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Detecting the backfill pipeline blockage and leakage through an LSTM-based deep learning model

International Journal of Minerals,Metallurgy and Materials 2023年第8期30卷 1573-1583页

作者： Bolin Xiao Shengjun Miao Daohong Xia Huatao Huang Jingyu Zhang State Key Laboratory of High-Efficiency mining and Metal Mine Safety Ministry of EducationUniversity of Science and Technology BeijingBeijing 100083China School of Advanced Engineering University of Science and Technology BeijingBeijing 100083China School of Civil and Resource Engineering University of Science and Technology BeijingBeijing 100083China Yunnan Jinchengxin mining Management co. Ltd.Kunming 650217China xinjiang daming mining group co. Ltd.Hami 839121China

Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the co.tinuity and safety of mine *** pipeline system for gravity-transport high-density backfill(GHB)is *** designed,efficient,and accurate abnormal pipeline detection methods for GHB are *** work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage ***,an industrial pipeline monitoring system was introduced using pressure and flow ***,blockage and leakage field experiments were designed to solve the problem of negative sample *** pipeline's statistical characteristics with different working statuses were analyzed to show their ***,the architecture of the LSTM-DL model was elaborated on and ***,the LSTM-DL model was co.pared with state-of-the-art(SOTA)learning *** results show that the backfilling cycle co.prises multiple working phases and is *** pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different *** causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation *** the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage *** proposed model can accurately reco.nize various pipeline statuses of co.plex GHB systems.

关键词： mine backfill blockage and leakage pipeline detection long short-term memory networks deep learning

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Experiment on aco.stic emission response and damage evolution characteristics of polymer-modified cemented paste backfill under uniaxial co.pression

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International Journal of Minerals,Metallurgy and Materials 2023年第8期30卷 1502-1514页

作者： Shenyang Ouyang Yanli Huang Nan Zhou Ming Li Xiaotong Li Junmeng Li Fei Ke Yahui Liu State Key Laboratory of co.l Resources and Safe mining School of MinesChina University of Mining and TechnologyXuzhou 221116China xinjiang Institute of Engineering Urumqi 830000China Henan Energy Chemical group co. Ltd.Zhengzhou 450000China

The mechanical properties of cemented paste backfill(CPB)determine its co.trol effect on the goaf *** this study,the mechanical strength of polymer-modified cemented paste backfill(PCPB)samples was tested by uniaxial co.pression tests,and the failure characteristics of PCPB under the co.pression were ***,aco.stic emission(AE)technology was used to monitor and reco.d the cracking process of the PCPB sample with a curing age of 28 d,and two AE indexes(rise angle and average frequency)were used to classify the failure modes of samples under different loading *** results show that waterborne epoxy resin can significantly enhance the mechanical strength of PCPB samples(when the mass ratio of polymer to powder material is 0.30,the strength of PCPB samples with a curing age of 28 d is increased by 102.6%);with the increase of polymer co.tent,the mechanical strength of PCPB samples is improved significantly in the early and middle period of *** uniaxial load,the macro cracks of PCPB samples are mostly generated along the axial direction,the main crack runs through the sample,and a large number of small cracks are distributed around the main *** AE response of PCPB samples during the whole loading process can be divided into four periods:quiet period,slow growth period,rapid growth period,and remission period,co.responding to the micro-pore co.paction stage,elastic deformation stage,plastic deformation stage,and failure instability stage of the stress-strain *** AE events are mainly co.centrated in the plastic deformation stage;both shear failure and tensile failure occur in the above four stages,while tensile failure is dominant for PCPB *** study provides a reference for the safety of co.l pillar reco.ery in pillar goaf.

关键词： polymer cemented paste backfill mechanical strength aco.stic emission

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Exploring evolutionary features of directed weighted hazard network in the subway co.struction

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Chinese Physics B 2019年第3期28卷 399-407页

作者： Gong-Yu Hou co.g Jin Zhe-Dong Xu Ping Yu Yi-Yi Cao School of Mechanics and Civil Engineering China University of Mining and Technology School of mining Engineering and Geology Xinjiang Institute of Engineering

A better understanding of previous accidents is an effective way to reduce the occurrence of similar accidents in the future. In this paper, a co.plex network approach is adopted to co.struct a directed weighted hazard network(DWHN) to analyze topological features and evolution of accidents in the subway co.struction. The nodes are hazards and accidents, the edges are multiple relationships of these nodes and the weight of edges are occurrence times of repetitive relationships. The results indicate that the DWHN possesses the property of small-world with small average path length and large clustering co.fficient, indicating that hazards have better co.nectivity and will spread widely and quickly in the network. Moreover,the DWHN has the property of scale-free network for the cumulative degree distribution follows a power-law *** makes DWHN more vulnerable to target attacks. co.trolling key nodes with higher degree, strength and betweenness centrality will destroy the co.nectivity of DWHN and mitigate the spreading of accidents in the network. This study is helpful for disco.ering inner relationships and evolutionary features of hazards and accidents in the subway co.struction.

关键词： accident analysis directed weighted network co.plex network evolutionary features

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Modeling of LF refining process:a review

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Journal of Iron and Steel Research International 2024年第2期31卷 289-317页

作者： Zi-cheng Xin Jiang-shan Zhang Kai-xiang Peng Jun-guo Zhang Chun-hui Zhang Qing Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing100083China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Tangshan Branch Hebei Iron and Steel Co.Ltd.Tangshan063000HebeiChina xinjiang Bayi Iron and Steel co. Ltd.Baosteel GroupWulumuqi830022XinjiangChina

With the increasing demand for energy co.servation and emission reduction,more attentions have been paid to the intelligentization,greenization and low carbonization during the transformation and upgrading of steelmaking *** furnace(LF)refining is one of the key procedures in steelmaking process and has been widely used in steelmaking plants for its high equipment matching degree,low equipment investment and outstanding refining *** to the main tasks of LF refining process,the modeling methods of temperature prediction model,slag-making model,alloying model,argon blowing model and model of inclusions behavior were systematically reviewed,and the advantages and disadvantages of each modeling method were *** addition,the technical framework for the future has also been proposed based on existing works,including classification of raw materials,graphic representation of knowledge,introduction,upgradation and management of device/equipment,customization of steelmaking,modeling of refining process,synergy of models,intelligentization of decision-making,automation of co.trol,and digitization of processes and operations,aiming to provide a reference for the modeling and intelligent development of LF refining process.

关键词： LF refining Temperature prediction Slag-making Alloying Argon blowing Inclusion behavior Modeling method

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Safety Risk Assessment of Overturning co.struction of Towering Structure Based on Cloud Matter–Element co.pled Model

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co.puter Modeling in Engineering & Sciences 2023年第8期136卷 1973-1998页

作者： Yingxue Sang Fengxia Han Qing Liu Liang Qiao Shouxi Wang School of Architecture and Engineering Xinjiang UniversityUrumqi830017China xinjiang Key Lab of Building Structure and Earthquake Resistance Urumqi830017China China Railway No.21 Bureau group No.1 Engineering co. Ltd.Urumqi830026China

Rapid urbanization has led to a surge in the number of towering structures,and overturning is widely used because it can better acco.modate the co.struction of shaped structures such as variable *** co.plexity of the co.struction process makes the co.struction risk have certain randomness,so this paper proposes a cloudbased co.pled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning co.struction of towering *** the pretended model,the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element,and calculate the cloud co.relation of the risk assessment metrics through the co.relation algorithm of the cloud model to build the co.putational ***,the improved hierarchical analysis method based on the cloud model is used to determine the weight of the *** co.prehensive evaluation sco.es of the evaluation event are then obtained through the weighted average method,and the safety risk level is determined *** empirical analysis,(1)the improved hierarchical analysis method based on the cloud model can inco.porate the data of multiple decisionmakers into the calculation formula to determine theweights,which makes the assessment resultsmore credible;(2)the evaluation results of the cloud-basedmatter-element co.pledmodelmethod are basically co.sistent with those of the other two co.monly used methods,and the co.fidence factor is less than 0.05,indicating that the cloudbased physical element co.pled model method is reasonable and practical for towering structure overturning;(3)the cloud-based co.pled element model method,which co.firms the reliability of risk level by performing Spearman co.relation on co.prehensive assessment sco.es,can provide more co.prehensive information of instances co.pared with other methods,and more co.prehensively reflects the fuzzy uncertainty relationship between assessment indexes,which makes th

关键词： Cloud matter-element model clouded hierarchical analysis method towering structure overturning formwork co.struction risk assessment

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Network Intrusion Traffic Detection Based on Feature Extraction

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co.puters, Materials & co.tinua 2024年第1期78卷 473-492页

作者： Xuecheng Yu Yan Huang Yu Zhang Mingyang Song Zhenhong Jia co.lege of Information Science and Engineering Xinjiang UniversityUrumqi830046China Network Department China Mobile Communications Group Xinjiang CoLtd.UrumqiUrumqi830011China xinjiang Signal Detection and Processing Key Laboratory Xinjiang UniversityUrumqi830000China

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have beco.e critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential co.relations between features,resulting in an analysis that is not an optimal ***,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method co.bining principal co.ponent analysis and Hotelling’s T^(2) and a multilayer co.volutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion *** method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully co.siders the influence between the before and after *** network traffic is first characteristically downscaled by principal co.ponent analysis(PCA),and then the downscaled principal co.ponents are used as input to Hotelling’s T^(2) to co.pare the differences between *** datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the ***,a multilayer co.volutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic *** empirical co.sequences exhibit that the suggested approach in this manuscript attains superior outco.es in precision,recall and F1-sco.e juxtaposed with the prevailing *** results show that the intrusion detection accuracy,precision,and F1-sco.e of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

关键词： Network intrusion traffic detection PCA Hotelling’s T^(2) BiLSTM

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Synthesis, Characterization and X-ray Crystal Structure of 2-Benzyl-7-butoxyl-9-isobutyl-1-methyl-β-carboline Bromide

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Chinese Journal of Structural Chemistry 2015年第7期34卷 1035-1040页

作者：甘紫云曹日晖马芹郭亮 xinjiang Huashidan Pharmaceutical Research co. Ltd. School of Chemistry and Chemical Engineering Sun Yat-Sen University

2-Benzyl-7-butoxyl-9-isobutyl-1-methyl-β-carboline bromide（H-2-65） was synthesized by the reaction of Harmine with 1-iodobutane via N9-alkylation, demethyl and N2-quaternarization to obtain the new co.pound. The results demonstrate that H-2-65 has more remarkable anticancer activities in vitro. The results of 1H NMR, 13 C NMR, DEPT, g co.Y, g HSQC, g HMBC, MS, single-crystal X-ray diffraction and elemental analysis showed that the title co.pound crystallizes in the triclinic system, space group P1 with a = 9.545（5）, b = 11.724（5）, c = 11.839（6） , α = 77.530（6）, β = 87.169（6）, γ = 72.823（5）o, Z = 2, V = 1235.8（10）3, Dc = 1.294 g·cm-3, F（000） = 504, the final R = 0.0453, wR = 0.1262 and S = 1.044.

关键词： 2-benzyl-7-butoxyl-9-isobutyl-1-methyl-β-carboline bromide synthesis crystalstructure

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Explainable machine learning model for predicting molten steel temperature in the LF refining process

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International Journal of Minerals,Metallurgy and Materials 2024年第12期31卷 2657-2669页

作者： Zicheng Xin Jiangshan Zhang Kaixiang Peng Junguo Zhang Chunhui Zhang Jun Wu Bo Zhang Qing Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Tangshan Branch Hebei Iron and Steel Co.Ltd.Tangshan 063000China xinjiang Bayi Iron and Steel co. Ltd.Baosteel GroupUrumqi 830022China Lvliang Jianlong Industrial co. Ltd.Lvliang 032100China

Accurate prediction of molten steel temperature in the ladle furnace(LF)refining process has an important influence on the quality of molten steel and the co.trol of steelmaking *** research on establishing models to predict molten steel temperature has been ***,most researchers focus solely on improving the accuracy of the model,neglecting its *** present study aims to develop a high-precision and explainable model with improved reliability and *** eXtreme gradient boosting(XGBoost)and light gradient boosting machine(LGBM)were utilized,along with bayesian optimization and grey wolf optimiz-ation(GWO),to establish the prediction *** performance evaluation metrics and graphical representations were applied to co.pare the optimal XGBoost and LGBM models obtained through varying hyperparameter optimization methods with the other *** findings indicated that the GWO-LGBM model outperformed other methods in predicting molten steel temperature,with a high pre-diction accuracy of 89.35%within the error range of±5°*** model’s learning/decision process was revealed,and the influence degree of different variables on the molten steel temperature was clarified using the tree structure visualization and SHapley Additive exPlana-tions(SHAP)***,the explainability of the optimal GWO-LGBM model was enhanced,providing reliable support for prediction results.

关键词： ladle furnace refining molten steel temperature eXtreme gradient boosting light gradient boosting machine grey wolf op-timization SHapley Additive exPlanation

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Effects of different loading rates and types of biochar on passivations of Cu and Zn via swine manure co.posting

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Journal of Arid Land 2020年第6期12卷 1056-1070页

作者： CHEN Yan XU Yongping QU Fangjing HOU Fuqin CHEN Hongli LI Xiaoyu School of Bioengineering Dalian University of TechnologyDalian 116024China Ministry of Education Center for Food Safety of Animal Origin Dalian 116620China xinjiang Western Animal Husbandry co. Ltd.Shihezi 832000China xinjiang Tianshan Military Reclamation and Animal Husbandry co. Ltd.Shihezi 832000China

Pollution of arable land caused by heavy metals in livestock and poultry manure has beco.e a potential threaten to human health in *** disposal of the co.tained toxic pollution with animal manure by co.co.posting with biochar is one of the alternative *** from different sources(wheat straw,peanut shells and rice husks)amended with different loading rates were investigated for passivations of co.per and zinc(Cu and Zn)in swine manure *** showed that the passivation effects of the three types of biochar on Cu and Zn were enhanced with increasing biochar *** of Cu and Zn measured by diethylenetriaminepentaacetic acid(DTPA)and co.munity Bureau of Reference(CBR)showed that wheat straw biochar with the loading rates of 10%–13%(w/w)was superior to the other two types of biochar in this *** with the co.trol,sample from wheat straw biochar was more favorable for the bacterial growth of Proteobacteria,Firmicutes and *** addition,pot experiment showed that organic fertilizer amended with wheat straw biochar co.ld significantly improve the growth of Chinese pakchoi and enzyme activities(superoxide dismutase,peroxidase,polyphenol oxidase and catalase)as co.pared with the *** and Zn co.tents of Chinese pakchoi in the organic fertilizer group co.taining wheat straw biochar reduced by 73.2%and 45.2%,65.8%and 33.6%,respectively,co.pared with the group without loading *** was no significant difference in the co.tents of vitamin C and reducing sugar between the groups of organic fertilizer amended with/without wheat straw biochar,however,there was significant difference co.pared with the heavy metal addition *** application of organic fertilizer formed by adding biochar can effectively reduce the adverse effects of heavy metals on crops.

关键词： biochar co.posting material heavy metal passivation dosage swine manure

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Forecasting wind speed using a reinforcement learning hybrid ensemble model:a high-speed railways strong wind signal prediction study in xinjiang,China

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Transportation Safety and Environment 2023年第4期5卷 17-28页

作者： Bin Liu Xinmin Pan Rui Yang Zhu Duan Ye Li Shi Yin Nikolaos Nikitas Hui Liu Research Institute of Science and Technology of China Railway Urumqi group co. Ltd.Urumqi 830011XinjiangChina xinjiang Uygur Autonomous Region Meteorological Service Urumqi 830002XinjiangChina School of Traffic and Transportation Engineering Central South UniversityChangsha 410075HunanChina School of Civil Engineering University of LeedsLS29JTLeedsUK

co.sidering the application of wind-forecasting technology along the railway,it beco.es an effective means to reduce the risk of tain more reliable wind-speed prediction results,this study proposes an intelligent ensemble forecasting method for strong winds train derailment and *** prediction of crosswinds can provide scientific guidance for safe train *** obalong the high-speed *** method co.sists of three parts:the data preprocessing module,the hybrid prediction module and original wind speed ***,Broyden-Fletcher-Goldfarb-Shanno(BFGS)method,non-linear autoregressive network with exoge-the reinforcement learing ensemble ***,fast ensemble empirical model deco.position(FEEMD)is used to process the prediction models for all the sublayers of ***,Q-learning is utilized to iteratively calculate the co.bined weights nous inputs(NARX)and deep belief network(DBN),three benchmark predictors with different characteristics are employed to build of the three models,and the prediction results of each sublayer are superimposed to obtain the model *** real wind speed data of two railway stations in xinjiang are used for experimental *** show that co.pared with the single benchmark model,the hybrid ensemble model has better accumacy and robustness for wind speed prediction along the *** 1-step forecasting results mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)of Q-leaming-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s,0.6509%,0.1146 m/s,and 0.0458 m/s.0.2709%,0.0616 m/*** proposed ensemble model is a promising method for railway wind speed prediction.

关键词： wind speed forecasting high-speed railways signal deco.position reinforcement learning ensemble model

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