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Chinese control and Decision Conference, CCDC

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 Beijing China College of Information Science and Engineering Northeastem University Shenyang China

The roller mill is a crucial production equipment for the nonferrous metal beneficiation process, responsible for crushing ores. Over time, the roller tyre of the roller mill gradually wears down, ultimately impacting the crushing efficiency. Therefore, Prognostics and Health Management (PHM) of the roller tyre is crucial to enhancing the production efficiency of the roller mill. In this study, a PHM method based on Dual Autoencoder (DUAE) is proposed to predict the grinding roller tyre’s service life. The effectiveness of the proposed PHM method is validated using actual data from a domestic concentrator. This research demonstrates that the PHM of roller tyres can be effectively conducted.

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Nonlinear Successive Compensation Method for Plant-Wide Production Index Based on Industrial Data

Nonlinear Successive Compensation Method for Plant-Wide Prod...

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Chinese control and Decision Conference, CCDC

作者： Kang Li Guobin Zou Yinlian Duan Fuli Wang BGRIMM Technology Group Beijing China State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining and Metallurgy Research Beijing China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665478977

Due to the complexities of high-dimensionality, nonlinearity, many constraints, and process model mismatch, the plant-wide production index needs to be compensated when the production index is applied to the actual production process. This paper proposes a nonlinear successive compensation method for the plant-wide production index based on industrial data. Based on the set point of the production index obtained from the plant-wide optimization, the method uses the large amount of industrial data stored in the industrial process to establish the relationship between production index compensation and the increase of economic benefit, optimizing the solution value of the production index compensation that maximizes the economic benefit increment. Through iterative compensation, the method gradually improved the economic benefit of the production process. The proposed method was applied to the hydrometallurgical production process of a refinery. The results verified the effectiveness of the proposed method.

关键词： Economics Industrial economics Production Big Data Data models Complexity theory Indexes

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An image classification algorithm for MSWI based on ResNet and attention mechanism

An image classification algorithm for MSWI based on ResNet a...

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Chinese control and Decision Conference, CCDC

作者： Jian Zhang Jianbo Yu Gang Yu Rongcheng Sun Jian Tang School of Computer Science Nanjing University of Information Science & Technology Nanjing China NUIST-TianChang Research Institute Nanjing University of Information Science & Technology Nanjing China State & Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The municipal solid waste incineration has great advantages for resource recovery and utilization, and the stability of its combustion state is of great significance to the control process. However, the problem of low accuracy still exists in the classification of the images of municipal solid waste incineration (MSWI). For this reason, this paper proposes an image classification algorithm for MSWI based on attention module. The framework of ResNet is employed to improve the depth and accuracy of the network, which solves the problem of insufficient depth of the traditional network while suffering from the deficiency of slow convergence speed. Consequently, the attention module is embedded to solve the problem of different importance of different parts in the incineration flame image. The results show that the algorithm improves the recognition accuracy of municipal solid waste incineration flame images to 96.7%, and the convergence speed is significantly improved compared to other basic neural network models.

关键词： Waste management Training Waste materials Accuracy Image recognition Incineration Fires

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Recognition of Combustion Conditions in MSWI process based on Hierarchical-Split ResNet

Recognition of Combustion Conditions in MSWI Process based o...

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Chinese control and Decision Conference, CCDC

作者： Jian Zhang Rongcheng Sun Gang Yu Jianbo Yu Jian Tang School of Computer Science Nanjing University of Information Science & Technology Nanjing China NUIST-TianChang Research Institute Nanjing University of Information Science & Technology Nanjing China State & Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Municipal solid waste incineration (MSWI) has significant advantages in the treatment of municipal solid waste, however, the instability of the solid waste combustion state can lead to a reduction in the operational efficiency of the whole system. In addition, the judgement of the MSWI state mainly relies on the operators' own experience to control the combustion, leading to the identification results with substantially subjective and arbitrary. In order to address these challenging problems, a Hierarchical-Split residual neural network is proposed for MSWI process combustion state recognition, which is modeled by collecting combustion images, combining the experience of field experts and the incinerator grate structure, and classifying the combustion state into four categories: normal burning, partial burning, channelling burning, and smothering. Aiming at the problem of incomplete image information extracted by the traditional single-scale convolution, the Hierarchical-Split feature extraction module is utilized not only to enhance the ability of the model to extract feature information, but also to improve the recognition accuracy of the model. Finally, the proposed method is validated by taking the flame data of a municipal waste treatment plant in Beijing as an example. The experimental results show that the method effectively improves the generalization and robustness, and has good recognition accuracy.

关键词： Waste management Waste materials Accuracy Image recognition Incineration Fires Feature extraction

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Operating Performance Assessment of Complex Industrial process Based on Kernel Locally Linear Embedding PLS

Operating Performance Assessment of Complex Industrial Proce...

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第32届中国过程控制会议(CPCC2021)

作者： Chu Fei Mo Shuangshuang Wang Fuli Lu Ningyun Ma Xiaoping Research Center of Underground Space Intelligent Control Engineering of the Ministry of Education China University of Mining and Technology School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Automatic Control Technology for Mining and Metallurgy Process Beijing General Research Institute of Mining & Metallurgy State Key Laboratory of Integrated Automation for Process Industries Northeastern University College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Due to the process disturbances and uncertainties,the operating performance of industrial process may deviate from the optimal operating point along with ***,non-optimal operation may lead to decline production quality and economic *** it is significant to implement online assessment strategy and further analyze the actual process operating performance,so as to make the production process in the best operating performance as far as *** process data is strongly nonlinear due to the change of system state caused by the change of machine performance or operation *** this time,the relationship between the local nearest neighbor samples is ***,the existing methods of operating performance assessment for solving nonlinear problems only focus on the global structure of data,i.e.,the variance,while ignoring the local neighborhood structure in each performance ***,a new method called kernel locally linear embedding PLS(KLLEPLS) is developed and applied for operating performance *** includes offline modeling,online evaluation and non-optimal cause *** algorithm not only has concise expression and similar analytical solutions for PLS,but also retains the original geometric structure of strongly nonlinear data when extracting the maximum correlation information between process ***,the practice and effectiveness of the proposed method are demonstrated by the dense medium coal preparation process and Tennessee Eastman(TE) process.

关键词： Complex industrial process operating performance assessment kernel locally linear embedding PLS non-optimal cause identification

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Grade Monitoring using Semantic Features of Flotation Froth Image

Grade Monitoring using Semantic Features of Flotation Froth ...

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Chinese control and Decision Conference, CCDC

作者： Xu Wang Jingmin Lian Daoxi Liu Yutian Lei School of Information Science and Engineering Northeastern University Shenyang China BGRIMM Technology Group Beijing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9781665478977

In the industrial flotation circuits, grades are the key performance indicators for flotation condition recognition and operations. Modeling based on machine vision is an effective tool for grade monitoring, and froth image feature extraction as a model input plays a crucial role. Given the low efficiency of hand-crafted features and the poor interpretability of high-dimensional features extracted by convolutional neural networks, a grade monitoring method with human-understandable semantic features as input is proposed in this paper. First, based on the combination of generative adversarial networks and encoder, we design a new network structure that maps the froth images data space to the latent semantic space. Then the semantic features of froth images can be automatically extracted by decomposing the constructed matrix with rich latent semantics. Finally, as demonstrated in the industrial experiment, the extracted semantic features can not only be visually interpreted but also can be effectively used in grade monitoring.

关键词： Machine vision Semantics key performance indicator process control Predictive models Feature extraction Generative adversarial networks

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Multivariate Time Delay Estimation Based on Dynamic Characteristic Analytics

Multivariate Time Delay Estimation Based on Dynamic Characte...

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第三十九届中国控制会议

作者： Xu Chen Chunhui Zhao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining &Metallurgy

Time delay widely exists among industrial process variables, which may lead to invalid description of systems and reduce the model accuracy for soft sensor, process monitoring etc. Therefore, it is crucial to estimate time delay for improving model accuracy. In traditional research, time delay estimation（TDE） algorithm for two variables is fully investigated, but little research pays attention to multivariate. The method of pairwise comparison may not only cause high calculation complexity but also cut off the correlation between multivariate. In this work, a new TDE algorithm is proposed to estimate time delay between multivariate, which extracts dynamic latent variable to represent the process using improved dynamic inner PCA algorithm（DiPCA）. Dynamic latent variable extracts the autocorrelation and cross-correlation between process variables. Take it as a standard and analyze the time delay, so that the influence of multivariate variables could be comprehensively considered. Since there may be non-linear variables, the significant factor（SF） is used for variable selection, and the multivariate variables are divided into multiple linear subgroups, which makes the analysis result more reasonable. The effectiveness is illustrated with two numerical examples and the Tennessee Eastman process.

关键词： Time delay estimation process dynamics dynamic latent variable variable selection multivariate

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Incremental Gaussian Mixture Model for Time-varying process Monitoring

Incremental Gaussian Mixture Model for Time-varying Process ...

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Data Driven control and Learning Systems (DDCLS)

作者： Qingyang Dai Chunhui Zhao State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

With the increasing complexity of industrial production, data-driven based monitoring methods attract more attention. However, the conventional static process monitoring methods may show poor performance for the time-varying processes since they fail to track the time-varying characteristics. As Gaussian Mixture Model (GMM) has been widely used for process monitoring, this paper presents a new incremental GMM model for monitoring time-varying processes. First, an incremental GMM (IGMM) model is proposed, which can recursively update model parameters, adaptively add new Gaussian components and discard the irrelevant component based on the shifting samples online. Then the Bayesian Inference Probability (BIP) is introduced for monitoring statistics and a two-level partition strategy that can separate normal shifting samples from fault samples is proposed, which reduces the possibility of adding fault samples to the model. On the basis of IGMM model, an adaptive monitoring scheme is developed, which can track the time-varying characteristics of processes. Finally, a time-varying numerical example and the Tennessee Eastman process are adopted to validate the feasibility of the proposed monitoring model. Experimental results clearly demonstrate the adaptiveness of the monitoring model to time-varying processes and the ability to avoid false updates.

关键词： Adaptation models process monitoring Mathematical model Gaussian mixture model Numerical models Data models Production

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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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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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