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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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Soft-sensing Prediction of mining SAG Mill Load

Soft-sensing Prediction of Mining SAG Mill Load

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第34届中国控制与决策会议

作者： Wang Guicheng Zheng Yin Cai Guoliang Zhang Min Yin Fengfeng Han Changxi Li Xiaobin Shanghai Institute of Technology State Key Laboratory of Process Automation in Mining&Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy

Taking the semi-autogenous mill,a commonly used equipment in the grinding process,as the research object,the problem of continuous and stable operation of the production process in the best working conditions is discussed to improve the utilization rate of resource and achieve the purpose of coordinating energy saving and production *** main reason why the semi-autogenous mill cannot achieve economical operation is that its load is difficult to realize on-line *** order to ensure the continuous operation of grinding production and prevent the occurrence of empty grinding and full grinding,the staff can only conservatively feed the mine through experience,resulting in unbalanced operating *** proposed PSPCAW-SVM composite soft-sensing algorithm is selected,and the soft-sensing model of the semi-autogenous mill load is established according to the sample data of the process,which is used for the online monitoring of the mill load,and the accuracy and stability of model is evaluated through the calculation of simulation prediction results and performance indicators.

关键词： Semi-autogenous Mill Load Monitoring Soft-sensing Model SVM Wavelet Denoising

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Soft-sensing Model of SAG Mill Based on PSPCAW-SVM

Soft-sensing Model of SAG Mill Based on PSPCAW-SVM

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第34届中国控制与决策会议

作者： Wang Guicheng Yang Haowen Yang Hongzhe Yin Fengfeng Cai Guoliang Han Changxi Shanghai Institute of Technology State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining& Metallurgy

By analyzing the soft-sensing modeling process of SAG based on support vector machine,the problem mainly involves the selection of auxiliary variables and data processing in soft-sensing *** this paper,the correlation coefficient method is used to analyze the correlation between the dominant variable and the auxiliary variable,and the auxiliary variable with high correlation is *** phase space of the signal related to the load of SAG is reconstructed,and the selection dimension of the auxiliary variables is preliminarily determined,and the principal component analysis technology is used to fuse some auxiliary variables to prevent information *** it combines the three techniques to construct an improved PSPCA algorithm for screening auxiliary variables,and determines the auxiliary variables of the soft-sensing model,and wavelet denoising filtering is performed on the data sample of the determined auxiliary variables to remove the noise in the original ***,PSPCAW-SVM softsensing model is established to lay the foundation for further research.

关键词： Soft-sensing Technology SVM Wavelet Denoising PCA

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A Deep Nonconnectionist Learning Framework for Industrial Data Modeling

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 105-113页

作者： Chen, Yongxuan Wang, Dianhui School of Low-Carbon Energy and Power Engineering Xuzhou221116 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Despite the extensive applications of deep neural networks in data modeling filed, there are still some obvious deficiencies for the implementation in modern industrial cases. There are mainly reflected in the following aspects: first, the architectures are difficult to configure;second, the modeling process is time-consuming;third, the training procedure easily falls into the local optimum situation. To overcome these problems, exploring the nonconnectionist learning model has become a popular topic recently. This article proposes a deep nonconnectionist learning model based on kernel principal component regression (KPCR), which is referred to as stacked KPCR (SKPCR). By stacking multiple KPCR modules, a multilayer learning model is constructed by adopting hierarchical feature extraction. In SKPCR, the model structure is determined incrementally and there is only one parameter needed to be configured for each layer. Furthermore, an enhanced learning strategy is designed for alleviating the information loss problem in the training process. An actual industrial case is used to validate the effectiveness, including the prediction performance and modeling efficiency, of our proposed method. © 2024 IEEE.

关键词： Deep neural networks

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Adaptive Dynamic Programming and Decentralized Optimal Output Regulation of Two-Time-Scale Interconnected Systems 12

Adaptive Dynamic Programming and Decentralized Optimal Outpu...

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12th IEEE International Conference on Control, automation and Information Sciences, ICCAIS 2023

作者： Zhao, Jianguo Yang, Chunyu Zhou, Linna Gao, Weinan China University of Mining and Technology School of Information and Control Engineering Xuzhou China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China

ISBN: (纸本)9798350328783

In this paper, we address the decentralized optimal output regulation problem for two-time-scale (TTS) interconnected systems with unknown slow dynamics through adaptive dynamic programming (ADP). Firstly, singular perturbation theory is leveraged to decompose the TTS interconnected system into a slow subsystem and several interconnected fast time-scale subsystems. Secondly, we develop a decentralized optimal control scheme for the fast time-scale subsystems with interconnections. Also, we devise an off-policy ADP algorithm to learn the optimal control strategy for the slow time-scale subsystem with unknown dynamics. Moreover, we combine the slow and fast control strategies to design the composite decentralized optimal output regulator. Both optimality and stability of the closed-loop system are rigorously analyzed. Finally, simulation results are provided to validate the effectiveness of the proposed methodology. © 2023 IEEE.

关键词： Dynamic programming

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Semantic Segmentation Research of Motion Blurred Images by Event Camera

Semantic Segmentation Research of Motion Blurred Images by E...

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Conference on Vehicle Control and Intelligence (CVCI)

作者： Xiangyong Liu Fengfeng Yin Wei Jiang Siwen Fan College of Automotive Studies Tongji University Shanghai China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Shanghai China Department of Electromechanical Engineering University of Macau Shanghai China State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Beijing China Linyi Engineering Branch China Railway Jinan Group Co Ltd Linyi China School of Mechatronical Engineering: Name of Organization Shanghai China

The semantic segmentation of road objects is a prerequisite for autonomous driving. Rapid or bumpy movement of vehicles can lead to the images to be blurred, which reduces the safety of autonomous driving. This study combines the dynamic visual sensor (DVS) and RGB camera to improve the detection accuracy of road semantic segmentation. Firstly, based on DVS and RGB images, we develop two datasets of blurred images, including the stone and run datasets. Then, a double channels’ detection network is designed with several Swin-transformer modules, and the two channels are designed to extract features from DVS and RGB images, respectively. In addition, a feature fusion module is designed to extract attention from DVS images. Finally, experiments demonstrate that our proposed double-channels’ fusion method achieves the state-of-the-art segmentation performance for blurred images.

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A Multiple-size-group CFD Model for an Internal Loop Airlift Reactor with Bubble Recirculation Regimes

MetaResource

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MetaResource 2025年第1期2卷 63-81页

作者： Tao Song Yuqing Feng Qingkai Wang Junwu Zhou Zhengchang Shen State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process BGRIMM Technology Group Beijing China CSIRO Mineral Resources Victoria Australia

Airlift reactors are used in a wide range of industries, such as hydrometallurgy, biochemical processes, chemical process industry and wastewater treatment. Despite the simple structure of airlift reactors, the flow field becomes complex with increasing gas velocity, and gas bubbles in the circulating regime can be observed in practice. In this paper, a numerical modelling method based on computational fluid dynamics (CFD) is presented for gas-liquid flow in airlift reactors under different bubble recirculation regimes. Gas-liquid flow was modelled using the Eulerian two-fluid equations, and extra user defined subroutines were incorporated to consider the complex physics, such as bubble-induced turbulence and turbulent dispersion force. Some alternative correlations for drag coefficient were tested to compare their ability to capture the bubble distributions in the riser and downcomer of the airlift reactors, with consideration of the interaction between bubbles. A model including multiple bubble sizes was applied to obtain more accurate simulation results of gas holdup and water velocity. Also, the use of the inhomogeneous multiple-size-group (MUSIG) model was explored as a way to better predict the complex flow regimes. The modelling method was applied to a laboratory internal loop airlift reactor, and the simulation results were compared with the published experimental measurements for gas holdup and water velocity. Reasonable agreement was obtained over a range of operating conditions, and an improvement was demonstrated using the proposed method. The simulations have shown that the inhomogeneous MUSIG model is a suitable tool to describe the complex gas-liquid interaction in the airlift reactor at a high gas superficial velocity.

关键词： Atmospheric modeling Simulation Computational fluid dynamics Force Predictive models Nonhomogeneous media Mathematical models Multiple signal classification Velocity measurement Inductors

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PHM of Roller Tyre in Nonferrous Metal Crushing process Based on DUAE

PHM of Roller Tyre in Nonferrous Metal Crushing Process Base...

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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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A EWC-Based Online-Learning Algorithm for Ordinary Differential Equation Network

A EWC-Based Online-Learning Algorithm for Ordinary Different...

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Asian Control Conference

作者： Yiping Wang Dasong Wang Minghao Li Kai Sun School of Information and Automation Qilu University of Technology (Shandong Acadamy of Sciences) Jinan China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In industrial processes, changes in operational conditions, including variations in raw materials, lead to concept drift in process data, thereby affecting the performance of soft sensor models trained offline. To address this issue, this study introduces an online-learning method for soft sensor modeling, by utilizing a Lipschitz ordinary differential equation recurrent neural network. This method combines two fundamental components: threshold detection and online incremental fine-tuning. Firstly, the base model is established through offline training using the initial dataset, designed for predicting data within a sliding window. Then, the method adaptively adjusts the threshold or initiates online model updates by comparing the prediction error against a preset threshold. This process not only improves the efficiency of online updates but also minimizes unnecessary adjustments. The online update phase incorporates an incremental fine-tuning approach, integrated with elastic weight consolidation technology, to boost the model adaptability and mitigate catastrophic forgetting during updates, thereby enhancing the efficiency of the model updating. Finally, the performance of the developed approach is validated through simulations on both an artificial dataset and a real-world industrial dataset, comparing it with other online-learning algorithms.

关键词： Training Adaptation models Soft sensors Concept drift Time series analysis Predictive models Ordinary differential equations

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