Experimental investigation into the salinity effect on the physicomechanical properties of carbonate saline soil

For engineering structures with saline soil as a filling material,such as channel slope,road subgrade,etc.,the rich soluble salt in the soil is an important potential factor affecting their safety performance.This study examines the Atterberg limits,shear strength,and compressibility of carbonate saline soil samples with different NaHCO3 contents in Northeast China.The mechanism underlying the influence of salt content on soil macroscopic properties was investigated based on a volumetric flask test,a mercury intrusion porosimetry(MIP)test,and a scanning electron microscopic(SEM)test.The results demonstrated that when NaHCO3 contents were lower than the threshold value of 1.5%,the bound water film adsorbed on the surface of clay particles thickened continuously,and correspondingly,the Atterberg limits and plasticity index increased rapidly as the increase of sodium ion content.Meanwhile,the bonding force between particles was weakened,the dispersion of large aggregates was enhanced,and the soil structure became looser.Macroscopically,the compressibility increased and the shear strength(mainly cohesion)decreased by 28.64%.However,when the NaHCO3 content exceeded the threshold value of 1.5%,the salt gradually approached solubility and filled the pores between particles in the form of crystals,resulting in a decrease in soil porosity.The cementation effect generated by salt crystals increased the bonding force between soil particles,leading to a decrease in plasticity index and an improvement in soil mechanical properties.Moreover,this work provides valuable suggestions and theoretical guidance for the scientific utilization of carbonate saline soil in backfill engineering projects.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

The Influence of Rice Husk Ash on Mechanical Properties of the Mortar and Concrete: A Critical Review

Increasing the population and infrastructure in both emerging and developed countries requires a considerable amount of cement, which significantly affects the environment. The primary materials of concrete (‘cement’) production emit a large quantity of CO2 into the environment. Also, the cost of conventional building materials like cement gives motivation to find geopolymer waste materials for concrete. To reduce harmful effects on the environment and cost of traditional concrete substance, alternative waste materials like rice husk ash (RHA), ground granulated blast-furnace (GGBS), fly ash (FA), and metakaolin (MK) can be used due to their pozzolanic behavior. RHA waste material with a high silica concentration obtained from burning rice husks can possibly be used as a supplementary cementitious material (SCM) in the manufacturing of concrete, and its strong pozzolanic properties can contribute to the strength and impermeability of concrete. This review paper highlights a summary of the positive effect of using RHA as a partial substitute for cement in building construction, as well as its optimal inclusion of enhanced mechanical properties like compressive strength, flexural strength, and split tensile strength of mortar and concrete.

Influence of Carbon Content on Element Diffusion in Silicon Carbide-Based TRISO Composite Fuel

The coating layers of Tri-structural Isotropic Particles(TRISO)serve to protect the kernel and act as barriers to fission products.Sintering aids in the silicon carbide matrix variably react with TRISO coating layers,leading to the destruction of the coating layers.Investigating how carbon content affects element diffusion in silicon carbide-based TRISO composite fuel is of great significance for predicting reactor safety.In this study,silicon carbide-based TRISO composite fuels with different carbon contents were prepared by adding varying amounts of phenolic resin to the silicon carbide matrix.X-ray Diffraction(XRD)and Scanning Electron Microscopy(SEM)were employed to characterize the phase composition,morphology,and microstructure of the composite fuels.The elemental content in each coating layer of TRISO was quantified using Energy-Dispersive X-ray Spectroscopy(EDS).The results demonstrated that the addition of phenolic resin promoted the uniform distribution of sintering aids in the silicon carbide matrix.The atomic percentage(at.%)of aluminum(Al)in the pyrolytic carbon layer of the TRISO particles reached its lowest value of 0.55%when the phenolic resin addition was 1%.This is because the addition of phenolic resin caused the Al and silicon(Si)in the matrix to preferentially react with the carbon in the phenolic resin to form a metastable liquid phase,rather than preferentially consuming the pyrolytic carbon in the outer coating layer of the TRISO particles.The findings suggest that carbon addition through phenolic resin incorporation can effectively mitigate the deleterious reactions between the TRISO coating layers and sintering aids,thereby enhancing the durability and safety of silicon carbide-based TRISO composite fuels.

Preparation of activated carbon with low ash content and high specific surface area from coal in the presence of KOH

An activated carbon with ash content less than 10% and specific surface area more than 1 600 m 2 /g was prepared from coal and the effect of K containing compounds in preparation of coal based activated carbon was investigated in detail in this paper. KOH was used in co carbonization with coal, changes in graphitic crystallites in chars derived from carbonization of coal with and without KOH were analyzed by X ray diffraction (XRD) technique, activation rates of chars with different contents of K containing compounds were deduced, and resulting activated carbons were characterized by nitrogen adsorption isotherms at 77 K and iodine numbers. The results showed that the addition of KOH to the coal before carbonization can realize the intensive removal of inorganic matters from chars under mild conditions, especially the efficient removal of dispersive quartz, an extremely difficult separated mineral component in other processes else. Apart from this, KOH demonstrates a favorable effect in control over coal carbonization with the goal to form nongraphitizable isotropic carbon precursor, which is a necessary prerequisite for the formation and development of micro pores. However, the K containing compounds such as K 2 CO 3 and K 2 O remaining in chars after carbonization catalyze the reaction between carbon and steam in activation, which leads to the formation of macro pores. In the end an innovative method, in which KOH is added to coal before carbonization and K containing compounds are removed by acid washing after carbonization, was proposed for the synthesis of quality coal based activated carbon.

Analysis on Variation of Soil Organic Carbon and Total Nitrogen Content and Carbon Storage in the Oasis Cotton Field of Manas River Valley

Objective] The research aimed to study soil organic carbon and total ni-trogen distribution in oasis cotton farmland. [Method] With the oasis cotton field of Manas River Val ey in Tianshan Mountains as the research area and abandoned farmland as a control, the distribution characteristics of soil organic carbon and total nitrogen content in the cotton field of Manas River Val ey in the last 23 years were investigated by using geographic methods. [Result] Presenting vertical distribution, cotton soil organic carbon and total nitrogen content in Manas River Val ey de-creased with the increase of soil depth, and those in 0-30 cm soil layer was sig-nificantly higher than those in soil layer of below 30 cm, while organic carbon stor-age showed the trend of increase. Also in vertical distribution, soil organic carbon and total nitrogen decreased significantly with the increase of soil depth, and soil organic carbon content in abandoned farmland decreased month by month. Howev-er, cotton soil organic carbon storage firstly decreased and then increased in the oasis cotton field that in the early growth of cotton, soil organic carbon in the layers of 0-30 and 30-100 cm decreased to the lowest in the bloom stage, and then or-ganic carbon increased with the reproductive growth of cotton into the later stages. However, due to no input of plant litter in the abandoned farmland, the soil organic carbon storage decreased month by month. There were significantly differences be-tween oasis cotton field and abandoned farmland in organic carbon contents. [Con-clusion] The soil organic carbon content and total nitrogen content in oasis cotton field were significantly higher than those in the abandoned farmland. The soil organ-ic carbon storage increased in the layer of 0-30 cm, while there was no significant change of soil organic carbon and total nitrogen content in the layer of 30-100 cm, which was consistent with the previous study on the distribution characteristics of soil organic carbon and total nitrogen content profile.

Enhancing fly ash utilization in backfill materials treated with CO_(2)carbonation under ambient conditions

The environmental concerns resulting from coal-fired power generation that produces large amounts of CO_(2)and fly ash are of great interest.To mitigate,this study aims to develop a novel carbonated CO_(2)-fly ash-based backfill(CFBF)material under ambient conditions.The performance of CFBF was investigated for different fly ash-cement ratios and compared with non-CO_(2)reacted samples.The fresh CFBF slurry conformed to the Herschel-Bulkley model with shear thinning characteristics.After carbonation,the yield stress of the fresh slurry increased significantly by lowering fly ash ratio due to gel formation.The setting times were accelerated,resulting in approximately 40.6%of increased early strength.The final strength decreased when incorporating a lower fly ash ratio(50%and 60%),which was related to the existing heterogeneous pores caused by rapid fluid loss.The strength increased with fly ash content above 70%because additional C-S(A)-H and silica gels were characterized to precipitate on the grain surface,so the binding between particles increased.The C-S(A)-H gel was developed through the pozzolanic reaction,where CaCO_(3)was the prerequisite calcium source obtained in the CO_(2)-fly ash reaction.Furthermore,the maximum CO_(2)uptake efficiency was 1.39 mg-CO_(2)/g-CFBF.The CFBF material is feasible to co-dispose CO_(2)and fly ash in the mine goaf as negative carbon backfill materials,and simultaneously mitigates the strata movement and water lost in post-subsurface mining.

Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Carbon,Nitrogen,and Sulfur Contents in Marine Phytoplankton Cells and Biomass Conversion

In this study,we isolated and cultured phytoplankton along the coast of China and measured the cellular carbon,nitrogen,and sulfur contents under four temperatures.The results showed that the contents of the cellular elements varied widely among different phytoplankton.We found that temperature is one of the important factors affecting the carbon,nitrogen,and sulfur contents in phytoplankton cells;however,the degree of influence of temperature is different for different kinds of phytoplankton.By measuring the nitrogen content in cells,we found that the C:N ratio indirectly measured in the experiment fluctuated in the range of 3.50-8.97,and the average C:N ratio was 5.52.In this experiment,we accurately measured the cell elemental contents at different temperatures and transformed the cell count results into carbon,nitrogen,and sulfur contents to express the biomass.This method ensures that the contribution of species that are small in number but with a large cell volume in biomass is considered.Moreover,this method comprehensively considers the interspecific differences of species and the uneven distribution of elements in phytoplankton cells,which is of significance in the estimation of marine carbon and nitrogen budget.The distribution of nitrogen content in marine phytoplankton can well indicate the marine eutrophication caused by human activities.Climate change can affect the community structure and element composition of marine phytoplankton,meanwhile marine carbon and nitrogen element can regulate the climate to a certain extent.

Chemical and Thermal Characterization of Cement Mortar Containing Ground Palm Oil Fuel Ash as a Partial Cement Replacement

This study investigates the influence of using ground palm oil fuel ash(G-POFA) from 10%-30% as cement replacement(by weight) on the cement mortar's pH under various curing conditions. These findings were supplemented by thermal gravimetric analysis(TGA). Moreover, the resistance of G-POFA blended cement mortars to water absorption and sorptivity was determined. Further, the k-value test was carried out to explain the pozzolanic and filler behavior of G-POFA and to support the results obtained from TGA. It was found that there was no significant impact of several curing conditions on the pH of mortars. The mortar with 10% G-POFA in replacement of cement(G-POFA-10) exhibited the best resistance against water absorption and sorptivity.

Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

Effect of decarburization annealing temperature and time on the carbon content, microstructure, and texture of grain-oriented pure iron

In this study,the effect of decarburization annealing temperature and time on the carbon content,microstructure,and texture of grain-oriented pure iron was investigated by optical microscopy and scanning electron microscopy with electron-backscatter diffraction. The results showed that the efficiency of decarburization dramatically increased with increasing decarburization temperature. However,when the annealing temperature was increased to 825°C and 850°C,the steel's carbon content remained essentially unchanged at 0.002%. With increasing decarburization time,the steel's carbon content generally decreased. When both the decarburization temperature and time were increased further,the average grain size dramatically increased and the number of fine grains decreased; meanwhile,some relatively larger grains developed. The main texture types of the decarburized sheets were approximately the same: {001}<110> and {112~115}<110>,with a γ-fiber texture. Furthermore,little change was observed in the texture. Compared with the experimental sheets,the texture of the cold-rolled sheet was very scattered. The best average magnetic induction(B_(800)) among the final products was 1.946 T.

Calorific values and ash contents of different parts of Masson pine trees in southern China

Calorific value of plants is an important parameter for evalu- ating and indexing material cycles and energy conversion in forest eco- systems. Based on mensuration data of 150 sample sets, we analyzed the calorific value （CV） and ash content （AC） of different parts of Masson pine （Pinus massoniana） trees in southern China using hypothesis testing and regression analysis. CV and AC of different tree parts were almost significantly different （P〈0.05）. In descending order, ash-free calorific value （AFCV） ranked as foliage 〉 branch 〉 stem bark 〉 root 〉 stem wood, and AC ranked as foliage 〉 stem bark 〉 root 〉 branch 〉 stem wood. CV and AC of stem wood from the top, middle and lower sections of trees differed significantly. CV increased from the top to the lower sections of the tnmk while AC decreased. Mean gross calorific value （GCV） and AFCV of aboveground parts were significantly higher than those of belowground parts （roots）. The mean GCV, AFCV and AC of a whole tree of Masson pine were 21.54 kJ/g, 21.74 kJ/g and 0.90%, re- spectively. CV and AC of different tree parts were, to some extent, cor- related with tree diameter, height and origin.

Study on Direct Synthesis of Diphenyl Carbonate with Heterogeneous Catalytic Reaction (VI) Effect of Sn Loading Method and Content on Activity of Sn-Pd Supported Catalyst

The compound metal oxide LaxPbyMnzO used as support was prepared by the sol-gel method, and the catalyst in which Pd was used as active component and Sn as co-active component for direct synthesis of diphenyl carbonate (DPC) with heterogeneous catalytic reaction was obtained by co-calcination and precipitation respectively.The catalyst was characterized by XRD, SEM and TEM respectively. The specific surface area of catalysts was measured by ChemBET3000 instrument, and the activity of the catalysts was tested by the synthesis of DPC in a pressured reactor. The results showed that when the co-active component Sn was added by co-calcination method A, its loading content was equal to 14.43% and active component Pd was loaded by precipitation, the yield and selectivity of DPC could reach 26.78% and 99% respectively.

Influences of Carbon Content on the Properties and Microstructure of Ultrafine WC-10Co Cemented Carbide

WC-10Co nanocrystalline composite powders prepared by spray pyrogenation-continuous reduction and carburization technology were consolidated by vacuum sintering plus hot isostatic pressing （HIP）. Influences of carbon content on properties and microstructure of ultrafine WC-10Co cemented carbide were investigated. The results show that the relative density of the ultrafine WC-10Co cemented carbides can reach 99.72%, and the transverse rupture strength （TRS） was higher than 3 890 MPa, Rockwell A hardness （HRA） was higher than 92.5, the average grain size was less than 460 nm, when carbon content in nanocrystalline composite powder was 5.54wt% and the ball-milled time was 48 hours, ultrafine WC-10Co cemented carbide with excellent properties and homogeneous microstructure was obtained.

STUDY ON THE VARIATION MECHANISM OF CARBON CONTENT OF LIQUID IRON IN MELTING GASIFIER

This paper studied the changing principles of carbon content in direct reduction iron (DRI) and liquid iron in the COREX melting gasifier. Under the normal working conditions of experimental equipment, liquid nitrogen was poured into the melting gasifier from its tuyere to cool down quickly. And then seven cross sections were made to study the carburization reaction and its characteristics of the solid iron and the liquid iron, and also the reaction of carbon between the slag and the metal. According to the results, the influences of the thickness of the semi-coke layer and the temperature on the carbon content of liquid iron in the COREX melting gasifier were confirmed.

Stratal Carbonate Content Inversion Using Seismic Data and Its Applications to the Northern South China Sea

On the basis of the relationship between the carbonate content and the stratal velocity and density, an exercise has been attempted using an artificial neural network on high-resolution seismic data for inversion of carbonate content with limited well measarements as a control. The method was applied to the slope area of the northern South China Sea near ODP Sites 1146 and 1148, and the results are satisfaetory. Before inversion calculation, a stepwise regression method was applied to obtain six properties related most closely to the carbonate content variations among the various properties on the seismic profiles across or near the wells. These include the average frequency, the integrated absolute amplitude, the dominant frequency, the reflection time, the derivative instantaneous amplitude, and the instantaneous frequency. The results, with carbonate content errors of mostly ±5 % relative to those measured from sediment samples, show a relatively accurate picture of carbonate distribution along the slope profile. This method pioneers a new quantitative model to acquire carbonate content variations directly from high-resolution seismic data. It will provide a new approach toward obtaining substitutive high-resolution sediment data for earth system studies related to basin evolution, especially in discussing the coupling between regional sedimentation and climate change.

Modeling of the Unburned Carbon in Fly Ash

Numerical simulation of the content of unburned carbon in fly ash on the 300MW tangentially pulverized coal fired boiler is performed by the numerical simulation software COALFIRE, which is based on international advanced TASCFLOW software platform. Firstly, take the result of calculation of number value as the sample, and then set up the support vector machine model of unburned carbon content on the boiler. The relative error between the predicted output and measured value is 0.00186%, which proves the modeling is good for the unburned carbon in fly ash predict.

Application of a Method for Calculating the Organic Carbon Content by Well Logs to Faulted Basins

The source rock model used in this project was developed by French Petroleum Research Institute. The total organic carbon content was estimated primarily and directly by using continuous conventional logging curves (such as sonic and resistivity curves), which are calibrated through the laboratory analysis data of organic carbon of cores, cuttings or sidewall cores. Regional evaluations have been carried out in downwarping basins abroad. The Haila′er Basin is a faulted basin and the evaluation of such a basin is a new subject. On the basis of a regional evaluation method for the downwarping basins, a new method suitable to faulted basins was developed. The effect is satisfactory when this new method is applied to the Wu′erxun Sag and the Bei′er Sag.

基于Stacking算法集成学习的页岩油储层总有机碳含量评价方法

总有机碳含量(TOC)是页岩油储层评价的重要参数,而传统总有机碳含量测井评价方法精度较低且普适性较差,机器学习模型在一定程度上提高了总有机碳含量预测精度,但结果存在不稳定性。为了进一步提高页岩油储层总有机碳含量预测精度,基于有机质岩石物理特征和不同总有机碳含量测井响应特征的深入分析,优选出深侧向电阻率、声波时差、补偿中子和密度测井曲线作为总有机碳含量的敏感测井响应,并将其作为输入特征,以岩心分析总有机碳含量作为期望输出值,分别建立了决策树模型、支持向量回归机模型、BP(Back Propagation)神经网络模型,并建立了以决策树模型为基模型、支持向量回归机模型为元模型的Stacking算法集成学习模型。利用B油田A区块的岩心样本数据和实际井数据对不同模型预测总有机碳含量结果进行了验证,结果表明,基于Stacking算法的集成学习模型的总有机碳含量预测精度最高,相较于决策树模型、支持向量回归机模型、BP神经网络模型和改进的ΔlgR法,预测精度有较大提高。因此,基于Stacking算法的集成学习模型为该研究区最有效的总有机碳含量计算方法,这为准确地评估页岩油储层的生烃潜力、确保页岩油储层的高效开采及资源利用奠定了基础。