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MetaResource 2025年第1期2卷 11-32页

作者： Ying Tian Xingyan Guo Qiang Zhang Zhengchang Shen Xiangwei Dong Chunzhi Li Kun Cao College of Mechanical and Electronic Engineering Shandong University of Science and Technology Qingdao China BGRIMM TECHNOLOGY GROUP Beijing China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Zhongtianhechuang Energy Co. Ltd. Ordos China

Smoothed particle hydrodynamics (SPH) is a Lagrangian, mesh-free numerical method renowned for its ability to handle fluid dynamics problems with large interface deformations and multiphase flow coupling. This study introduces an SPH-based multiphase flow model for simulating bubbly flows involving various immiscible fluids. The model uses a volume-based density discretization equation, ensuring numerical accuracy near interfaces, independent of density ratios, thus maintaining accuracy and stability even at high density ratios. By integrating the Continuous Surface Force method for surface tension into the multiphase SPH model, the study simulates interfacial behavior between phases. The model accurately predicts Laplace pressure differences across interfaces, maintaining interface stability at density ratios up to 100.0. Simulations of single and double bubble ascents elucidate the influence of the Bond number on bubble shape, rising distance, and velocity. As the Bond number increases, bubbles flatten and develop tails, affecting their integrity. The study also simulates multiple bubbles ascents in water, showcasing the model's ability to capture complex interfacial behaviors in bubbly flows, including deformation, adsorption, coalescence, and tearing.

关键词： Deformable models Accuracy Deformation Force Hydrodynamics Mathematical models Stability analysis Surface tension Numerical models Numerical stability

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Developing Deep Learning Based Intelligent Perception Algorithm for Thickener Equipment Monitoring 8

Developing Deep Learning Based Intelligent Perception Algori...

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8th International Joint Conference on Rules and Reasoning, RuleML+RR-Companion 2024

作者： Liu, Zuochen Wang, Qingkai Dang, Jiandong Wang, Xiaochun Li, Kang Avogadro, Roberto Roman, Dumitru Ma, Xiang State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing100160 China Beijing100160 China Mining Co. Ltd Aahui Hefei231500 China SINTEF AS POB 124 Blindern Oslo0314 Norway

Dense dehydration by use of thickener equipment is a critical process in mineral processing, directly impacting the quality of concentrate products. However, current industrial practices lack effective methods for monitoring the condition of thickeners. This makes it difficult for operators to accurately perceive the internal state of the thickener, leading to a relatively inefficient production process. This paper addresses the challenge of real-time detection of one of the key process parameters, feed rate, in thickener by employing a data-driven model-ling approach with intelligent perception. The upstream processes are analyzed, and relevant variables are selected for modeling. The Long Short-Term Memory (LSTM) - Recurrent Neural Network (RNN) and the Gated Recurrent Unit (GRU) - RNN with the Particle Swarm Optimization (PSO) algorithm are used to build and train the models. The results show that the PSO-GRU-based intelligent perception model for thickener feed rate estimation achieves higher accuracy and shorter training times compared with the LSTM-RNN model. © 2024 Copyright for this paper by its authors.

关键词： Long short-term memory

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Trend-Aware Patch Feature Fusion Transformer for Fault Diagnosis of Vibration Signals

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IEEE Sensors Journal 2025年

作者： Zhang, Ruicong Chu, Fei Qiu, Yazhi He, Dakuo Wang, Fuli China University of Mining and Technology School of Information and Control Engineering Xuzhou221000 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110000 China

The Vision Transformer (ViT) has demonstrated reliable performance in industrial equipment fault diagnosis by capturing global features and long-range dependencies in vibration signals. Nonetheless, when processing vibration signals, ViT primarily focuses on the correlations between patches, while neglecting the trend variation information hidden between signal units. This limitation hinders the model’s ability to extract latent fault information. To address this issue, we propose a trend-aware patch feature fusion Transformer (TAPformer) for fault diagnosis of vibration signals. Specifically, we design both patch-level and signal-level trend-aware self-attention blocks and adopt a weighted approach to integrate coarse-grained patch-level trend features with fine-grained signal-level trend features at the output of each self-attention head. This approach enables the model to capture fault information hidden within trend variations, effectively enhancing its fault detection capabilities. TAPformer establishes optimal weighted fusion hyperparameters through ablation experiments and is tested on four public datasets. Compared to eight advanced models, TAPformer achieves the highest accuracy across all four datasets, demonstrating the effectiveness and generalization capability of the proposed method. © 2001-2012 IEEE.

关键词： Fault detection

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Intelligent expert control method and application in SABC grinding circuit based on hybrid model

Intelligent expert control method and application in SABC gr...

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Chinese Control and Decision Conference, CCDC

作者： Guobin Zou Junwu Zhou Daoxi Liu Tao Song Kang Li College of Information Science and Engineering Northeastern University State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China

SABC grinding circuit (Semi Autogenous + Ball mill + Crushing circuit) is an important typical configuration in comminution production, which has control problems such as uncertainty and strong coupling. In this paper, an intelligent expert control method for grinding process based on hybrid model is proposed. First, a hybrid model driven by mechanism and data is proposed to realize online perception of production indicators such as particle size in cyclone overflow; then, the application scope of expert rules is improved by intelligent correction of uncertainties; finally, the process mechanism knowledge and the online perception data of the hybrid model are integrated to realize the coordinated production of each sub process in the target SABC circuit. The method proposed in this paper has been applied in an intelligent expert control system of SABC circuit in a concentrator to meet the production target.

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Non-Line-Of-Sight Target Localization in T-Shaped Roadways Using Simo Fmcw Millimeter-Wave Radar

SSRN

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SSRN 2024年

作者： Hu, Qingsong Luo, Yujia Wang, Liudi Cheng, Yuanxun Zhang, Yuansheng Yuan, Shuya Kou, Zhihao School of Information and Control Engineering China University of Mining & Technology Jiangsu Xuzhou221116 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing100032 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100032 China

The hazardous and complex nature of underground mining imposes stringent safety requirements on autonomous driving. T-shaped roadways, common in such environments, present significant challenges for non-line-of-sight (NLOS) target localization. Traditional underground positioning technologies often fail in these settings due to severe signal attenuation and multipath effects, leading to potential accidents. This manuscript proposes a novel localization algorithm using single-input-multi-output (SIMO) Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar to address these challenges. We analyze millimeter wave multipath propagation in T-shaped tunnels and construct an FMCW multipath echo model. Using CA-CAFAR and MVDR algorithms, we accurately estimate target distance and angle information. Additionally, a theoretical false point matching elimination method is designed to improve positioning accuracy by mitigating false target interference. Field tests in typical T-shaped NLOS scenarios demonstrate the method's high-precision positioning, enhancing the safety and efficiency of autonomous vehicles in coal mines. © 2024, The Authors. All rights reserved.

关键词： Coal mines

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Inspection trolley based on the distance detection obstacle

Inspection trolley based on the distance detection obstacle

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第35届中国控制与决策会议

作者： Guicheng Wang Ao Sheng Xiaobin Li Min Zhang Changliang Guan Fengfeng Yin School of Electrical and Electronics Engineering Shanghai Institute of Technology Library Shanghai Institute of Technology State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy

In Industry 4.0 background,unmanned fullprocess automated production has gradually become a hot topic,and the realization of intelligent and information factory requires the joint cooperation of multiple industries,in which the inspection device that replaces traditional manual work is an inescapable *** present,domestic and foreign research on intelligent inspection trolley is focused on obstacle avoidance function based on various *** this context,it is introduced an algorithm based on monocular camera as a sensor,using Python language to access OpenCV library,and utilizing Gaussian filter function and Canny operator-based image contour extraction to complete the distance *** algorithm can achieve the distance measurement and obstacle avoidance function at a lower *** test results show that the system can automatically identify the distance of obstacles in the inspection process,and according to the visual information captured by the camera in real time,it can independently generate steering action and collision reminder,and can complete the preliminary requirement of intelligent inspection.

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Intelligent Operational Adjustment of Copper Flotation process Based on CBR-GRNN

Intelligent Operational Adjustment of Copper Flotation Proce...

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Chinese Control and Decision Conference, CCDC

作者： Hao Yan Xinchun Jia Zhenji Feng Tao Song Fuli Wang School of Automation and Software Engineering Shanxi University Taiyuan China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The mechanism of the copper flotation process is complex and highly uncertain, making it difficult to establish a precise mechanism model. For a long time, the operational adjustment of the flotation process has mainly relied on manual labor, which makes it difficult to ensure mineral quality and economic benefits for the enterprise. In order to achieve automation of the flotation process operation, this article proposes an intelligent operational adjustment method based on case-based reasoning and the generalized regression neural network (CBR-GRNN). In the offline stage, the working conditions are divided based on PCA and K-means method, and the case bases are established for each type of different working conditions. In the online stage, we first determine the category of the target case and search in the corresponding case base to obtain the most similar operational adjustment solution. Due to the fact that the most similar case may not fully match the target case, further compensation is necessary. Therefore, this paper proposes a new difference-driven case revision scheme using GRNN. The final solution for reuse is the operational adjustment value compensated by GRNN. Finally, the experimental results in an actual copper flotation process have demonstrated that the proposed method can effectively perform operational adjustment. Furthermore, the superiority of the proposed method is further demonstrated by comparing it with the other two techniques.

关键词： Employee welfare Economics automation Uncertainty Neural networks process control Minerals

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Intelligent expert control method and application in SABC grinding circuit based on hybrid model

Intelligent expert control method and application in SABC gr...

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第35届中国控制与决策会议

作者： Guobin Zou Junwu Zhou Daoxi Liu Tao Song Kang Li College of Information Science and Engineering Northeastern University State Key Laboratory of Process Automation in Mining and Metallurgy BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy

SABC grinding circuit(Semi Autogenous+Ball mill+Crushing circuit) is an important typical configuration in comminution production,which has control problems such as uncertainty and strong *** this paper,an intelligent expert control method for grinding process based on hybrid model is ***,a hybrid model driven by mechanism and data is proposed to realize online perception of production indicators such as particle size in cyclone overflow;then,the application scope of expert rules is improved by intelligent correction of uncertainties;finally,the process mechanism knowledge and the online perception data of the hybrid model are integrated to realize the coordinated production of each sub process in the target SABC *** method proposed in this paper has been applied in an intelligent expert control system of SABC circuit in a concentrator to meet the production target.

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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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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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