Multi-scale pore fractal characteristics of differently ranked coal and its impact on gas adsorption

Well-developed pores and cracks in coal reservoirs are the main venues for gas storage and migration.To investigate the multi-scale pore fractal characteristics,six coal samples of different rankings were studied using high-pressure mercury injection(HPMI),low-pressure nitrogen adsorption(LPGA-N2),and scanning electron microscopy(SEM)test methods.Based on the Frankel,Halsey and Hill(FHH)fractal theory,the Menger sponge model,Pores and Cracks Analysis System(PCAS),pore volume complexity(D_(v)),coal surface irregularity(Ds)and pore distribution heterogeneity(D_(p))were studied and evaluated,respectively.The effect of three fractal dimensions on the gas adsorption ability was also analyzed with high-pressure isothermal gas adsorption experiments.Results show that pore structures within these coal samples have obvious fractal characteristics.A noticeable segmentation effect appears in the Dv1and Dv2fitting process,with the boundary size ranging from 36.00 to 182.95 nm,which helps differentiate diffusion pores and seepage fractures.The D values show an asymmetric U-shaped trend as the coal metamorphism increases,demonstrating that coalification greatly affects the pore fractal dimensions.The three fractal dimensions can characterize the difference in coal microstructure and reflect their influence on gas adsorption ability.Langmuir volume(V_(L))has an evident and positive correlation with Dsvalues,whereas Langmuir pressure(P_(L))is mainly affected by the combined action of Dvand Dp.This study will provide valuable knowledge for the appraisal of coal seam gas reservoirs of differently ranked coals.

Model of Mediating Determinants of Time Differences in INDYCAR

The IndyCar series distinguishes itself by providing the same design and operation of the single-seater to its pilots.The difference in times is then attributable to the skills of the drivers,but considering the data from the races could test this assumption.The objective of this work was to establish a trajectory model to predict race times.A cross-sectional,correlational,and explanatory work was carried out with a sample of 18,474 records in the period from 2020 to 2023 of the IndyCar series.The results show that the time span predicts the time differences.In relation to the studies of acceptance of the technology,the adjustment of this to human capacities to explain the time differences in the series of racing cars is discussed.

Characteristics of oxygen consumption of coal at programmed temperatures

Oxygen consumption is an important index of coal oxidation.In order to explore the coal-oxygen reaction,we developed an experimental system of coal spontaneous combustion and tested oxygen consumption of differently ranked coals at programmed temperatures.The size of coal samples ranged from 0.18~0.42 mm and the system heat-rate was 0.8℃/min.The results show that, for high ranked coals,oxygen consumption rises with coal temperature as a piecewise non-linear process.The critical coal temperature is about 50℃.Below this temperature,oxygen consumption decreases with rising coal temperatures and reached a minimum at 50℃,approximately.Subsequently,it begins to increase and the rate of growth clearly increased with temperature.For low ranked coals,this characteristic is inconspicuous or even non-existent.The difference in oxygen consumption at the same temperatures varies for differently ranked coals.The results show the difference in oxygen consumption of the coals tested in our study reached 78.6%at 100℃.Based on the theory of coal-oxygen reaction,these phenomena were analyzed from the point of view of physical and chemical characteristics,as well as the appearance of the coal-oxygen complex.From theoretical analyses and our experiments,we conclude that the oxygen consumption at programmed temperatures reflects the oxidation ability of coals perfectly.

A strategy to significantly improve the classification accuracy of LIBS data:application for the determination of heavy metals in Tegillarca granosa

Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categories of Tegillarca granosa samples consisting of a healthy group;Zn,Pb,and Cd polluted groups;and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry(LIBS)and the newly proposed linear regression classification-sum of rank difference(LRC-SRD)algorithm.As the comparison models,least regression classification(LRC),support vector machine(SVM),and k-nearest neighbor(KNN)and linear discriminant analysis were also utilized.Satisfactory accuracy(0.93)was obtained by LRC-SRD model and which performs better than other models.This demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.

Rapid identification of healthy Tegillarca granosa using laser-induced breakdown spectroscopy and fusion model

Objectives:This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry(LIBS)to quickly identify healthy Tegillarca granosa(T.granosa).Materials and Methods:The sum of ranking differences(SRD)was used to fuse multiple anomaly detection metrics to build the one-class classifier,which was only trained with healthy T.granosa.The one-class classifier can identify healthy T.granosa to exclude non-healthy T.granosa.The proposed method calculated multiple anomaly detection metrics and standardized them to obtain a fusion matrix.Based on the fusion matrix,the samples were ranked by SRD and those ranked lowest and below the threshold were considered to be unhealthy.Results:Multiple anomaly detection metrics were fused by the SRD algorithm and tested on each band,and the final fusion model achieved an accuracy rate of 98.46%,a sensitivity of 100%,and a specificity of 80%.The remaining three single classification models obtained the following results:the SVDD model achieved an accuracy rate of 87.69%,a sensitivity of 90%,and a specificity of 60%;the OCSVM model achieved an accuracy rate of 80%,a sensitivity of 76.67%,and a specificity of 60%;and the DD-SIMCA model achieved an accuracy rate of 95.38%,a sensitivity of 98.33%,and a specificity of 60%.Conclusions:The experimental results showed that the proposed method achieved better results than the traditional one-class classification methods with a single metric.Therefore,the fusion method effectively improves the performance of traditional one-class classifiers when using LIBS to quickly identify healthy substances(healthy T.granosa).