Hardgrove Grindability Research Articles

Characteristics of carbonized sludge for co-combustion in pulverized coal power plants

Co-combustion of sewage sludge can destabilize its combustion profile due to high volatility, which results in unstable flame. We carried out fuel reforming for sewage sludge by way of carbonization at pyrolysis temperature of 300–500 °C. Fuel characteristics of carbonized sludge at each temperature were analyzed. As carbonization temperature increased, fuel ratio increased, volatile content reduced, and atomic ratio relation of H/C and O/C was similar to that of lignite. The analysis result of FT-IR showed the decrease of aliphatic C–H bond and O–C bond in carbonization. In the analysis result of TG–DTG, the thermogravimetry reduction temperature of carbonized sludge (CS400) was proven to be higher than that of dried sludge, but lower than that of sub-bituminous coal. Hardgrove grindability index increased in proportion to fuel ratio increase, where the carbonized sludge value of 43–110 was similar or higher than the coal value of 49–63. As for ash deposits, slagging and fouling index were higher than that of coal. When carbonized sludge (CS400) and coal were co-combusted in 1–10% according to calorific value, slagging tendency was low in all conditions, and fouling tendency was medium or high according to the compositions of coal.

Maximum solid concentrations of coal water slurries predicted by neural network models

The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg–Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation.

The Effect of High Power Microwave Energy on the Grindability of Turkish Cayirhan Lignite

In this study, the effect of microwave radiation on grindability of Turkish lignite has been investigated. Turkish lignite (−15 mm, 500 g) was treated by microwave (at a frequency of 2.45 GHz) in a Te010 single mode cavity using a pneumatic system to control the residence time of each sample in the cavity with high-electric-field-strength microwave energy at different power settings (in the range of 5–20 kW) for a short residence time (2 sec). In order to determine the grinding resistance of Turkish lignite samples treated by microwave ovens, the Hardgrove Grindability Test was applied for each treated and untreated sample and also the amount of fine product (−74 μm) was evaluated and compared with each other. Experimental results have shown that significant increases in grindability were achieved when the lignite samples were exposed to microwave radiation. The Hardgrove Grindability Index of samples and the amount of fine product increased up to 123 and 117%, respectively.

An investigation of the grindability of two torrefied energy crops

The process of torrefaction alters the physical properties of biomass, reducing its fibrous tenacious nature. This could allow increased rates of co-milling and therefore co-firing in coal fired power stations, which in turn would enable a reduction in the amount of coal used and an increase in the use of sustainable fuels, without the need for additional plant. This paper presents an experimental investigation of the pulverisation behaviour of two torrefied energy crops, namely: willow and Miscanthus. A multifactorial method approach was adopted to investigate the three process parameters of temperature, residence time and particle size, producing fuels treated using four different torrefaction conditions. The untreated and torrefied fuels were subjected to standard fuel analysis techniques including ultimate analysis, proximate analysis and calorific value determination. The grindability of these fuels was then determined using a laboratory ball mill and by adapting the Hardgrove Grindability Index (HGI) test for hard coals. After grinding, two sets of results were obtained. Firstly a determination similar to the HGI test was made, measuring the proportion of sample passing through a 75 μm sieve and plotting this on a calibrated HGI chart determined using four standard reference coals of known HGI values. Secondly the particle size distributions of the entire ground sample were measured and compared with the four standard reference coals. The standard fuel tests revealed that temperature was the most significant parameter in terms of mass loss, changes in elemental composition and energy content increase. The first grindability test results found that the untreated fuels and fuels treated at low temperatures showed very poor grindability behaviour. However, more severe torrefaction conditions caused the fuels to exhibit similar pulverisation properties as coals with low HGI values. Miscanthus was found to have a higher HGI value than willow. On examining the particle size distributions it was found that the particle size distributions of torrefied Miscanthus differed significantly from the untreated biomass and had comparable profiles to those of the standard reference coals with which they had similar HGI values. However, only the torrefied willow produced at the most severe conditions investigated exhibited this behaviour, and the HGI of torrefied willow was not generally a reliable indicator of grindability performance for this energy crop. Overall it was concluded that torrefied biomass can be successfully pulverised and that torrefied Miscanthus was easier to grind than torrefied willow.

粒状固体材料の粉体物性と粉砕性の関係

Comminution is an important mechanical operation in the many industries that treat partiulate solid materials. The grindability or the grinding resistance (e.g. Work index, Hardgrove grindability index) of the solid material are useful for evaluating the energy efficiency of the grinding processes, the grinding circuits and the grinding machines. It is difficult to measure the grindability from the point of the view of the measuring processes, time or the cost of the testing machine. Measuring powder properties (ex. void fraction, angle of repose) is easy in comparison with determining or grinding resistance.We investigated the relationship between powder property and grindability in a particulate solid aterial. The solid material used was mono-sized quartz particles which were classified by dry and wet creening. The powder properties measured were the void fractions of the particle bed, with and without tapping, and the angle of repose of the particles. The grindability was defined by the mass of ground products less than the feed size per unit of crushing energy. This was determined by the compressive crushing test of the particle bed. We were able to define the correlation between the powder properties tested and the grindability; that is, we found that it was possible to evaluate the grindability or the grinding resistance of a particulate material by measuring the powder properties.

Effects of coal properties on the production rate of combustion solid residue

Abstract The production rates of furnace bottom ash in a pulverized coal-fired power plant were monitored for a two-year period and its variations with respect to coal properties were analyzed. The power plant was originally designed to fire the coal sludge generated from a washing process; however, the coal sludge and its mixture with low-rank bituminous coal have been started to be used as the main fuel with time. The results of the hardgrove grindability measurements have shown that the grinding properties of sludge or its mixtures could not be predicted based on proximate analysis (moisture, ash, carbon and volatile contents); it could only be determined by experiments. The production rate of bottom ash in this particular power plant remained relatively insensitive to the high ash and moisture contents and could be estimated almost only by knowing the calorific value of the source coal. The evaluated dependency was linear.

Prediction of Coal Grindability Based on Petrography, Proximate and Ultimate Analysis Using Neural Networks and Particle Swarm Optimization Technique

In recent years, use of artificial neural networks have increased for estimation of Hardgrove grindability index (HGI) of coals. For training of the neural networks, gradient descent methods such as Backpropagaition (BP) method are used frequently. However they originally showed good performance in some non-linearly separable problems, but have a very slow convergence and can get stuck in local minima. In this paper, to overcome the lack of gradient descent methods, a novel particle swarm optimization and artificial neural network was employed for predicting the HGI of Kentucky coals by featuring eight coal parameters. The proposed approach also compared with two kinds of artificial neural network (generalized regression neural network and back propagation neural network). Results indicate that the neural networks – particle swarm optimization method gave the most accurate HGI prediction.

Hardgrove grindability index prediction using support vector regression

Hardgrove grindability index (HGI) measures the grindability of coal and is a qualitative measure of coal. It is referred to in mining, beneficiation and utilization of coal. HGI of coal depends on the coal composition and there is an interest to predict this property from proximate analysis of coal. In this paper, support vector regression (SVR), a potential machine learning technique is used to develop a non-linear relationship between input proximate analyses of coal with output HGI by training the SVR model with limited measured data and to validate it with the rest of the untrained data. SVR is a promising method and suggests that a smaller data set can be used for training the model than what has been studied earlier using artificial neural network (ANN) techniques, so that the model still validates the remaining data.

Modeling of particle breakage in a smooth double roll crusher

A model has been developed to describe the breakage behavior of particle breakage in a smooth double roll crusher. In addition to this, an empirical relationship has been obtained between Hardgrove Grindability Index (HGI) of material and breakage parameters. The model uses matrix method of breakage process analysis. Experiments were carried out in a laboratory scale smooth double roll crusher using different materials. The model has been verified experimentally and gives product size distribution after breakage which matches with the experimental distribution well within the satisfactory range.

Field-scale investigation of pulverized coal mill power consumption

Twenty field-scale tests were conducted in a 28 MW pulverized coal power plant in Healy, Alaska, to examine mill power consumption in relation to coal grind size. The intent in this field-scale study was to verify if grind size truly impacted power consumption by a detectable amount. The regression model developed from the data indicates that grind size does impact mill power consumption, with finer grinds consuming significantly more power than coarser grinds. However, other factors such as coal hardness (i.e. the lower the Hardgrove Grindability Index, or the harder the coal, the higher the power consumption) and mill throughput (i.e., the higher the throughput, the higher the power consumption) had to be included before the impact of grind size could be isolated. It was also observed that combining amperage and flow rate into a single parameter, i.e., specific amperage, hurt modeling. Cost analysis based on the regression model indicate a power savings of $19,972 per year if the coal were ground to 50% passing 76 {mu}m rather than the industry standard of 70% passing 76 {mu}m. The study also demonstrated that size reduction constituted a significant portion of the power consumption.

Fast test method for the determination of the grindability of fine materials

This paper presents briefly the standard grindability testing methods such as Bond- and Hardgrove process. Then the study shows two relatively fast alternative way for the determination of specific grinding work and Bond work index of brittle materials. The standard laboratory grindability tests can be applied to the grinding of materials in a relatively coarse particle size interval (Bond method: x < 3.36 mm, Hardgrove: x = 0.59–1.19 mm, Zeisel process: x = 0.75–1.00 mm). However, there has been huge industrial demand for the production of different kind of fine materials for few decades. The feed material particle size is generally below 50–100 μm. That is way the reliable mill dimensioning demands the investigation of the grindability of fine materials. One of the main aims of grindability research carried out by the University of Miskolc, Institute of Raw Material Processing and Environment Process Engineering – coordinated by Csőke – is to develop a grindability determination method, mainly for fine materials, by means of the improvement of Hardgrove and Bond measuring methods and devices. Therefore, the Hardgrove- and Bond mills were equipped with torque and power input measuring instruments (torque-meter, electric energy meter). Standard and modified Bond- and Hardgrove tests were carried out systematically with different kind of brittle materials in the standard particle size range. Furthermore, locked-cycle grinding experiments were completed with Universal Hardgrove mill in order to compare the results with the values obtained previously by the “one-step” Hardgrove grindability test. The results proved the reliability of the new modified tests. Thereafter experiment series were performed with fine materials such as alumina and power station coal fly ash. In case of alumina sample the laboratory specific grinding work results were compared with those values of industrial ball mill.

Estimation of Hardgrove grindability index of Turkish coals by neural networks

In this research, different techniques for the estimation of coal HGI values are studied. Data from 163 sub-bituminous coals from Turkey are used by featuring 11 coal parameters, which include proximate analysis, group maceral analysis and rank. Non-linear regression and neural network techniques are used for predicting the HGI values for the specified coal parameters. Results indicate that a hybrid network which is a combination of 4 separate neural networks gave the most accurate HGI prediction and all of the neural network models outperformed non-linear regression in the estimation process.

Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models

The effects of proximate and ultimate analysis, maceral content, and coal rank (Rmax) for a wide range of Kentucky coal samples from calorific value of 4320 to 14960 (BTU/lb) (10.05 to 34.80 MJ/kg) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that the relationship between (a) Moisture, ash, volatile matter, and total sulfur; (b) ln (total sulfur), hydrogen, ash, ln ((oxygen+nitrogen)/carbon) and moisture; (c) ln (exinite), semifusinite, micrinite, macrinite, resinite, and Rmax input sets with HGI in linear condition can achieve the correlation coefficients (R2) of 0.77, 0.75, and 0.81, respectively. The ANN, which adequately recognized the characteristics of the coal samples, can predict HGI with correlation coefficients of 0.89, 0.89 and 0.95 respectively in testing process. It was determined that ln (exinite), semifusinite, micrinite, macrinite, resinite, and Rmax can be used as the best predictor for the estimation of HGI on multivariable regression (R2=0.81) and also artificial neural network methods (R2=0.95). The ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the hardgrove grindability index prediction.

Studies of relationship between petrography and elemental analysis with grindability for Kentucky coals

The effects of macerals, ash, elemental analysis and moisture of wide range of Kentucky coal samples from calorific value of 23.65–34.68 MJ/kg (10,170–14,910 (BTU/lb)) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression method. Two sets of input: (a) macerals, ash and moisture (b) macerals, elemental analysis and moisture, were used for the estimation of HGI. The least square mathematical method shows that increase of the TiO 2 and Al 2O 3 contents in coal can decrease HGI. The higher Fe 2O 3 content in coal can result in higher HGI. With the increase of micrinite and exinite contents in coal, the HGI has been decreased and higher vitrinite content in coal results in higher HGI. The multivariable studies have shown that input set of macerals, elemental analysis and moisture in non-linear condition can be achieved an acceptable correlation, R = 90.38%, versus R = 87.34% for the input set of macerals, ash and moisture. It is predicted that elemental analysis of coal can be a better representative of mineral matters for the prediction of HGI than ash.

Studies of the relationship between petrography and grindability for Kentucky coals using artificial neural network

Although there are several formulas available for predicting Hardgrove grindability of coal, most of them are linear and do not simultaneously take into consideration most of the relevant factors. The artificial neural network is an information processing tool that is capable of establishing an input–output relationship by extracting controlling features from a database presented to the network. In this paper, a neural network approach was proposed to deal with the grindability behavior of coal. 195 sets of experimental data were evaluated with artificial neural network to predict the HGI of Kentucky coals. Two different kinds of the trained artificial neural network were undertaken using the database created in this study. It is shown from the examples that the artificial neural network adequately recognized the characteristics of the coal experimental data sets, retaining a generality for further prediction. It is believed that an artificial neural network based prediction procedure shown in this paper can be further employed for Hardgrove grindability index prediction. The influence of liptinite, vitrinite, ash, and sulfur content on HGI was studied by a parametric study.

Prediction of grindability with multivariable regression and neural network in Chinese coal

Grindability index of coal is usually determined by Hardgrove Grindability Index (HGI). The correlation between the proximate analysis of Chinese coal and HGI was studied. It was found from statistical analysis that, the higher the moisture and the volatile matter content in coal, the less the HGI will be. On the contrary, the higher the ash and the fixed carbon content in coal, the higher the HGI will be. But the correlation between proximate analysis and HGI in coals is nonlinear. The prediction equation of HGI reported in literature, which is based on proximate analysis of coal and linear regression method, is not correct for coals in China. In this paper, the generalized regression neural network (GRNN) method was used to predict the HGI. A higher precision in the prediction result was obtained through such new method. By this method, the HGI can be estimated indirectly from the proximate analysis of coal when the HGI measurement equipment is not available.

Studies of the relationship between mineral matter and grinding properties for low-rank coals

Investigations into the effects of mineral matter content on Hardgrove Grindability Index (HGI) were carried out on some low-rank Turkish coals. Quantitative X-ray diffraction (XRD) analyses were carried out using an interactive data processing system (SIROQUANT™) based on Rietveld interpretation methods. Selective leaching processes were used to determine the water and acid-soluble contents of coal samples. Among the coal seams tested, the HGI values of Elbistan coal samples presented a large range from 39 to 83, whereas Tufanbeyli coal samples ranged from 48 to 69. Treatment of the coal with water, ammonium acetate, and hydrochloric acid showed that a considerable part of the ash-forming inorganic matter occurs in water-soluble, acid-soluble, or ion-exchangeable form. Grindability tests on samples of varied water and acid-soluble content showed a significant effect of water and acid-soluble contents on HGI.

04/00039 Thermogravimetric study on desulfurization of shell: Lu, C.-M. et al. Gongcheg Rewuli Xuebao, 2003, 22, (1), 177–179. (In Chinese)

This chapter presents a few important aspects of briquetting of coal fines. Briquette quality depends on composition, particle sizes, and processing conditions. The chapter presents various data on the influences of such factors on mechanical strength and water resistance of briquettes formed from high rank coals using a molasses/lime binder alone and also including bagasse. These data relate to Hardgrove grindability index (HGI), coal size, moisture, and curing time. The size distribution patterns of coals A and B were found to differ greatly from those of the other coals. In particular, coals A and B contained little fine material below −75 μm whereas the significant quantities of fine material in the other coals were distributed such that the −45 μm fraction exceeded the fraction between −75 μm and +45 μm.

Studies of the relationship between coal petrology and grinding properties

The maceral and microlithotype composition of selected coals has been investigated with respect to the grinding properties, specifically Hardgrove grindability index (HGI), of the coals. The study expands upon previous investigations of HGI and coal petrology by adding the dimension of the amount and composition of the microlithotypes. Coal samples, both lithotypes and whole channels, were selected from restricted rank ranges based on vitrinite maximum reflectance: 0.75–0.80% R max, 0.85–0.90% R max and 0.95–1.00% R max. In this manner, the influence of petrographic composition can be isolated from the influence of rank. Previous investigations of high volatile bituminous coals demonstrated that, while rank is an important factor in coal grindability, the amount of liptinite and liptinite-rich microlithotypes is a more influential factor. In this study, we provide further quantitative evidence for the influence of microlithotypes on HGI and, ultimately, on pulverizer performance.

Effects of moisture and coal blending on Hardgrove Grindability Index of Western Australian coal

Investigations into the effects of moisture and coal blending on Hardgrove Grindability Index (HGI) were carried out on Collie coal of Western Australia. Experiments were conducted in a standard Hardgrove apparatus on four individual Premier seam coals (namely P2, P3, P4 and Hebe) and several blends (namely Hebe/P2, Hebe/P3, Hebe/P4, Hebe/P2/P4) prepared at various blending ratios. The experiments comprised of 5 days of air-drying followed by oven drying. Among the coal seams tested, Hebe showed the highest HGI (58) whereas P4 was the lowest (47). HGI was found to correlate well with residual moisture, with correlation coefficients ranging from 0.5 to 0.9 depending on the type of coal seam or blend. In contrast, moisture measurements on the samples loaded into the HGI apparatus (size 0.600 to 1.180 mm), referred to as the “coarse fraction” showed erratic trends with HGI. The experimental results suggest that no relationship exist between the coarse fraction moisture and HGI. Measured HGI values of binary and ternary blends were found to correspond well with the weighted average values of HGI within ±2 HGI units. This effect was confirmed by a further investigation with a range of 11 binary (P3/Hebe) blends of various proportions.