Multiscale analysis of fine slag from pulverized coal gasification in entrained-flow bed

Fine slag(FS)is an unavoidable by-product of coal gasification.FS,which is a simple heap of solid waste left in the open air,easily causes environmental pollution and has a low resource utilization rate,thereby restricting the development of energy-saving coal gasification technologies.The multiscale analysis of FS performed in this study indicates typical grain size distribution,composition,crystalline structure,and chemical bonding characteristics.The FS primarily contained inorganic and carbon components(dry bases)and exhibited a"three-peak distribution"of the grain size and regular spheroidal as well as irregular shapes.The irregular particles were mainly adsorbed onto the structure and had a dense distribution and multiple pores and folds.The carbon constituents were primarily amorphous in structure,with a certain degree of order and active sites.C 1s XPS spectrum indicated the presence of C–C and C–H bonds and numerous aromatic structures.The inorganic components,constituting 90%of the total sample,were primarily silicon,aluminum,iron,and calcium.The inorganic components contained Si–O-Si,Si–O–Al,Si–O,SO_(4)^(2−),and Fe–O bonds.Fe 2p XPS spectrum could be deconvoluted into Fe 2p_(1/2) and Fe 2p_(3/2) peaks and satellite peaks,while Fe existed mainly in the form of Fe(III).The findings of this study will be beneficial in resource utilization and formation mechanism of fine slag in future.

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.

Stylistic Analysis on English On-line Chat

Based on the fact that on-line chat has become the most developing language form in the information age, this article point out the stylistic features of on-line English chat. Though in written language form, such language is spoken language in nature, thus it is worthwhile to analyze this special phaenomenon in lexical and grammatical level.

An image segmentation method of pulverized coal for particle size analysis

An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.

Multi-criteria comparative analysis of the pressure drop on coal gangue fly-ash slurry at different parts along an L-shaped pipeline

Disposing of coal gangue and fly-ash on the surface is a risky method with tremendous potential catastrophic consequences for the environment.Backfill mining is a promising practice for turning those hazardous wastes into functional backfill materi-als.Unfortunately,how to efficiently deliver the slurry to the desired places remains under-researched.To address this issue,the computational fluid dynamics software Fluent was used in the current study in addition to a laboratory rheological test to simulate the impact of various parameters on the evolution of pressure at a particular section of the pipeline.Furthermore,the response surface method was employed to investigate how the various components and their corresponding influencing weights interact to affect the pressure drop.This study demonstrates that the pressure drop of the slurry is highly influenced by slurry concentration,speed,and pipe diameter.While conveying speed is the main component in the bend section,pipe diameter takes over in the horizontal and vertical pipe sections.

A novel stochastic modeling framework for coal production and logistics through options pricing analysis

We propose a novel stochastic modeling framework for coal production and logistics using option pricing theory.The problem of valuing the inherent real optionality a coal producer has when mining and processing thermal coal is modelled as pricing spread options of three assets under the stochastic volatility model.We derive a three-dimensional Fast Fourier Transform(“FFT”)lower bound approximation to value the inherent real optionality and for robustness check,we compare the semi-analytical pricing accuracy with the Monte Carlo simulation.Model parameters are estimated from the historical monthly data,and stochastic volatility parameters are obtained by matching the Kurtosis of the low-ash diff data to the Kurtosis of the stochastic volatility process which is assumed to follow Cox–Ingersoll–Ross(“CIR”)model.

On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

Mechanics Analysis of Overhead Transmission Lines Based On-line Monitoring

At present, the on-line monitoring is widely applied to the power line monitoring. In this paper, a new mechanical calculation model is established according to the on-line monitoring. And this model is based on the parameters that tension sensors and angle sensors on suspended points detect, and combines with the parameters of the wire itself, and also considers the deflection angel of wires due to wind. In this model, mechanics parameters of wires are turned into the new coordinate plane after deflection angel of wires due to wind, or windage yaw plane. A statics tension balance equation is built in the vertical direction of the new windage yaw plane. According to the theoretical analysis and algorithm, we verify the accuracy of this newly developed mechanical calculation model.

Assessment of gas and dust explosion in coal mines by means of fuzzy fault tree analysis

During the past decade, coal dust and gas explosions have been the most two serious types of disasters in China, threatening the lives of miners and causing significant losses in terms of national property. In this paper, an evaluation model of coal dust and gas explosions was constructed based on a fuzzy fault tree by taking the Xingli Coal Mine as a research site to identify the risk factors of coal dust and gas explosions.Furthermore, the hazards associated with such explosions were evaluated for this particular coal mine.After completing an on-site investigation, the fuzzy probabilities of basic events were obtained through expert scoring, and these expert opinions were then aggregated as trapezoidal fuzzy numbers to calculate the degrees of importance of all basic events. Finally, these degrees of importance were sorted. According to the resulting order, the basic events with higher probabilities were determined to identify key hazards in the daily safety management of this particular coal mine. Moreover, effective measures for preventing gas and coal dust explosions were derived. The fuzzy fault tree analysis method is of high significance in the analysis of accidental coal mine explosions and provides theoretical guidance for improving the efficiency of coal mine safety management in a scientific and feasible manner.

Wavelength Dependence in the Analysis of Carbon Content in Coal by Nanosecond 266 nm and 1064 nm Laser Induced Breakdown Spectroscopy

The wavelength dependence of laser induced breakdown spectroscopy （LIBS） in the analysis of the carbon contents of coal was studied using 266 nm and 1064 nm laser radiations. Compared with the 1064 nm wavelength laser ablation, the 266 nm wavelength laser ablation has less thermal effects, resulting in a better crater morphology on the coal pellets. Besides, the 266 nm wavelength laser ablation also provides better laser-sample coupling and less plasma shielding, resulting in a higher carbon line intensity and better signal reproducibility. The carbon contents in the bituminous coal samples have better linearity with the line intensities of atomic carbon measured by the 266 nm wavelength than those measured by the 1064 nm wavelength. The partial least square （PLS） model was established for the quantitative analysis of the carbon content in coal samples by LIBS. The results show that both of the 266 nm and 1064 nm wavelengths are capable of achieving good performance for the quantitative analysis of carbon content in coal using the PLS method.

Wavelet analysis method of harmonics and electromagnetic interference in coal mines

In this study we propose an analytical method based on orthogonal wavelet transforms for detecting harmonic noise and Electromagnetic Interference (EMI) from power supply systems and equipment in coal mines. The method will separate interference from signals through wavelet packet decomposition and then accomplish wavelet packet synthesis towards decomposition results after filtering, to remove harmonic noise and electromagnetic interference. Detailed simulation experiments are presented to study power harmonics and Electrical Fast Transient Burst (EFT/B) interference and to validate the effectiveness of our proposed method. The experimental results show that the proposed method, suitable for mutant and non-stationary signal detection, can accurately analyze harmonic interference and EMI in coal mines, as well as establish EMI source models and perform underground Electromagnetic Compatibility (EMC) prediction analyses.

Extracting the core indicators of pulverized coal for blast furnace injection based on principal component analysis

An updated approach to refining the core indicators of pulverized coal used for blast furnace injection based on principal component analysis is proposed in view of the disadvantages of the existing performance indicator system of pulverized coal used in blast furnaces. This presented method takes into account all the performance indicators of pulverized coal injection, including calorific value, igniting point, combustibility, reactivity, flowability, grindability, etc. Four core indicators of pulverized coal injection are selected and studied by using principal component analysis, namely, comprehensive combustibility, comprehensive reactivity, comprehensive flowability, and comprehensive grindability. The newly established core index system is not only beneficial to narrowing down current evaluation indices but also effective to avoid previous overlapping problems among indicators by mutually independent index design. Furthermore, a comprehensive property indicator is introduced on the basis of the four core indicators, and the injection properties of pulverized coal can be overall evaluated.

Study of a Comprehensive Assessment Method for Coal Mine Safety Based on a Hierarchical Grey Analysis

Coal mine safety is a complex system, which is controlled by a number of interrelated factors and is difficult to estimate. This paper proposes an index system of safety assessment based on correlated factors involved in coal mining and a comprehensive evaluation model that combines the advantages of the AHP and a grey clustering method to guarantee the accuracy and objectivity of weight coefficients. First, we confirmed the weight of every index using the AHP, then did a general safety assessment by means of a grey clustering method. This model analyses the status of mining safety both qualitatively and quantitatively. It keeps management and technical groups informed of the situation of the coal production line in real time, which aids in making correct decisions based on practical safety issues. A case study in the application of the model is presented. The results show that the method is applicable and realistic with regard to the core of a coal mine's safety management. Consequently, the safe production of a mine and the awareness of advanced safe production management is accelerated.

Spatial analysis and evaluation of a coal deposit by coupling AHP & GIS techniques

Nowadays,the evaluation of coal deposits becomes crucial,due to many uncontrollable factors,which affect the energy sector.A comparative evaluation of coal deposits is essential for their hierarchical classification regarding their sustainable exploitation,when compared to other coal deposits or competitive fuels,which may be used as alternative solutions for electricity generation.In this paper,a method for spatial analysis and evaluation of a lignite deposit is proposed,by creating four spatial key indicators via GIS analysis,which are then aggregated by applying a weighted linear combination.The analytical hierarchy process is applied to estimate the relative weights of the indicators,in order to perform a weighted cartographic overlay.Through the synthesis of the indicators,an overall,total spatial quality indicator is calculated.The weighted analysis was shown to be more effective compared to the unweighted one,because it can provide more reliable results regarding the exploitation of the examined lignite deposit.The implementation of GIS-based analytical hierarchy process in spatial analysis and evaluation of lignite deposits,in terms of sustainable exploitation,demonstrates that this method can be extensively applied for evaluating the economic potential of mineral deposits.

Thermal Analysis of Vitamin C Affecting Low.temperature Oxidation of Coal

Simultaneous thermal analysis was used to study the influence of Vitamin C as possible chemical additive inhibiting coal oxidation process at low temperature. Some oxidation characteristics of Vitamin C affecting the coal oxidation were investigated at different heating rates. The TG-DSC data show that the impact of Vitamin C on coal oxidation process can be directly evaluated using ignition temperature and critical temperature. Comparison with the effect of water on coal oxidation shows that Vitamin C is more efficient than water. However, the blank experiment conducted with inert a-Al2O3 also suggests that Vitamin C can decompose at about 200 ℃, which limits the usage of Vitamin C on inhibiting coal oxidation.

Bayesian discriminant analysis for prediction of coal and gas outbursts and application

Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., initial speed of methane diffusion, a consistent coal coefficient, gas pressure, destructive style of coal and mining depth, as discriminating factors of the model. In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations. We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model. Finally, we applied the model to the prediction of coal and gas outbursts in the Yunnan Enhong Mine. Our results coincided completely with the actual situation. These results show that a model of Bayesian discriminant analysis has excellent recognition performance, high prediction accuracy and a low error rate and is an effective method to predict coal and gas outbursts.

Efficiency assessment of coal mine safety input by data envelopment analysis

In recent years improper allocation of safety input has prevailed in coal mines in China, which resulted in the frequent accidents in coal mining operation. A comprehensive assessment of the input efficiency of coal mine safety should lead to improved efficiency in the use of funds and management resources. This helps government and enterprise managers better understand how safety inputs are used and to optimize allocation of resources. Study on coal mine＇s efficiency assessment of safety input was con- ducted in this paper. A C^2R model with non-Archimedean infinitesimal vector based on output is established after consideration of the input characteristics and the model properties. An assessment of an operating mine was done using a specific set of input and output criteria. It is found that the safety input was efficient in 2002 and 2005 and was weakly efficient in 2003. However, the efficiency was relatively low in both 2001 and 2004. The safety input resources can be optimized and adjusted by means of projection theory. Such analysis shows that, on average in 2001 and 2004, 45% of the expended funds could have been saved. Likewise, 10% of the safety management and technical staff could have been eliminated and working hours devoted to safety could have been reduced by 12%. These conditions could have Riven the same results.

A Systematic Analysis of Coal Accumulation Process

Formation of coal seam and coal-rich zone is an integrated result of a series of factors in coal accumulation process. The coal accumulation system is an architectural aggregation of coal accumulation factors. It can be classified into 4 levels： the global coal accumulation super-system, the coal accumulation domain mega-system, the coal accumulation basin system, and the coal seam or coal seam set sub-system. The coal accumulation process is an open, dynamic, and grey system, and is meanwhile a system with such natures as aggregation, relevance, entirety, purpose-orientated, hierarchy, and environment adaptability. In this paper, we take coal accumulation process as a system to study origin of coal seam and coal-rich zone; and we will discuss a methodology of the systematic analysis of coal accumulation process. As an example, the Ordos coal basin was investigated to elucidate the application of the method of the coal accumulation system analysis.

Numerical analysis on coal-breaking process under high pressure water jet

Based on the theory of nonlinear dynamic finite element,the control equation ofcoal and water jet was acquired in the coal breaking process under a water jet.The calculationmodel of coal breaking under a water jet was established;the fluid-structure couplingof water jet and coal was implemented by penalty function and convection calculation.The dynamic process of coal breaking under a water jet was simulated and analyzed bycombining the united fracture criteria of the maximum tensile strain and the maximal shearstrain in the two cases of damage to coal and damage failure to coal.

CS_2 extraction and FTIR & GC/MS analysis of a Chinese brown coal

The abundance of low-rank coals in China, such as bituminous and brown coals, makes studies of their composition and structure of great significance to both coal chemistry research and for efficient utilization of the coal. We describe how a Chinese brown coal was ultrasonically extracted with carbon disulfide （CS2） and the raw and extracted coals were characterized by Fourier Transform Infrared Spectroscopy （FTIR）. The corresponding extract was analyzed with a coupled gas chromatograph mass spectrometer （GC/MS）. The FTIR analysis shows a great abundance of amidocyanogen, carbonyl, aromatic and hetero aromatic rings and single carbon-beteroatom bonds. This suggests the possible occurrence of compounds like alcohols, phenols, amines, esters, carboxylic acids, ethers, aromatics or heteroaromatics. The GC/MS analysis of the CS2 extract detected 62 compounds, among which the non-polar ones were of lower abundance while the polar ones were in higher abundance and were structurally diverse. This demonstrates the compositional and structural complexity of Shengli coal.