检索结果-内蒙古大学图书馆

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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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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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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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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Single-mode Sub-signal Selection Method Based on VMD and Predic Performance for Multi-component Mechanical Signal 39

Single-mode Sub-signal Selection Method Based on VMD and Pre...

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39th Chinese Control Conference, CCC 2020

作者： Tang, Jian Zhu, Hongjuan Li, Dong Zhang, Jian Yu, Gang Beijing University of Technology Faculty of Information Technology Beijing100124 China Pla Navy Beijing100129 China Joint Staff of the Central Military Commission 55th Institute Beijing100128 China Nanjing University of Information Science and Technology College of Computer and Software Nanjing210044 China State Beijing Key Laboratory of Process Automation in Mining Metallurgy Beijing100160 China

ISBN: (纸本)9789881563903

The mapping relationship between the mill load and the multi-component mechanical signals generated by the ball mill of the mineral grinding process is non-deterministic and complex. With the inherent filtering function of the human ear, the operating expert can effectively estimate the mill load and its internal parameters for their familiar mill in the actual industrial process. In order to obtain multiple single-mode sub-signals with physical meaning and complementary characteristic, this paper proposes a single-mode sub-signal selection method based on variational modal decomposition (VMD) and predictive performance. At first, based on prior knowledge, the value of decomposition layers required to perform VMD is determined. Then, VMD is used to decompose the original mechanical signal into multiple time-domain single-mode sub-signals with different bandwidths and time scales, and further are transformed to the frequency domain to obtain candidate single-mode sub-signal frequency spectrum. Finally, based on these candidate spectral data, a serial of candidate sub-models for mill load parameter prediction are constructed, and a series of selective ensemble models are built for obtaining reduced single-mode sub-signal frequency spectrum, The final single-mode sub-signals with the biggest complementary characteristics are selected based on the practical requirement. The effectiveness of the method is demonstrated by comparative experiment simulations based on the shell vibration signal of a laboratory-scale ball mill. © 2020 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Frequency domain analysis

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A time-delay analysis method for the variables of grinding process

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IFAC-PapersOnLine 2018年第21期51卷 88-93页

作者： Yu, Gang Zhou, Junwu Wang, Qingkai Zhao, Jianjun Department of IT & Automation BGRIMM Technology Group Beijing102600 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China Beijing Key Laboratory of Automation of Mining and Metallurgy Process Beijing102600 China

Data based expert knowledge mining is very important for intelligent and optimal grinding process control. However, the original process data includes noise, and there is a certain time delay between the variables of grinding process. These factors bring some difficulties to the extraction of expert knowledge based on data analysis. To solve this problem, a method of combining wavelet de-noising with cross-correlation is proposed for the time-delay analysis of variables in grinding process. The case study on the grinding process shows the effectiveness of the proposed method. © 2018

关键词： Data mining

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Multivariate Molten Iron Quality Modeling Based on Improved Incremental Random Vector Functional-link Networks

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IFAC-PapersOnLine 2018年第21期51卷 290-294页

作者： Jiang, Y. Zhou, P. Yu, G. The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102628 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102628 China

Aiming at the problems that the model structure is complex and the training time is too long for traditional incremental random vector functional-link networks (I-RVFLNs), this paper proposes an improved incremental random vector functional-link networks (Improved I-RVFLNs). Compared with the traditional I-RVFLNs, this algorithm changes the mode of constructing network. First, the number of hidden layer nodes is fixed in advance, and then the output weights of all nodes are changed in sequence. Finally, based on the actual data of a large blast furnace in Southern China, a nonlinear auto-regressive exogenous (NARX) model of multivariate molten iron quality (MIQ) is established by using the improved I-RVFLNs, and then it is compared with other modeling algorithms. The results show that compared with other quality modeling algorithms, the proposed method not only has higher model accuracy and shorter test time, but also solves the problem that the number of optimal hidden layer nodes in the conventional RVFLNs algorithm is difficult to choose, and the network is too complicated due to too many hidden nodes in the traditional I-RVFLNs. © 2018

关键词： Blast furnaces Iron Vectors Blast furnace iron making Data driven model Functional link network Molten iron NARX modeling

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Combining Active Learning and Fisher Discriminant Analysis for the Semi-supervised process Monitoring

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IFAC-PapersOnLine 2018年第21期51卷 147-151页

作者： Yin, Lili Wang, Huangang Fan, Wenhui Zhou, Junwu Yu, Gang Department of Automation Tsinghua University Beijing100084 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China Beijing Key Laboratory of Automation of Mining and Metallurgy Process Beijing102600 China

Fault detection and fault classification are two extremely important parts in process monitoring. However, obtaining the true labels of the industrial data is often time-consuming and expensive in practice, which brings great difficulties to the fault classification. To solve this problem, a new semi-supervised fault classification method is proposed, which combines the active learning and fisher discriminant analysis (FDA). The limited capacity of FDA can be effectively improved by integrating the active learning for semi-supervised fault classification. The experiments on the Benchmark of Tennessee Eastman process (TEP) prove the effectiveness of the proposed method. © 2018

关键词： Semi-supervised learning Discriminant analysis Fault detection Fisher information matrix process control process monitoring Active Learning Fault classification Fisher discriminant analysis In process monitoring Industrial datum Limited capacity Semi supervised Tennessee Eastman process

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Selective Ensemble Least Square Support Vector Machine with Its Application

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IFAC-PapersOnLine 2018年第18期51卷 631-636页

作者： Tang, J. Qiao, J.F. Liu, Z. Wu, Z.W. Zhou, X.J. Yu, G. Zhao, J.J. Faculty of Information Technology Beijing University of Technology Beijing China State Key Laboratory of Synthetical Automation for Process Industries Northeaster University Shenyang110004 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

Kernel-based modeling methods have been used widely to estimate some difficulty-to-measure quality or efficient indices at different industrial applications. Least square support vector machine (LSSVM) is one of the popular ones. However, its learning parameters, i.e., kernel parameter and regularization parameter, are sensitive to the training data and the model's prediction performance. Ensemble modeling method can improve the generalization performance and reliability of the soft measuring model. Aim at these problems, a new adaptive selective ensemble (SEN) LSSVM (SEN-LSSVM) algorithm is proposed by using multiple learning parameters. Candidate regularization parameters and candidate kernel parameters are used to construct many of candidate sub-sub-models based on LSSVM. These sub-sub-models based on the same kernel parameter are selected and combined as candidate SEN-sub-models by using branch and bound-based SEN (BBSEN). By employing BBSEN at the second time, these SEN-sub-models based on different kernel parameters are used to obtain the final soft measuring model. Thus, multiple kernel and regularization parameters are adaptive selected for building SEN-LSSVM model. UCI benchmark datasets and mechanical frequency spectral data are used to validate the effectiveness of this method. © 2018

关键词： Learning systems Parameterization Support vector machines Generalization performance Learning parameters Least square support vector machines Mechanical frequency Prediction performance Regularization parameters Selective ensemble modeling soft measuring

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Mill Load Parameter Forecasting Based on Multi-source Single-scale Mechanical Frequency Spectral Multiple feature Subsets

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IFAC-PapersOnLine 2018年第21期51卷 53-58页

作者： Tang, J. Qiao, J.F. Liu, Z. Zhou, Z.X.J. Yu, G. Zhao, J.J. Faculty of Information Technology Beijing University of Technology Beijing China State Key Laboratory of Synthetical Automation for Process Industries Northeaster University Shenyang110004 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

Multi-source mechanical signals at different locations of the ball mill system have different contributions for constructing mill load parameter forecasting (MLPF) model. It is necessary to select suitable multi-source mechanical signals and their frequency spectral feature subsets. Aim at these problems, a new MLPF approach based on LASSO algorithm and selective ensemble (SEN) modeling strategy by using multi-source single-scale mechanical frequency spectrum is proposed. At first, Fast Fourier transformer (FFT) is used to obtain single-scale frequency spectrum of these multi-source (channel) mechanical signals. Then, SEN-LASSO modeling approach based on candidate regularization parameter set is used to obtain single-scale frequency spectral feature subsets and to build single-channel SEN MLPF model jointly. Finally, SEN strategy is employed again to construct the final MLPF model by selecting and combining these single-channel SEN MLPF models. Simulation results based on a laboratory-scale ball mill validate effectiveness of this approach. © 2018

关键词： Ball mills Feature extraction Forecasting Regression analysis Spectroscopy Frequency spectra Measuring model Mechanical frequency Mechanical signals Mill loads Regularization parameters Selective ensemble modeling Selective ensembles

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