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第35届中国控制与决策会议

作者： Zhenyu Guo Yu Fu Kang Li Guobin Zou Qingkai Wang BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy College of Information Science and Engineering Northeastern University

The roller mill is a crucial production equipment for the nonferrous metal beneficiation process,responsible for crushing *** time,the roller tyre of the roller mill gradually wears down,ultimately impacting the crushing ***,Prognostics and Health Management(PHM) of the roller tyre is crucial to enhancing the production efficiency of the roller *** this study,a PHM method based on Dual Autoencoder(DUAE) is proposed to predict the grinding roller tyre's service *** effectiveness of the proposed PHM method is validated using actual data from a domestic *** research demonstrates that the PHM of roller tyres can be effectively conducted.

关键词： roller mill PHM DUAE nonferrous metal beneficiation process

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Analysis of Operational Data and Self-learning Control Rules for mining Mill Equipment

Analysis of Operational Data and Self-learning Control Rules...

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Asian Control Conference

作者： Guicheng Wang Liang Yuan Zhicheng Bai Guoliang Cai Ning Hua Xiaobin Li School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The engineering application under industrial big-data analysis is of great significance for improving the optimization effect of enterprise production process control processes. By analyzing the operating principle of the semi-automatic mill and studying its control optimization strategy, based on the operational data of the SAG mill, industrial big-data analysis is adopted to self-learning its control optimization rules. Firstly, SPSS is used to preprocess the high-dimensional process data generated during the operation of the semi-automatic mill. Four parameters, namely power, axial pressure, grinding sound, and grinding concentration, are preliminarily selected. Then, PCA is used to screen and reduce the dimensionality of multidimensional data, and K-Means clustering algorithm is used to classify and identify the reduced dataset. After simulation research, the results show that PCA can reduce the redundancy of SAG mill operation data, and the control optimization rules formed by self-learning can serve as operational guidance for improving the efficiency and energy-saving of SAG mill operation. The effectiveness can be further verified in practical engineering applications in the later stage.

关键词： Dimensionality reduction Energy consumption Technological innovation Redundancy Force process control Clustering algorithms

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Virtual Sample Generation Method Based on GAN for process Data with Its Application

Virtual Sample Generation Method Based on GAN for Process Da...

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第34届中国控制与决策会议

作者： Cui Canlin Tang Jian Xia Heng Wang Dandan Yu Gang Faculty of Information Technology Beijing University of Technology Beijing Laboratory of Smart Environmental Protection State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy

The difficult-to-measure parameters such as product quality and environmental protection indices of complex industrial processes mostly adopt the long-term and high-cost off-line detection method,which makes the modeling samples for building the risk warning model of these difficult-to-measure parameters extremely *** solve the above problems,a virtual sample generation method combining active learning(AL) mechanism and generative adversarial network(GAN) for process data is proposed and used for risk warning modeling of difficult-to-measure ***,the difficult-to-measure parameter risk level is added as condition information to GAN,so that the generator can generate virtual samples with specified risk ***,the virtual samples are screened by using the maximum mean difference(MMD) approach and the visual results are obtained by using principal component analysis(PCA) and t-distributed stochastic neighbor embedding(t-SNE).Finally,domain experts make active *** effectiveness and rationality of the proposed method are verified by using benchmark data sets.

关键词： generative adversarial network(GAN) active leaming(AL) complex industrial processes difficult-to-measure parameters virtual sample generation

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Simulation study on interaction coefficient of DEM in non-spherical large size (5-30 mm) coal particles

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Particuology 2020年第6期18卷 142-153页

作者： Daolong Yang Yanxiang Wang Jianping Li Qingkai Wang Yuntao Wang Yanting Yu School of Mechareonic Engineering Jiangsu Normal UniversityXuzhou 221116China State Key Laboratory of Process Automation in Mining&Metallurgy/Beijing Key Laboratory of Process Automation in Mining&Metallurgy Beijing 102600China School of Mechatronic Engineering China University of Mining and TechnologyXuzhou221116China

The pneumatic conveying system of coal particles can greatly reduce the dust and improve the environmental quality at underground mining workface and the surrounding of coal *** particle shape and the interaction coefficients between particles and the contact surface play important roles in the pneumatic conveying and CFD-DEM *** order to build the semblable shape models and obtain the accurate interaction coefficients of large coal particles,this article establishes the con tact model by the particle overlap method and describes the mathematical model of the shape characteristics for large coal *** particle models are simulated by adopting the multi-index mixed orthogonal *** accumulation density,the porosity and the error between simulation and experiment are taken as the indexes,and the particle models and the particle contact coefficients are taken as the orthogonal test *** a result,three more accurate particle models and their interaction coefficients are obtained,which provide the model basis for the pneumatic conveying of large coal particles.

关键词： Non-spherical particle DEM Particle shape Large size particle Orthogonal experiment

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Nonlinear Successive Compensation Method for Plant-Wide Production Index Based on Industrial Data

Nonlinear Successive Compensation Method for Plant-Wide Prod...

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第34届中国控制与决策会议

作者： Kang Li Guobin Zou Yinlian Duan Fuli Wang BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining and Metallurgy Research College of Information Science and Engineering Northeastern University

Due to the complexities of high-dimensionality,nonlinearity,many constraints,and process model mismatch,the plant-wide production index needs to be compensated when the production index is applied to the actual production *** paper proposes a nonlinear successive compensation method for the plant-wide production index based on industrial *** on the set point of the production index obtained from the plant-wide optimization,the method uses the large amount of industrial data stored in the industrial process to establish the relationship between production index compensation and the increase of economic benefit,optimizing the solution value of the production index compensation that maximizes the economic benefit *** iterative compensation,the method gradually improved the economic benefit of the production *** proposed method was applied to the hydrometallurgical production process of a *** results verified the effectiveness of the proposed method.

关键词： Hydrometallurgical Industrial data optimal compensate plant-wide production index

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Based on Clustering Algorithm Expert System of Self-learning Rule Base

Based on Clustering Algorithm Expert System of Self-learning...

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第34届中国控制与决策会议

作者： Cai Guoliang Wang Guicheng Guan Changliang Yin Fengfeng Zhu Jiale Zijin ZhiKong (Xiamen) Technology Co. Ltd. State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining& Metallurgy Shanghai Institute of Technology

Through data analysis method to obtain the internal connection of the data set,and extract different characteristics from it,and provide a strategy for the self-learning and acquisition of the expert system rule *** article analyzes the composition and operating principle of the expert system,and selects the clustering algorithm to design the process of self-learning rules,and then obtains the rule base of the expert system,trying to solve the problem of difficulty in obtaining the empirical reasoning rule base.A circuit fault diagnosis example is selected to verify the feasibility of the *** simulation tests,it is proved that the algorithm can realize self-learning rules,and an expert system rule base is initially formed,and fault information can be reasoned and diagnosed to simulate or replace empirical rules to a certain extent.

关键词： Self-learning K-means Clustering Algorithm Expert System Rule Base

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Experimental EEMD analysis of multisource and multicomponent mechanical signals for wet ball mill load

Experimental EEMD analysis of multisource and multicomponent...

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2023 International Workshop on Signal processing and Machine Learning, WSPML 2023

作者： Zhang, Jian Yu, Gang Tang, Jian Sun, Rongcheng School of Computer Science Nanjing University of Information Science and Technology Nanjing China State and Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing China BGRIMM Technology Group Co. Ltd. Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781510671928

During the process of mineral grinding, the ball mill generates the mechanical signals containing rich information due to different positions, which include mill loads and mill load parameters (MLPs). Actually, as one of the key factors, online MLPs detection is usually exploited to realize the intellectualization and improve the production efficiency of mineral processing plants. In this paper, on the basis of the ensemble empirical mode decomposition technique, the multisource and multicomponent mechanical signals of an experimental ball mill under changing ball, material, and water load conditions are analyzed to obtain different physical sub-signals via the intrinsic mode function. © 2023 SPIE. All rights reserved.

关键词： Empirical mode decomposition

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Relationship and contribution rate estimation analysis based on the EEMD for complicated mechanical signals

Relationship and contribution rate estimation analysis based...

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2023 International Workshop on Signal processing and Machine Learning, WSPML 2023

作者： Zhang, Jian Yu, Gang Tang, Jian Liu, Pengbo School of Computer Science Nanjing University of Information Science and Technology Nanjing China State and Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing China BGRIMM Technology Group Co. Ltd. Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781510671928

During the process of mineral grinding, mill load parameters (MLPs) determine the information of the mechanical signals. So, online MLPs detection is one of the key factors for improving the production efficiency of mineral processing plants. In this paper, the correlation between the multichannel mechanical signal and the different MLPs is explored by the power spectral density. Furthermore, the contribution rate of the multisource and multicomponent mechanical signals to the MLPs and mill load is measured on the basis of the correlation coefficient. Finally, a prediction model for MLPs can be constructed according to an adaptive decomposition strategy and the appropriate sub-signals. © 2023 SPIE. All rights reserved.

关键词： Empirical mode decomposition

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An online rock lithological detecting method using color and texture analysis

An online rock lithological detecting method using color and...

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Chinese Control and Decision Conference, CCDC

作者： Bo Lu Junwu Zhou Qingkai Wang Gang Yu Kang Li School of Information Science and Engineering Northeastern University BGRIMM Technology Group Beijing China BGRIMM Technology Group State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In the crushing and comminution process, different lithological rocks strongly affect the performance of crushing equipment. However, traditional lithological identification cannot be analyzed in a timely manner for processing control. Many rock lithological classification methods using color images have been proposed in the past, but few have successfully connected their results with online equipment monitoring. In this paper, we have developed an automatic vision system to evaluate rock lithology in real-time using color and texture features. The results show that the proposed method based on color and texture is robust and outperforms previous methods that rely on any single feature alone. This will help achieve reliable and precise control of the Semi-autogenous mill (SAG) feeding process.

关键词： Image color analysis Machine vision process control Color Rocks Real-time systems Minerals

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RFID-Based Indoor Human Behavior Recognition Using SATCN: A Self-Attention Enhanced Temporal Convolutional Network

RFID-Based Indoor Human Behavior Recognition Using SATCN: A ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhenchao Chen Mulan Yang Xuehan Hou Lvqing Yang Wensheng Dong Bo Yu Qingkai Wang School of Informatics Xiamen University Xiamen China School of Economics and Finance Xi’an Jiaotong University Xian China School of Software Xi’an Jiaotong University Xian China Zijin Zhixin(Xiamen)Technology Co. Ltd. Xiamen China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the field of indoor human behavior recognition, traditional deep learning methods often underperform in adequately capturing temporal features and core information in RFID behavior data, leading to suboptimal predictive performance. We note that human behavior is primarily composed of complex action sequences, where certain sequences formed by Received Signal Strength Indicator (RSSI) values may exhibit more representative and information-rich patterns. Therefore, we propose SATCN: an innovative model that leverages Temporal Convolutional Networks (TCN) and self-attention mechanisms for advanced RFID-based human behavior recognition. The SATCN model employs TCN to adeptly capture the inherent temporal dependencies within behavior data, while integrating self-attention mechanisms to focus on high-value features in the data. Experimental validation on RFID indoor human behavior recognition dataset reveals that SATCN achieves an accuracy of 94.51%, a 10.83 percentage point increase over existing models, highlighting SATCN’s potential in enhancing the accuracy of RFID-based indoor human behavior recognition.

关键词： Measurement Deep learning Accuracy Federated learning Convolution Data models Behavioral sciences

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