Applications of microscopy to coke making

A Canadian perspective of the petrographic, thermal rheological and grade of metallurgical coals required to make coke with high strength and strength after reaction (CSR) properties is presented. The development of automated microscopic techniques to obtain reproducible and reliable petrographic data to predict coke quality is discussed. The amount of “altered vitrinite” in the microscopic coke textures has been used as a reference to quantifiy in situ coal oxidation. Relationships between coke microscopy, coal petrography and thermal rheological data show that FSI can be used to estimate the amount of oxidized vitrinite plus petrographic inert contents of coal. Plastic temperature ranges determined from microscopic examination of the coal/coke transformations for Appalachian and Canadian coals show that standard thermal rheological tests underestimate the plastic range for high inertinite coals.     

Relationship between the technical parameters of cokes produced from blends of three Polish coals of different coking ability

The demand for metallurgical coke for blast furnaces is forcing the coking industry to look for new sources of coking coals. The physical and chemical parameters of coals used in coking blends determine the quality (reactivity and strength) of the finished cokes. This study examines the technical properties of the cokes produced from various blends of three Polish coals with different coking. These coals were collected from three mines: Zofiówka, Szczygłowice, and Krupiński (Upper Silesian Coal Basin, Poland). The coal charges were coked in the laboratory scale, at temperatures of up to 1000 °C, in an inert atmosphere. The coke reactivity (index CRI) and the coke strength after reaction (CSR) were measured and correlated to the properties of parent coals using statistical analysis. The result of this study shows strong relationships between the concentration of the best coking coal (Zofiówka) in the blend and the CRI and CSR of the resulting coke. The CRI and CSR parameters for cokes obtained from single coals and from their blends show the additive character. This study also confirms the linear relationship between CRI and CSR parameters of the cokes.     

Coke/carbon reactions in the study of factors affecting coke quality

Pilot-scale and plant cokes with initially different properties were subjected to weight loss by reaction with carbon dioxide, and the resulting products were characterized both microscopically and by ASTM Tumbler tests. The cokes were found to lose strength progressively as weight loss occured, and the examination of carbon forms and microstructures showed the strength changes to be related to the initial properties of the cokes and ultimately to particular compositional characteristics of the coals used to make the cokes. The results have been providing coal-selection guidelines to assist in the manufacture of higher-quality coke. Our coke reactivity test is also being used to signal changes in the coal supply and to monitor certain other coking parameters.     

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

The alkali-extraction test as a reliable method of detecting oxidized metallurgical coal

Because oxidized coal is known to have an adverse effect on bulk-density control and coal handling in the coke plant, and on coke quality, U.S. Steel has developed the alkali-extraction test as a means to identify oxidized (weathered) coal in coal blends. In the test, coal is boiled in a caustic solution so that oxidized coal dissolves and darkens the solution. The solution is then tested for light transmittance, with the solution transmittance decreasing as the coal oxidation increases. A transmission value less than 80% indicates that the coal is too oxidized for metallurgical use. This rejection limit corresponds to 8 to 12% microscopically recognizable oxidized coal. Petrographic determinations of oxidized coal in four ranks of metallurgical coal show a linear relationship (0.964 correlation coefficient) with corresponding transmission values and a detection limit of three percent oxidized coal for the test. In addition, the oxygen content and infrared-band intensities of these coals also show a linear trend with transmission values. The study indicates that the alkali-extraction test is a reliable test for detecting oxidized coal in metallurgical coal blends and is superior to the Free-Swelling Index for setting mining limits in stripping operations. U.S. Steel is presently using the test to monitor captive and purchased coals for acceptance as metallurgical coal.     

Multivariate Modelling of Geometallurgical Variables by Projection Pursuit

The integration of geological and geometallurgical data can significantly improve decision-making and optimize mining production due to a better understanding of the resources and their metallurgical performances. The primary-response rock property framework is an approach to the modelling of geometallurgy in which quantitative and qualitative primary properties are used as proxies of metallurgical responses. Within this framework, primary variables are used to fit regression models to predict metallurgical responses. Whilst primary rock property data are relatively abundant, metallurgical response property data are not, which makes it difficult to establish predictive response relationships. Relationships between primary input variables and geometallurgical responses are, in general, complex, and the response variables are often non-additive which further complicates the prediction process. Consequently, in many cases, the traditional multivariate linear regression models (MLR) of primary-response relationships perform poorly and a better alternative is required for prediction. Projection pursuit is a powerful exploratory statistical modelling technique in which data from a number of variables are projected onto a set of directions that optimize the fit of the model. The purpose of the projection is to reveal underlying relationships. Projection pursuit regression (PPR) fits standard regression models to the projected data vectors. In this paper, PPR is applied to the modelling of geometallurgical response variables. A case study with six geometallurgical variables is used to demonstrate the modelling approach. The results from the proposed PPR models show a significant improvement over those from MLR models. In addition, the models were bootstrapped to generate distributions of feasible scenarios for the response variables. Our results show that PPR is a robust technique for modelling geometallurgical response variables and for assessing the uncertainty associated with these variables.     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.     

Validating and Improving Models for Vibratory Installation of Steel Sheet Piles with Field Observations

Vibratory driving is the most common installation technique for steel sheet pile walls. In practice, the assessment of the feasibility of this installation process is mainly based on rules of thumb, on numerical and empirical models or on experts opinions. In order to improve these prediction methods and formulas, 252 observations from the Dutch engineering practice have been compared with six different types of models. This comparison has been carried out applying the receiver operating characteristic (ROC) curve technique, which is new in geotechnical engineering. This paper introduces the ROC-curve technique to estimate mainly the quality of a model and to be able to optimize parameters and variables in the model. 252 field observations were used to re-examine prediction methods for the minimum required vibration force and to prove the ROC method works. The paper shows this technique is suitable for three purposes: (1) determining the quality of a model, (2) objectively comparing several models to each other, given certain assumptions and (3) for optimizing thresholds within a model. The model with added professionals’ experience proves to perform equally well as the numerical model Hypervib-I.     

Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

This paper describes two artificial intelligence techniques for prediction of maximum dry density (MDD) and unconfined compressive strength (UCS) of cement stabilized soil. The first technique uses various artificial neural network (ANN) models such as Bayesian regularization method (BRNN), Levenberg- Marquardt algorithm (LMNN) and differential evolution algorithm (DENN). The second technique uses the support vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε-insensitive loss function has been adopted. The inputs of both models are liquid limit (LL), plasticity index (PI), clay fraction (CF)%, sand (S)%, gravel Gr (%), moisture content (MC) and cement content (Ce). The sensitivity analyses of the input parameters have been also done for both models. Based on different statistical criteria the SVM models are found to be better than ANN models for the prediction of MDD and UCS of cement stabilized soil.     

Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks

In this paper, three types of artificial neural network (ANN) are employed to prediction and interpretation of pressuremeter test results. First, multi layer perceptron neural network is used. Then, neuro-fuzzy network is employed and finally radial basis function is applied. All applied networks have shown favorable performance. Finally, different models have been compared and network with the most outstanding performance in two stages is determined. Contrary to conventional behavioral models, models based neural network do not demonstrate the effect of input parameters on output parameters. This research is response to this need through conducting sensitivity analysis on the optimal structure of proposed models.     

The use of sulfur to extract hydrogen from coal

The world's recoverable coal reserves contain about 3 × 10¹⁰ tons of hydrogen. The reaction of sulfur vapor with medium-volatile bituminous coal produces hydrogen sulfide in yields up to 97% (based on sulfur), and utilizes 70–75% of the hydrogen from the coal. The conversion of hydrogen sulfide to hydrogen can be effected through commercially proven processes; several laboratory-scale processes could also be scaled up for future use. The solid by-product of the coal–sulfur reaction meets or exceeds specifications for fixed carbon, ash, and friability of conventional metallurgical coke, though produced at lower temperatures than typical by-product coke ovens. A conceptual process is presented in which sulfur is converted to hydrogen sulfide by reaction with coal, the hydrogen sulfide in turn is converted to the desired hydrogen and to sulfur, and the sulfur is recycled through the reactor. The by-product is a good quality coke, but may also have other applications as a carbon material.     

基于岩石地质本质性的碳酸盐岩单轴抗压强度预测

充分认识岩石的地质本质性是准确描述其物理力学特性的桥梁。岩石的地质本质性涵盖了岩石的物质性、结构性和赋存状态3个方面的内容。在综合考虑岩石上述3方面特征及其与单轴试验联系的基础上,以矿物组成、密度、纵波波速和含水状态为基本指标,采用回归和BP神经网络的方法对碳酸盐岩单轴抗压强度进行预测,并采用灰色关联分析法验证本研究所选用的预测基本指标的合理性。实例应用表明：本次采用的回归方法对该类岩石强度预测的最大误差为15.3%,BP神经网络方法预测的最大误差为8.5%。预测误差出现的原因为碳酸盐岩物质组成复杂,所选预测基本指标是实际情况的简化,同时泥灰质岩石所具有的膨胀性也导致实测和预测结果具有一定的差异。     

Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique

The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R²), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.     

An extreme learning machine approach for slope stability evaluation and prediction

This paper presents slope stability evaluation and prediction with the approach of a fast robust neural network named the extreme learning machine (ELM). The circular failure mechanism of a slope is formulated based on its material, geometrical and environmental parameters such as the unit weight, the cohesion, the internal friction angle, the slope inclination, slope height and the pore water ratio. The ELM is proposed to evaluate the stability of slopes subjected to potential circular failures by means of prediction of the factor of safety (FS). Substantial slope cases collected worldwide are utilized to illustrate and assess the capability and predictability of the ELM on slope stability analysis. Based on the mean absolute percentage errors and the correlation coefficients between the original and predicted FS values, comparisons are demonstrated between the ELM and the generalized regression neural network (GRNN) as well as the prediction models generated from the genetic algorithms. Moreover, sensitivity analysis of the slope parameters and the ELM model parameters is carried out based on the two utilized evaluation functions. The time expense of the ELM on slope stability analysis is also investigated. The results prove that the ELM is advantageous to the GRNN and the genetic algorithm based models in the analysis of slope stability. Hence, the ELM can be a promising technique for approaching the problems in geotechnical engineering.     

基于最优加权组合模型及高斯－牛顿法的滑坡变形预测研究

在对最优加权组合理论和高斯－牛顿法优化非线性模型参数的方法研究的基础上,依托于洒勒山滑坡的实际变形监测资料,建立了该滑坡变形预测的3个非线性预测模型：指数模型、Verhulst模型和灰色GM（1,1）模型;利用最优加权组合理论建立了洒勒山滑坡的最优加权组合预测模型,并运用高斯－牛顿法对各单一模型和组合模型的参数进行了优化。通过对比分析得出：组合模型的预测精度高于任何单一模型的预测精度;参数优化后各单一模型的预测精度都有不同程度的提高;参数优化后的组合模型预测精度是最高的。因此,综合运用最优组合理论和高斯－牛顿法处理滑坡预测预报模型,是提高滑坡预测预报精度的行之有效的方法。     

Debris-flow susceptibility analysis using fluvio-morphological parameters and data mining: application to the Central-Eastern Pyrenees

Based on debris-flow inventories and using a geographical information system, the susceptibility models presented here take into account fluvio-morphologic parameters, gathered for every first-order catchment. Data mining techniques on the morphometric parameters are used, to work out and test three different models. The first model is a logistic regression analysis based on weighting the parameters. The other two are classification trees, which are rather novel susceptibility models. These techniques enable gathering the necessary data to evaluate the performance of the models tested, with and without optimization. The analysis was performed in the Catalan Pyrenees and covered an area of more than 4,000 km². Results related to the training dataset show that the optimized models performance lie within former reported range, in terms of AUC, although closer to the lowest end (near 70 %). When the models are applied to the test set, the quality of most results decreases. However, out of the three different models, logistic regression seems to offer the best prediction, as training and test sets results are very similar, in terms of performance. Trees are better at extracting laws from a training set, but validation through a test set gives results unacceptable for a prediction at regional scale. Although omitting parameters in geology or vegetation, fluvio-morphologic models based on data mining, can be used in the framework of a regional debris-flow susceptibility assessment in areas where only a digital elevation model is available.     

苏鲁豫皖四省毗邻地区天然焦特征及燃烧特性

碳达峰碳中和背景下提高资源利用效率尤为重要,然而天然焦长久以来并未得到应有的重视。我国天然焦资源丰富,在苏鲁豫皖四省毗邻地区的煤矿开采中经常发现由于岩浆岩侵入,煤层受到烘烤而变质为天然焦的现象。广泛采集苏鲁豫皖四省毗邻地区天然焦样品,在描述天然焦宏观及显微特征的基础上,对天然焦开展工业分析、地球化学特征、自燃倾向性和焦尘爆炸性等方面的测试与分析,采用美国TA2100热分析仪测试典型样品的燃烧特性。结果表明:天然焦的宏观物理特性、显微组分特性与煤具有明显的差异性;总体上各项分析指标与无烟煤没有明显的界线,天然焦与同地的残留煤相比,其挥发分产率降低、C/H比大幅提高;天然焦变质程度达到或高于无烟煤阶段,不易自燃、焦尘无爆炸性。燃烧特性测试结果表明山东菏泽赵楼天然焦样品具有较强的反应活性,并根据天然焦的特性探讨了利用方向,认为其在制作型煤、合成氨、碳材料、生产水泥以及CO₂地质封存等方面具有利用价值或潜力。      

土的综合结构势及结构性参数研究进展

土结构性是决定土力学特性的一个最为根本的内在因素。基于综合结构势理论的土结构性参数研究是土结构性研究的一种行之有效的方法, 它不仅考虑了土颗粒排列的几何特征和土颗粒联结的力学特征,而且对土结构性进行了综合的、整体的、动态的量化。对已提出的结构性参数进行全面的总结和评述,统一了结构性参数的命名。在此基础上,总结了结构性参数与强度和变形的关系、结构性参数本构模型和结构性参数应用方面的研究现状,并进一步分析了土结构性描述的合理途径以及结构性与固结状态对强度和变形的影响,提出了同一结构性条件下研究结构性土强度、变形本构规律的新观点。     

Prediction of time to slope failure: a general framework

The prediction of time to slope failure (TSF) is a goal of major importance for both landslide researchers and practitioners. A reasonably accurate prediction of TSF allows human losses to be avoided, damages to property to be reduced and adequate countermeasures to be designed. A pure “phenomenological” approach based on the observation and interpretation of the monitored data is generally employed in TSF prediction. Such an approach infers TSF mainly from the ground surface displacements using regression techniques based on empirical functions. These functions neglect the rheological soil parameters in order to reduce the prediction uncertainties. This paper presents an overlook of the methods associated with this approach and proposes a unique expression encompassing most of the previously proposed equations for TSF prediction, thus offering a general framework useful for comparisons between different methods. The methods discussed in this paper provide an effective tool, and sometimes the only tool, for TSF prediction. The fundamental problem is always one of data quality. A full confidence in all assumptions and parameters used in the prediction model is rarely, if ever, achieved. Therefore, TSF prediction models should be applied with care and the results interpreted with caution. Documented case studies represent the most useful source of information to calibrate the TSF prediction models.     

Different Approaches for Simulating Brittle Failure in Two Hard Rock Mass Cases: A Parametric Study

One of the aims of rock mechanics analysis is to predict fallouts in underground excavations. The objective of this paper was to study the relative importance of different strength parameters and their significance on the simulation of brittle failure and fallouts. This work was conducted as a parametric study, using numerical modelling and a number of approaches. The results were compared with observed fallouts. More obvious and distinct shear bands could be observed with decreased element sizes close to, and at, the boundary. The maximum shear strain was the most reliable indicator for fallout prediction. The results of the (instantaneous) cohesion softening friction softening models were sensitive to changes of the peak strength parameters and less sensitive to variations in residual parameters. The result from the cohesion-softening friction-hardening (CSFH) model, when using a peak cohesion equal to the intact rock strength, best captured the observed rock behaviour.