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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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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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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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Operating Performance Assessment of Plant-Wide processes Based on Hierarchical Multiblock Performance-Relevant Kernel Independent Component Analysis

Operating Performance Assessment of Plant-Wide Processes Bas...

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第40届中国控制会议

作者： Yan Liu Fei Chu Fuli Wang Dakuo He College of Information Science&Engineering Northeastern University College of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

The operating performance assessment of plant-wide processes is very important for improving product quality and comprehensive economic *** this study,a new operating performance assessment method based on hierarchical multiblock performance-relevant kernel independent component analysis(HMPRKICA) is proposed for nonlinear and non-Gaussian plant-wide *** proposed approach divides the process variables into several sub-processes according to the physical structure and incorporates sub-process information into high level for operating performance assessment of plant-wide *** the new method,the difficulty of process analysis is reduced and the interpretability of the assessment results are *** process operating performance is nonoptimal,the responsible sub-process is first located by the sub-process assessment result,and then the variable contribution rates are calculated in the corresponding sub-process to narrow the search for the nonoptimal ***,a case study of gold hydrometallurgy process evaluates the efficiency of the proposed method.

关键词： Plant-wide process operating performance assessment HMPRKICA non-Gaussianity nonlinearity

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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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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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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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A denoising shrinkage stacked autoencoder method for fault detection in industrial beneficiation ILAB process with uncertainty

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Measurement 2025年

作者： Wei Zhang Yunpeng Gao Yongbin Zhou Fei Teng Haili Zhao Jianjun Zhao Department of College of Electrical and Information Engineering Hunan University Changsha 410012 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing 102628 China BGRIMM Technology Group Beijing 102628 China

The intelligent laboratory (ILAB) is used in industrial beneficiation processes to replace manual laboratory testing and enhance quality control. Fault detection is crucial for ensuring reliable operation. However, uncertainties in sampling, preparation, measurement, and environmental conditions degrade fault detection performance. Conventional methods for uncertainty-aware fault detection typically assume the same level of uncertainty for all variables, which oversimplifies the complexity of real industrial processes. In real industrial processes, uncertainties are interrelated, and considering them in isolation does not accurately reflect the true state of the system. To address this issue, a denoising shrinkage stacked autoencoder (DSSAE) fault detection method is proposed. First, a combined uncertainty degree is constructed to accurately represent uncertainty by integrating multiple sources such as sampling, preparation, measurement, and acid corrosion aging. Second, the shrinking blocks embedded in the stacked autoencoder adaptively adjust the thresholds using a soft thresholding mechanism to set near-zero feature values to zero, reducing irrelevant features layer by layer while enhancing important features. Third, the redefined loss function constrains the reconstruction error within an allowable range, which reduces the sensitivity of uncertainty and prevents overfitting to uncertain data. The proposed method is validated on the benchmark Tennessee Eastman (TE) process, industrial beneficiation ILAB process, and wastewater treatment plant (WWTP) process. Experimental results show that the proposed method exhibits good comprehensive performance.

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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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