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Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India
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作者 Mukul Sharma Bhanwar Singh Choudhary +2 位作者 Autar K.Raina Manoj Khandelwal Saurav Rukhiyar 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第8期2879-2893,共15页
Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the inc... Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded blastholes.Thus,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes.Additionally,it will help achieve the desired fragment size.This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam.Data on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques.The coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the results.It indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,respectively.Multiple parametric sensitivity analyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size.The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting.The optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively. 展开更多
关键词 Fiery seam Rock fragmentation Response Surface Method(RSM) Artificial Neural Network(ANN) Random Forest Algorithm(RFA) Multiple Parametric sensitivity analysis (mpsa)
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Study of the Sensitive Properties of Marine Gas Hydrate Based on the Prestack Elastic Inversion 被引量:1
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作者 TONG Siyou WANG Jialin +3 位作者 LI Linwei ZHANG Haiqi WU Zhiqiang SHAO Yulan 《Journal of Ocean University of China》 SCIE CAS CSCD 2019年第5期1086-1092,共7页
Using a bottom simulating reflector(BSR)on a seismic profile to identify marine gas hydrate is a traditional seismic exploration method.However,owing to the abundance differences between the gas hydrate and free gas i... Using a bottom simulating reflector(BSR)on a seismic profile to identify marine gas hydrate is a traditional seismic exploration method.However,owing to the abundance differences between the gas hydrate and free gas in different regions,the BSR may be unremarkable on the seismic profile and invisible in certain cases.With the improvement of exploration precision,difficulty arises in meeting the requirements of distinguishing the abundance differences in the gas hydrate based on BSR.Hence,we studied other sensitive attributes to ascertain the existence of gas hydrate and its abundance variations,eventually improving the success rate of drilling and productivity.In this paper,we analyzed the contradiction between the seismic profile data and drilling sampling data from the Blake Ridge.We extracted different attributes and performed multi-parameter constraint analysis based on the prestack elastic wave impedance inversion.Then,we compared the analysis results with the drilling sampling data.Eventually,we determined five sensitive attributes that can better indicate the existence of gas hydrate and its abundance variations.This method overcomes the limitations of recognizing the gas hydrate methods based on BSR or single inversion attribute.Moreover,the conclusions can notably improve the identification accuracy of marine gas hydrate and provide excellent reference significance for the recognition of marine gas hydrate.Notably,the different geological features of reservoirs feature different sensitivities to the prestacking attributes when using the prestack elastic inversion in different areas. 展开更多
关键词 sensitIVE seismic attribute the bottom simulating reflector marine gas HYDRATE PRESTACK ELASTIC impedance INVERSION multi-parameter constraint analysis
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污水处理过程递推双线性子空间建模及无模型自适应控制 被引量:3
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作者 张帅 周平 《自动化学报》 EI CAS CSCD 北大核心 2022年第7期1747-1759,共13页
污水处理过程中,生化反应硝态氮浓度和溶解氧浓度是决定出水水质好坏的两个最关键变量,难以采用常规基于模型的方法进行有效控制.本文基于数据驱动建模与控制技术,提出一种污水处理过程递推双线性子空间辨识(Recursive bilinear subspac... 污水处理过程中,生化反应硝态氮浓度和溶解氧浓度是决定出水水质好坏的两个最关键变量,难以采用常规基于模型的方法进行有效控制.本文基于数据驱动建模与控制技术,提出一种污水处理过程递推双线性子空间辨识(Recursive bilinear subspace identification,RBLSI)建模和无模型自适应控制方法.首先,针对污水处理过程的非线性时变动态特性,采用最小二乘递推双线性子空间辨识方法建立污水处理生化反应过程具有参数自适应能力的递推双线性模型;其次,基于建立的数据驱动模型,采用基于多参数灵敏度分析(Multi-parameter sensitivity analysis,MPSA)和遗传粒子群优化(Genetic algorithm-particle swarm optimization,GA-PSO)算法的无模型自适应控制(Model-free adaptive control,MFAC)方法对硝态氮和溶解氧浓度进行直接数据驱动控制;最后,数据实验及其比较分析表明了所提方法的有效性和优越性. 展开更多
关键词 污水处理 递推双线性子空间辨识 无模型自适应控制 多参数灵敏度分析
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