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第32届中国控制与决策会议

作者： Hao Yan Jin Zhu Fuli Wang Dakuo He Qingkai Wang College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University State Key Laboratory of Process Automation in Mining and Metallurgy

Due to the lack of detection instruments or long measurement cycles in the industrial flotation process,accurate and real-time estimation of the technical index is of great significance for optimizing flotation performance and operational *** the real-world flotation process,incomplete data is a widespread phenomenon owing to hardware sensor failures and other *** this end,this paper proposes a Bayesian network（BN）-based concentrate grade estimation method under incomplete *** real-time froth image information and the concentrate grades of the previous periods are taken as the input of the BN model,and the current concentrate grade is the output of the BN *** expectation maximum（EM） algorithm is used to estimate the model *** application results show the proposed method can accurately estimate the concentrate grade even if some data are missing.

关键词： Industrial flotation process Bayesian network Incomplete data Concentrate grade Estimation

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Real-Time Control Algorithm of Intelligent Energy-Saving Lights based on IoT

Real-Time Control Algorithm of Intelligent Energy-Saving Lig...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Bo Su Zeyuan Zhang Yuansheng Zhang Qingyue Yang Jiong Jiang School of Aerospace Science and Technology of Xidian University Xi'an Shaanxi China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Aerospace Information research Institute of Chinese Academy of Sciences Beijing China Shaanxi Land Engineering Construction Group Co. Ltd. Xi'an Shaanxi China

The world is currently facing a very serious energy crisis, how to save energy and improve energy utilization is an important issue to all countries. Lighting power consumption occupies a large proportion of people's electricity consumption, and most of the existing lighting control strategies are based on the activities of people or time scenes, ignoring the influence of natural light. In this paper, in order to make full use of natural light, we propose a real-time control algorithm of intelligent energy-saving lights. When natural light exists, the strategy calculates the level of light brightness at that moment through an algorithm and adjusts the brightness level of luminaire. This method saves energy by reducing the luminaire brightness level while meeting people's needs for work surface illumination. The simulation results indicated that our real-time lighting control algorithm has better energy saving effect compared with the traditional lighting control strategy.

关键词： Power demand Simulation Brightness Software algorithms Lighting Software quality Real-time systems

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Research and Application of Nelson Concentrator in Beneficiation Test of Quartz Vein Type Gold Ore

Research and Application of Nelson Concentrator in Beneficia...

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作者： Wenping Zhang Mingming Cai Guangsheng Li Fabo Huang Xingfu Zhu Chao Xu Jianjun Zhao Department Metallurgical Laboratory Branch of Shandong Gold Mining Technology Co.Ltd Department State Key Laboratory of Process Automation in Mining & Metallurgy

A gold deposit in Shandong belongs to Quartz vein type gold-bearing deposit,the main gold minerals are natural gold and silver-gold minerals,there are a few metallic minerals such as pyrite,the main gangue minerals are quartz,feldspar,sericite and so on,the average size of gold minerals is 62.37 microns,the particle size of the cloth is *** a kind of gravity separation equipment,Nelson concentrator is more and more favored by mines because of its high enrichment ratio,high recovery rate and extremely friendly to environment,the experimental research of Nelson concentrator was carried out,and the gold concentrate with gold grade of 385.06 G/T and recovery of 62.21% was obtained.

关键词： Research and Application of Nelson Concentrator in Beneficiation Test of Quartz Vein Type Gold Ore Quartz

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Operating performance assessment of industrial process based on MIC-graph convolutional networks with local slow feature analysis

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Neural Networks 2025年 191卷

作者： Lili Hao Fei Chu Tao Chen Mingxing Jia Fuli Wang Research Center of Underground space Intelligent Control Engineering of the Ministry of Education School of information and Control Engineering China University of Mining and Technology Xuzhou 221116 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing 100160 China School of Chemistry and Chemical Engineering University of Surrey Guildford GU2 7XH UK College of Information Science and Engineering Northeastern University Shenyang 110819 China

To ensure the safe and stable operation of industrial processes, deep neural network-based operational performance assessment methods have been extensively adopted according to the latest research findings. However, existing industrial process performance assessment models often fail to account for the local spatial structure features and the slowly varying features from time series samples. Such limitations result in the suboptimal exploitation of spatial interaction information and hinder the models’ responsiveness to complex system state transitions, thereby impeding the precise assessment of industrial process performance. To this end, a maximum information coefficient-based graph convolutional networks (MIC-GCN) is proposed for operational performance assessment, which aims to effectively capture the intricate interactions of latent spatial structures embedded in temporal process data. First, a MIC-based graph construction method is employed to transform time series data into graph-structured data with nodes and edges, thereby preserving the local geometric structure of the original data and revealing high-dimensional spatial interaction information among data samples. Second, local slow feature analysis (SFA) is utilized to extract fine-grained dynamic correlation information from the spatial structure of the data. Furthermore, the Siamese GCNs are designed to simultaneously process graph-structured data samples at two consecutive time steps, which facilitates the capture of slowly varying feature representations embedded in the evolving topological structures. The proposed method can precisely extract and deeply mine spatiotemporal interactive features information, thereby enhancing the accuracy of performance assessment. Experimental validation on coal slurry flotation and dense medium coal preparation platforms confirms the method’s efficacy and reliability.

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Corrigendum to ‘Thermally stable piezoelectric sensors for quantitative pressure sensing based on linear piezoelectric zinc oxide thin films’ [Mater. Design 235 (2023) 112466]

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Materials & Design 2024年 237卷

作者： Yongkuan Li Hongsen Lin Jianrong Lin Cheng Zhou Aixin Luo Jiachen Yang Jieyu Zhang Zhixiang Hong Xueyan Hou Peng Xiao Bingfeng Fan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Physics and Optoelectronic Engineering Foshan University 528225 People’s Republic of China Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Process Automation in Mining & Metallurgy Beijing 102628 People’s Republic of China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 People’s Republic of China

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An Energy Consumption Prediction LSTM Model of metallurgy Enterprises 4

An Energy Consumption Prediction LSTM Model of Metallurgy En...

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4th International Conference on Environmental and Energy Engineering, IC3E 2020

作者： Wang, Xueying Yu, Zhuchao Xi, Pengfei Chu, Gaixia Lai, Shiyang Li, Jing Zhang, Yuanzheng School of Business Administration Northeastern University Shenyang China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China Qinghai XKXX Information Technology Co. LTD Xining China College of Medicine and Biological Information Engineering Northeastern University Shenyang China

Aiming at the characteristics of multi-dimensional production data, complicated sources and diverse data structures of metallurgy enterprises, it is of great significance to study how to use energy management-related data to predict metallurgy enterprises' energy consumption. Using with an accurate measurement method, the enterprises can not only reduce the cost of energy consumption but also develop economic efficiency in producing for metallurgy enterprises, which results in low energy use efficiency, high enterprise cost, and weak scalability. In this paper, we establish an LSTM model to achieve energy consumption prediction for metallurgy enterprises by optimizing the model's parameters using a grid search algorithm. We also compare our model's prediction results with other mainstream machine learning algorithms, i.e., MARS and SVM through indexes such as MSE, RMSE, MAPE, and MRAE to evaluate the prediction effect of the learning algorithm. According to our simulation, LSTM performs best in the task of energy consumption prediction. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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The study on genetic mineral processing engineering 29

The study on genetic mineral processing engineering

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29th International Mineral processing Congress, IMPC 2018

作者： Chuanyao, Sun Long, Han Junwu, Zhou Zhenguo, Song State Key Laboratory of Mineral Processing BGRIMM China Institute of Mineral Processing BGRIMM China State Key Laboratory of Process Automation in Mining and Metallurgy BGRIMM China

ISBN: (纸本)9787030227119

The deposit genesis, the ore property, and the mineral characteristics, which has a close relationship with the beneficiablity, could be regarded as the "Genetic Properties", actually are the determining factors for beneficiation process development. However, in conventional concentrator's process development and engineering, there have been lack of systematic research and test on those genetic properties, also lack of well-established collecting, statistics and analysis system for those huge amount of data such as the historical mineral processing technique research, process mineralogy study, practical operating and engineering data. Obviously it is far less than enough for profound integration combining modern IT together with traditional mineral processing technology, as a result there are a number of disadvantages in mineral processing R&D and engineering, such as long term, high cost, low efficiency as well as repetitive testwork not making full advantage of previous experiences. Aiming to make utmost of those Genetic Properties and overcome above disadvantages in mineral processing R&D and engineering, the concept of "Genetic Mineral processing Engineering (GMPE)" is put forwarded, and the strategical study has been conducted on this regard, with the main focus on following aspects: a) Testing and extraction of the "Genetic Properties";b) Establishment of data base and data warehouse of GMPE;c) Intelligent decision making system for beneficiation process;d) Verification testwork;e) Virtual concentrator. The technical route proposed is to firstly conduct research and test on those Genetic Properties mentioned above, based on that the specific data base will be set up and applied for intelligent process recommendation and simulation with a few verification testwork conducted in the meantime, finally achieve a fast, high efficiency and precise determination of technology and process for ore beneficiation. The systematic study on the three-in-one GMPE would make a

关键词： Data warehouses

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Bayesian Network-based Technical Index Estimation for Industrial Flotation process under Incomplete Data

Bayesian Network-based Technical Index Estimation for Indust...

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Chinese Control and Decision Conference, CCDC

作者： Hao Yan Jin Zhu Fuli Wang Dakuo He Qingkai Wang College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Due to the lack of detection instruments or long measurement cycles in the industrial flotation process, accurate and real-time estimation of the technical index is of great significance for optimizing flotation performance and operational adjustment. In the real-world flotation process, incomplete data is a widespread phenomenon owing to hardware sensor failures and other reasons. To this end, this paper proposes a Bayesian network (BN)-based concentrate grade estimation method under incomplete data. The real-time froth image information and the concentrate grades of the previous periods are taken as the input of the BN model, and the current concentrate grade is the output of the BN model. The expectation maximum (EM) algorithm is used to estimate the model parameters. The application results show the proposed method can accurately estimate the concentrate grade even if some data are missing.

关键词： Minerals Real-time systems Indexes Maximum likelihood estimation Manufacturing processes Predictive models

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An Energy Consumption Prediction LSTM Model of metallurgy Enterprises

An Energy Consumption Prediction LSTM Model of Metallurgy En...

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作者： Xueying Wang Zhuchao Yu Pengfei Xi Gaixia Chu Shiyang Lai Jing Li Yuanzheng Zhang School of Business Administration Northeastern University State Key Laboratory of Process Automation in Mining & Metallurgy College of Medicine and Biological Information Engineering Northeastern University

A iming at the characteristics of mu lti-dimensional production data,comp licated sources and diverse data structures of metallurgy enterprises,it is of great significance to study how to use energy management-related data to predict metallurgy enterprises' energy *** with an accurate measurement method,the enterprises can not only reduce the cost of energy consumption but also develop economic efficiency in producing for metallurgy enterprises,which results in low energy use efficiency,high enterprise cost,and weak *** th is paper,we establish an LSTM model to achieve energy consumption prediction for metallurgy enterprises by optimizing the model's parameters using a grid search algorith *** also compare our model's pred iction results with other mainstream machine learning algorith ms,i.e.,MARS and SVM through indexes such as MSE,RMSE,MAPE,and MRA E to evaluate the predict ion effect of the learning algorith *** to our simulat ion,LSTM performs best in the task of energy consumption prediction.

关键词： MSE An Energy Consumption Prediction LSTM Model of metallurgy Enterprises Model

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Fault Detection Based on Multi-local SVDD with Generalized Additive Kernels

Fault Detection Based on Multi-local SVDD with Generalized A...

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2019中国智能自动化大会(CIAC 2019)

作者： Huangang Wang Daoming Li Junwu Zhou Xu Wang Department of Automation Tsinghua University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy

Support vector data description(SVDD),has attracted many researchers' attention in statistical process *** batch process fault detection,based on the process data analysis of the threeway structural,a novel SVDD method integrating both generalized additive kernels and local models is proposed in this paper,which is Multilocal support vector data description with Generalized Additive Kernels(MLGAK-SVDD).It can obtain both the convenient on-line batch process fault detection model and the end-of-batch fault detection model at the same ***,a case study based on a fed-batch penicillin fermentation process is conducted to verify the validity of the proposed MLGAK-SVDD method.

关键词： Batch process fault detection Support vector data description Generalized additive kernel Local models

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