzonych wartości w różnych warunkach pracy. Przeprowadzono zostały próby weryfikujące skuteczność działania, które potwierdziły wysoką sprawność algorytmu.

Słowa kluczowe

EN

pulverization; running time; ANFIS system

PL

proszkowanie; czas pracy; ANFIS

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 5
• Strony: 122--127
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Cao H.

• Xi'an Jiaotong University

autor

Wang Y.

• Xi'an Jiaotong University

autor

Jia L.

• Xi'an Jiaotong University

Bibliografia

• [1] Heng Wang, Min-ping Jia, Peng Huang, Zuo-liang Chen, A study on a new algorithm to optimize ball mill system based on modeling and GA, Energy Conversion and Management, 51 (2010), No.4, 846-850
• [2] Quansheng Duan, Jizhen Liu, Zhifang Wu, Design and experimental study on the pulverized coal concentration sensor based on γ-ray absorption method, Proceedings of 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, (2008), 901-906
• [3] Chen, Xia, Hu, Hongli, Liu, Zhihong, A pulverized coal concentration measurement system based on capacitance sensor, Proceedings of 2009 International Technology and Innovation Conference, (2009), 1-4 PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 89 NR 5/2013 127
• [4] SU Zhi-gang, WANG Pei-hong, YU Xiang-jun, LÜ Zhen-zhong, Soft sensor modeling for on-line monitoring the capacity of coal pulverizing system, Proceedings of the CSEE, 27 (2007), No. 29, 90-95
• [5] Hao Yongsheng, Yu Xiangjun, Zhao Gang, Lü Zhenzhong, Optimization for ball mill operation based on improved particle swarm optimization algorithm, Journal of Southeast University(Natural Science Edition), 38, (2008), No. 3, 419-423
• [6] Jyh-Shing Roger Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man. CY., 23, (1993), No. 3, 665-685
• [7] Roohollah Noori, Gholamali Hoshyaripour, Khosro Ashrafi, Babak Nadjar Araab, Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration, Atmospheric Environment, 44, (2010), No. 4, 476-482
• [8] Adel Mellit, Soteris A. Kalogirou, ANFIS-based modelling forphotovoltaic power supply system: A case study, Renewable Energy, 36, (2011), No. 1, 250-258
• [9] M. Mohandes, S. Rehman, S.M. Rahman, Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS), Applied Energy, 88, (2011), No. 11, 4024-4032
• [10] Ali Firat Cabalar, Abdulkadir Cevik, Candan Gokceoglu, Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering, Computers and Geotechnics, 40, (2012), 14-33
• [11] Patricia Melin, Jesus Soto, Oscar Castillo, Jose Soria, A new approach for time series prediction using ensembles of ANFIS models, Expert Systems with Applications, 39, (2012), No. 3, 3494-3506
• [12] Lixin Jia and Xinzhong Li, Self-optimization combined with fuzzy logic control for ball mill. International Journal of Computers, Systems and Signals, 1, (2000), No. 2, 231-239
• [13] Tianyou CHAI and Heng YUE, Multivariable intelligent decoupling control system and its application. Acta Automatica Sinica, 31, (2005), No. 1, 123-131
• [14] R. Yager, D. Filev, Generation of fuzzy rules by mountain clustering, Journal of Intelligent and Fuzzy Systems, 2, (1994), No. 3, 209–219