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Investigation of the effect of filling level on the wear and vibration of a SAG mill by DEM

Particuology 2022年第4期20卷 24-34页

作者： Changhua Xie Yuan Zhao Tao Song Yongzhi Zhao Institute of Process Equipment Zhejiang UniversityHangzhou 310027China State Key Laboratory of Process Automation in Mining & Metallurgy.Beijing 100160 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing 100160China

Semi-autogenous grinding(SAG)mill is widely used in the grinding process of coal and ores because of its strong applicability,large capacity and low consumption of grinding ***,the research of SAG mill is still of great important practical *** those parameters of the SAG mill,the flling level is very important for it is not only directly related to the processing capacity of the mill,but also affects the operation cost and stability of *** this paper,combined with shear impact energy model(SIEM)and discrete element method(DEM),the particle collision energy spectrum was applied to analyze the influence of ore flling level and media flling level on the power draw of the mill,the impact energy on ore,and the wear of liner and media in a slice of an industrial SAG *** was found that increasing the ore flling level would increase energy consumption and reduce the energy efficiency and the wear of liner and grinding ***,the vibration of mill was explored from the collision energy on *** results showed that the low frequency but high energy ollision between grinding media and liner was the main reason for liner wear and mill vibration.

关键词： Flling level SAG mill Collision energy DEM Wear Vibration

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A Two-stage Heterogeneous Ensemble Learning Algorithm Based on MLP-SVR-LGBM 5

A Two-stage Heterogeneous Ensemble Learning Algorithm Based ...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Yang, Chunpeng Zhang, Xurui Wang, Yiping Zhao, Jianjun Sun, Kai Automation in Mining & Metallurgy State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Beijing China School of Information and Automation Jinan China Automation in Mining & Metallurgy Beijing Key Laboratory of Process Beijing China

ISBN: (纸本)9798350325294

A single machine learning approaches often encounter numerous challenges and limitations when addressing complex problems. In this study, we propose a novel stacking ensemble learning model for heterogeneous soft sensors, utilizing multi-layer perceptron (MLP), support vector regression (SVR), and light gradient boosting machine (LGBM) as base learners, with gradient boosting decision tree (GBDT) as the meta-learner. First, the proposed heterogeneous soft sensor model fully considers the diversity of the base learners and their learning performance to effectively exploit the advantages of various machine learning methods. Second, the meta-learner, GBDT, is an integrated learning algorithm that can better handle different data of the base learners and improve the performance of the overall model. Subsequently, we demonstrate the superiority of the proposed method is confirmed through an artificial dataset case study. © 2023 IEEE.

关键词： Decision trees

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A Two-stage Sparse Optimization Algorithm for Gated Recurrent Units Estimators 5

A Two-stage Sparse Optimization Algorithm for Gated Recurren...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Zhang, Xurui Yang, Chunpeng Zhao, Lei Zhao, Jianjun Sun, Kai Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China School of Information and Automation Jinan China Automation in Mining & Metallurgy Beijing Key Laboratory of Process Beijing China

ISBN: (纸本)9798350325294

Industrial processes are always with sophisticated characteristics in particular highly nonlinear, multivariate coupling, and dynamic time lag. To overcome these dilemmas, a gated recurrent unit (GRU) neural network based on p - regularization is proposed to design effective prediction algorithms for the processes. Firstly, a primordial GRU model is trained from the given data. Secondly, a two-stage regularization algorithm is integrated into the fundamental model to simplify the input gate and the hidden layers of the GRU, respectively, which simultaneously achieves input variable selection and structural optimization. Finally, the performance of the proposed double p-regularized GRU is verified through a practical industrial dataset and compared with other machine learning algorithms. Experimental results demonstrate that the proposed algorithm can extract relevant features and simplify the model structure, therefore improving the prediction accuracy of the model. © 2023 IEEE.

关键词： Structural optimization

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RIS-assisted Channel Prediction Model for Mine V2X Systems 5

RIS-assisted Channel Prediction Model for Mine V2X Systems

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Cheng, Yuanxun Hu, Qingsong Luo, Yujia Zhang, Yuansheng China University of Mining and Technology School of Information and Control Engineering Xuzhou China Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China

ISBN: (纸本)9798331533991

The complex and dynamic environment of mines leads to time-variability and uncertainty in channel propagation, which affects the stability and reliability of Vehicle-to-Everything (V2X)communication in mine networks. Channel prediction can help mitigate these issues. Introducing reconfigurable intelligence surface (RIS) into the mine V2X system can enhance signal coverage and quality in the complex mine environment. To address the issue of long-term error accumulation in existing channel prediction models, this paper analyzes the prediction error of channel state information (CSI) in mine V2X systems. It was observed that the error distribution exhibits characteristics of an approximate normal distribution, revealing its predictability. Utilizing this characteristic, this paper proposes an error correction strategy to suppress long-term error accumulation during the prediction process. Furthermore, an adaptive method for optimizing RIS phases is designed based on the Snow Ablation Optimizer. Specifically, this paper utilizes a metaheuristic optimization algorithm and deep learning techniques to propose a mine V2X channel prediction model that incorporates the error correction strategy. Experimental results show that optimizing the RIS phase can effectively enhance channel gain, and the mine V2X channel prediction model incorporating the error correction strategy can effectively suppress cumulative errors during the prediction process, thereby improving prediction accuracy. © 2024 IEEE.

关键词： Normal distribution

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RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios 5

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLO...

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

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

ISBN: (纸本)9798331533991

With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz are easily obstructed, making traditional LOS-based methods ineffective in complex environments. To overcome this, NLOS localization methods and Reconfigurable Intelligent Surfaces offer promising solutions by dynamically modifying the wireless environment. This paper introduces a Direct Position Determination method that integrates Deep Neural Networks (DNN) with the Sparrow Search Algorithm (SSA) to tackle the challenges of high computational complexity and low localization accuracy in RIS-aided systems. An NLOS scenario with RIS is established, and a far-field channel model is derived. The method uses DNN to model the nonlinear relationship between the likelihood function and user position, and then employs SSA to optimize the DNN-based fitness function, efficiently solving the objective function. Experimental results show that the DNN-SSA method consistently achieves high localization accuracy across different scenarios, even at low SNR levels, demonstrating its effectiveness and potential for 6G high-precision localization. © 2024 IEEE.

关键词： Deep neural networks

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Semantic Segmentation Research of Motion Blurred Images by Event Camera 7

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

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7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

作者： Liu, Xiangyong Yin, Fengfeng Jiang, Wei Fan, Siwen College of Automotive Studies Tongji University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Department of Electromechanical Engineering Shanghai China State Key Laboratory of Process Automation in Mining & Metallurgy 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 Shanghai China

ISBN: (纸本)9798350340488

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. © 2023 IEEE.

关键词： blurred images double channels' network dynamic visual sensors feature fusion semantic segmentation

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Optimal Control of Flotation Industrial process Using Model-based Reinforcement Learning 4

Optimal Control of Flotation Industrial Process Using Model-...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Jia, Runda Chen, Xuli Zheng, Jun Yu, Gangs College of Information Science and Engineering Northeastern University Liaoning Shenyang China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this paper, the optimal control of the flotation industrial process (FIP) is studied. The flotation process uses differences in the physical and chemical properties of mineral surfaces to selectively attach minerals to air bubbles, and separate useful from useless minerals. To optimize control of the process, we use the model-based reinforcement learning (MBRL) method to design the optimal controller for the flotation process. A case study on the flotation mechanism model verifies the efficiency of the proposed method. The results show that the MBRL method can learn the optimal control policy with fewer episodes. © 2022 IEEE.

关键词： Flotation

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Data-Driven Tube-based Model Predictive Control of an Industrial Thickener 4

Data-Driven Tube-based Model Predictive Control of an Indust...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Jia, Runda Zhang, Shulei Li, Zhiqi Li, Kang College of Information Science and Engineering Northeastern University Liaoning Shenyang China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this work, a data-driven tube-based model predictive control (MPC) is presented to track the setpoints of the underflow concentration. By defining the terminal admissible set to consider all the possible steady-states, the controller can ensure tracking for all reachable operating setpoints. Besides, a data-driven general polyhedral uncertainty set is constructed by employing the principal component analysis (PCA) technique, which can effectively capture correlations among uncertain variables. Based on the constructed uncertainty set, the feasible region could be enlarged while reducing the conservatism of control performance. In addition, recursive feasibility and stability of the controller can be guaranteed. The effectiveness of the proposed method is verified by tracking problems of the thickening process. © 2022 IEEE.

关键词： Model predictive control

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DeepMultiple: A Deep Learning Model for RFID-based Multi-object Activity Recognition 35

DeepMultiple: A Deep Learning Model for RFID-based Multi-obj...

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Shen, Shunwen Yang, Lvqing Chen, Sien Dong, Wensheng Yu, Bo Wang, Qingkai School of Informatics Xiamen University Xiamen China School of Navigation Jimei University Xiamen China School of Management Xiamen University Xiamen China 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

Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years. At present, the research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method. Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on single-object activity recognition, which is not suitable for multi-object scenarios. To address these issues, we propose DeepMultiple, a novel tag non-attachment activity recognition model for multi-object. The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally ProbSparse is used to optimize the vanilla Transformer-based Encoder to enhance the activity recognition ability. We deployed a single reader and antenna for multi-object activity tracking during the experiments to reduce deployment difficulties. Extensive experimental results show that DeepMultiple can recognize ten types of multi-object activities with 98.12% precision under different challenging settings, which has excellent performance compared with several state-of-the-art methods. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Antennas

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Bearing Remaining Life Prediction Method Based on Wavelet Transform Denoising and Feature Preservation CycleGAN under Unbalanced Samples 3

Bearing Remaining Life Prediction Method Based on Wavelet Tr...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Wang, Songyang Yu, Jun Zhao, Kun Guo, Zhenyu School of Automation Harbin University of Science and Technology Harbin China School of Automation State Key Laboratory of Process Automation in Mining & Metallurgy Harbin University of Science and Technology Harbin China Cixing Group Co Ltd Beijing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9781665492812

With the rapid development of industrialization today, mechanical equipment has undergone significant development and progress in terms of type and quality. Rolling bearings are the key components of mechanical transmission and the most vulnerable mechanical components. Only 10% of rolling bearings can operate as designed. Exploring more scientific and reasonable test methods to predict the remaining service life of rolling bearings can not only reduce operating costs, but also prevent accidents. Life prediction models can be divided into three categories: statistical models, dynamic models, and data-driven prediction models. The existing life prediction models based on statistics, dynamics, etc. continue to increase the workload and difficulty of life prediction in the case of increasingly complex, time-consuming and labor-intensive mechanical equipment. With the rapid development of information technology, industrial process data collection has become efficient and convenient, providing sufficient data resources for data-driven intelligent life prediction technology, and gradually showing great development potential. The remaining life prediction mainly includes two key parts: the feature extraction of bearing vibration data and the construction of life prediction model. Therefore, this paper takes the rolling bearing as the research object, and carries out the remaining life prediction research from the two directions of feature extraction and prediction model construction. In the bearing operation monitoring data, the proportion of the fault data relative to the normal operation data is very small, forming a typical data imbalance phenomenon, and the traditional life prediction is not ideal when dealing with such data. In this paper, a secondary filtering method combining wavelet transform and parameter adaptive variational modal decomposition is used to preprocess the data and enhance the features;and fully consider the influence of unbalanced cluster data, a l

关键词： deep neural network remaining useful life prediction rolling bearing

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