查看更多>>摘要：Foams improve mobility control in injection operations within geological settings.Nanoparticles,such as iron-oxide,have been shown to enhance the stability of foams when combined with surfactants.In this research,we leverage the magnetic properties of these nanoparticles to detect their presence as a surrogate for monitoring the geologic extent of injected flu-ids in the subsurface.The feasibility of using these nanoparticles for monitoring purposes stems from their detectability at low concentrations in subsurface environments.We developed two distinct methods to detect the presence of magnetite nanoparticles in complex fluids.To simulate complex subsurface fluids in a laboratory setting,we included various ions and surfactants and investigated their effects on the detection of nanoparticles.To this end,we designed an experimental setup and tested two magnetic detection methods:Induction Heating(IH)and Oscillator Frequency Shift(OFS).The IH method involves applying a high-frequency alternating magnetic field to a solution containing small amounts of magnetic nanopar-ticles and measuring the temperature response.We built an experimental setup to generate this magnetic field for different samples,with temperature changes recorded by an infrared camera.The results indicate that nanoparticle concentrations linearly affect the solution's temperature rise.However,the presence of ions and surfactants also influences the temperature response.The OFS method measures shifts in the resonance frequency of a circuit caused by changes in magnetic perme-ability inside a coil.This coil is part of a transistor oscillator circuit that produces a sinusoidal voltage waveform,with the oscillation frequency depending on the coil's inductance.The presence of nanoparticles causes a shift in resonance frequency,which were precisely measured for various samples.The drop in resonance frequency is a linear function of nanoparticle concentration,and both methods detect concentrations as low as 150 mg/L of Fe3O4 nanoparticles.
查看更多>>摘要：Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was carried out to determinate the coal bursting liability,and the empirical criterion of coal bursting liability was proposed.Moreover,the machine learning method was applied to coal bursting liability determination.Through analyzing the elastic strain energy release and failure time,the residual elastic strain energy release rate index KRE was proposed to evaluate the coal bursting liability.Accord-ing to the relationship between needle penetration index(NPI),KRE and the critical value of KRE,the Needle Penetration Test-based Empirical Classification Criterion(NPT-ECC)was obtained.In addition,four machine learning classification models were constructed.After training and testing of the models,Needle Penetration Test-based Machine Learning Clas-sification Model(NPT-MLCM)was proposed.The research results show that the accuracy of NPT-ECC is 6.66％higher than that of China National Standard Comprehensive Evaluation(CNSCE)according to verification of the coal fragment ejection ratio F.Gridsearch cross validation-extreme gradient boosting(GSCV-XGBoost)has the best prediction perfor-mance among all the models,and accuracy,Macro-Precision,Macro-Recall and Macro-F1-score of which were 86.67％,88.97％,87.50％and 87.37％.Based on this,the Needle Penetration Test-based GSCV-XGBoost(NPT-GSCV-XGBoost)was proposed.After comparative analysis and discussion,NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability.
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