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Rapid prediction of flow and concentration fields in solid-liquid suspensions of slurry electrolysis tanks

International Journal of Minerals,metallurgy and Materials 2024年第9期31卷 2006-2016页

作者： Tingting Lu Kang Li Hongliang Zhao Wei Wang Zhenhao Zhou Xiaoyi Cai Fengqin Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process Beijing 102628China School of Materials Science and Engineering Lanzhou University of TechnologyLanzhou 730050China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China

Slurry electrolysis(SE),as a hydrometallurgical process,has the characteristic of a multitank series connection,which leads to various stirring conditions and a complex solid suspension *** computational fluid dynamics(CFD),which requires high computing resources,and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE *** scientific selection of calculation samples via orthogonal experiments,a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each ***,a prediction model of the SE tank was constructed using the K-nearest neighbor *** results show that with the increase in levels of orthogonal experiments,the prediction accuracy of the model improved *** model established with four factors and nine levels can accurately predict the flow and concentration fields,and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937,*** with traditional CFD,the response time of field information prediction in this model was reduced from 75 h to 20 s,which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements.

关键词： slurry electrolysis solid-liquid suspension computational fluid dynamics K-nearest neighbor algorithm rapid prediction

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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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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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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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The Prediction Model of SAG Mill Load Based on Industrial Big-data mining

The Prediction Model of SAG Mill Load Based on Industrial Bi...

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第43届中国控制会议

作者： Guicheng Wang Changyu Wang Zhicheng Bai Changliang Guan Min Zhang Xiaobin Li School of Electrical and Electronics Engineering Shanghai Institute of Technology State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process and Beijing Key Laboratory of Process Automation in Mining & Metallurgy

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Regarding the modeling problem of grinding process in mining,taking the operating load of semi-automatic grinding mill as the research object,starting from industrial big data analysis,using data mining methods to establish decision tree models and random forest models,and evaluating and predicting the constructed *** testing,decision tree models with different effects can be obtained,indicating that the decision tree model has a certain degree of flexibility,and selecting parameters can lead to a model with good *** addition,under the same conditions,a random forest model was constructed,and simulation results showed that the combination model constructed using the random forest algorithm was superior to a single decision tree model,with better prediction performance.

关键词： SAG mill data mining big-data analysis decision tree random forest

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key Technologies for intelligent mining of Underground Metal Mines

MetaResource

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MetaResource 2025年第1期1卷 13-22页

作者： Kai Zhan Yuansheng Zhang Zhaoyang Ma Xu Liu Xiao Lv Guanzhou Liu Huxin Wang Xincheng Liang BGRIMM Technology Group Beijing China BGRIMM Intelligent Technology Co. Ltd Beijing China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China

Currently, global mining development has entered a new historical stage. With the gradual depletion of shallow mineral resources and the increasing proven reserves of deep resources, deep resource exploitation has become a global mining trend. However, deep resource development faces numerous challenges, including product pricing, production costs, operational safety, and workforce issues. The development of intelligent and even autonomous mining technologies, along with the establishment of smart mines, has become an inevitable choice for deep resource exploitation. A key technology and complete technical system for underground metal mine intelligent mining is constructed around the mining production process, with “ore flow” as the main thread. With “equipment intelligence”, “precise positioning”, “real-time dispatching”, “high-speed communication”, and “continuous mining” as the main themes, remote, tele-controlled, and intelligent mine production management is being realized. The achievements are demonstrated in engineering applications at multiple mining enterprises, providing theoretical foundations and technical support for the intelligent, efficient, and green development of deep mineral resources.

关键词： Wireless communication Production management Scalability Metals Pricing Real-time systems Dispatching Safety Data mining Mineral resources

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Multi-condition flotation process condition recognition method based on deep learning and multivariate data pre-processing

Multi-condition flotation process condition recognition meth...

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Chinese Control and Decision Conference, CCDC

作者： Yicheng Wang Shu Wang Kang Li College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The state and trend of flotation surface foam play a crucial role in flotation operations as they provide important indications of working conditions, index predictions, and operational volume adjustments. While researchers have focused on manual feature design of froth images or automatic extraction of depth features and their relationship to flotation conditions, the information contained in the flotation process variables and the importance of data pre-processing are usually overlooked. To address these shortcomings, this study proposes an integrated framework for flotation process data preprocessing and condition recognition. It includes a fast imputation algorithm based on multi-condition data center of mass (FCKNN) for overcoming the challenges of imputing missing values in on-line industrial processes, as well as a high-precision work condition recognition method that fuses deep visual features with key process variables. Finally, the framework was applied to the gold flotation process. The experimental results show that the proposed framework can perform work condition recognition with high accuracy and solve the challenge of online missing value imputation.

关键词： Employee welfare Visualization Image recognition Accuracy process control Manuals Feature extraction

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RIS-assisted Channel Prediction Model for Mine V2X Systems

RIS-assisted Channel Prediction Model for Mine V2X Systems

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

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

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

关键词： Deep learning Adaptation models Accuracy Predictive models Reconfigurable intelligent surfaces Gaussian distribution Prediction algorithms Approximation algorithms Error correction Vehicle-to-everything

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

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

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yujia Luo Qingsong Hu Yuanxun Cheng Shuya Yuan Yuansheng Zhang School of Information and Control Engineering China University of Mining and Technology 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: (数字)9798331533991

ISBN: (纸本)9798331534004

关键词： Location awareness 6G mobile communication Wireless communication Accuracy Simulation Search methods Artificial neural networks Reconfigurable intelligent surfaces Nonlinear optics Signal to noise ratio

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