Calorific Value Of Coal Research Articles

Characteristics of polycyclic aromatic hydrocarbon release during spontaneous combustion of coal and gangue in the same coal seam

Based on the influence of temperature, coal quality, oxygen supply and other factors, the formation characters of PAHs during spontaneous combustion of coal and gangue in the same coal seam were experimentally studied. The results showed that the amount of PAHs released from coal and gangue first increased and then decreased with the rising of coal and gangue spontaneous combustion temperature. And it reached a peak value about 5.76–7.17 times of the minimum value at 400 °C–500 °C. The types of PAHs released during the high temperature stage of spontaneous combustion were up to 9. The release amount of PAHs generally increased with the rising of the volatile content, sulfur content, carbon content, fixed carbon content and calorific value of coal and gangue, but decreased with the rising of ash content. The amount of PAHs produced by spontaneous combustion of raw coal was generally higher than that of gangue, and the difference was more obvious at high-temperature stage. The total toxicity amount of PAHs produced by spontaneous combustion of raw coal was higher than that of gangue, up to 9.93 times. Its law of change with temperature was consistent with the total amount of PAHs, which increased and then decreased with the rising of temperature; and reached the peak at 300 °C–400 °C with a peak value of 6.72–14.60 times of the minimum value.

The Influence of Coal Calorific Value on Power Plant Boiler Operation

The Influence of Coal Calorific Value on Power Plant Boiler Operation

Rapid Determination of the Gross Calorific Value of Coal Using Laser-Induced Breakdown Spectroscopy Coupled with Artificial Neural Networks and Genetic Algorithm

Online measurement for the gross calorific value (GCV) of coal is important in the coal utilization industry. This paper proposed a rapid GCV determination method that combined a laser-induced breakdown spectroscopy (LIBS) technique with artificial neural networks (ANNs) and genetic algorithm (GA). Input variables were selected according to the physical mechanism and mathematical significance to improve the prediction of the ANN. GA was applied to determine an optimal architecture for the network instead of a trial and error method. As a result, the mean standard deviation (MSD) of the GCV for four prediction set samples is 0.38 MJ/kg in 50 trials (repetitions of training the ANN with the same input data but different random initial weights and biases), proving that the ANN model is able to provide high modeling repeatability in the GCV analysis. The mean absolute error (MAE) of the GCV for the prediction set is 0.39 MJ/kg. The result meets the requirements (0.8 MJ/kg) for coal online analyses using the n...

Chemical demineralization of high ash Indian coal by using alkali and acid solutions

In the present work an attempt have been made to upgrade the low grade Indian coal to high grade coal by using caustic (NaOH) leaching followed by acid (H2SO4) treatment. The experimental works aimed at a selective attack on in situ MCL (Mahanadi Coal Limited) coal, Odisha, India with some fluxing agents like NaOH and H2SO4. The leaching experiments were carried out in a stainless steel batch reactor of 2.5L capacity with controlled heating facility. Initially, the coal sample with −16+100mesh particle size was leached with 50–150g/L of NaOH solution at 100°C for one hour and then subjected to leach further with 20% sulfuric acid. The maximum demineralization from the level of 26% ash with NaOH (100g/L) leaching 27.5% is achieved and with combined chemical treatment method (100g/L NaOH followed by 20% H2SO4) was obtained 48% effectively. In addition, the results showed that the percent of demineralization improved adequately by decreasing the particle size also with rise of the leaching time and temperature. Coal characterization of raw and treated coal was done by Scanning Electron Microscopy (SEM)/Energy Dispersive Spectroscopy (EDS) analysis and also showed that the calorific value of coal was increased with increase the percent of demineralization of coal.

Domestic Bituminous Coal's Calorific Value Trend Analysis (2010∼2014) and Carbon Emission Factor Development

Domestic Bituminous Coal's Calorific Value Trend Analysis (2010∼2014) and Carbon Emission Factor Development

基于GA-BP的煤质发热量在线监测

电力用煤由于受到产地、运输和价格等多种因素影响，种类变化繁多，煤质发热量有很大的变化。这对锅炉的安全经济运行有很大的影响。为了及时了解机组的运行状况，本文应用反向建模思想，结合遗传算法优化的BP神经网络，模拟出锅炉运行过程中易测量的参数值和煤质发热量的关系，构建了煤质发热量在线测量模型。用电厂运行数据进行模型验证，精度满足工程要求，切实可行。 Due to a variety of factors, such as the origin, transport and price, the types of steam coal for power changed in a large range. Thus coal calorific value varied widely. It has a great impact on the safe operation of the boiler. In order to keep abreast of the unit’s operating conditions, this thesis applied reverse modeling ideas, combined with Genetic Algorithm optimizing BP neural network to simulate the relationship between the parameter values easily measured in the process of boiler operation and coal heat, and constructed an online coal calorific value monitoring model. The result of model validation with plant operation data showed that the accuracy met the engineering requirements, and the model was practical.

基于状态观测器的软测量动态补偿方法

荧光显微图像复原有着很多重要的应用，例如，天文成像，电子显微镜成像，单光子发射计算机断层成像术和正电子发射断针对采用机理分析方法得到的煤发热量存在响应速度迟缓、变负荷时动态误差大的问题，提出一种基于状态观测器的动态补偿方法。依据单元机组负荷–压力模型，由汽包压力和汽轮机一级压力构造锅炉总热量信号，采用一高阶惯性环节描述锅炉给煤量与总热量信号之间的动态关系。针对此高阶惯性环节设计状态观测器，得到具有较小惯性的中间状态信号，利用此信号除以给煤量得到煤发热量信号。实验结果表明：动态补偿后的煤发热量具有抗扰动性好、响应速度快的优点。 The coal calorific value calculated by using the method of mechanism analysis has slow response speed and big dynamic error under variable load conditions. In order to solve this problem, a dy-namic compensation method based on state observer is proposed. Based on the load-pressure model of unit, the total heat input into the boiler is constructed by the drum pressure and the tur-bine primary pressure. The dynamic relationship between the boiler fuel flow and the total heat is described by a high order inertia link. By designing a state observer for the high order inertia link, the intermediate state signal with smaller inertia is obtained, which is divided by the fuel flow to gain the signal of the coal calorific value. The signal of coal calorific value has small dynamic error under variable load conditions, Experiments show that: the signal of coal calorific value has better behaviors on disturbance restraint and dynamic response.

Pyrolisator Coal to be Cokes (Coal Cokes) Casting Metal Industry Standard

Pyrolisis of coal is partial combustion to reduce total moisture, volatile matter and sulfur contens and increase the calorific value of coal. The results of pyrolysis of coal is coke. At the laboratory level studies, pyrolisis done in batch using different calorie, namely 5800, 6000, 6300 kcal/kg and a time of 15-60 minutes and the temperature 400-800°C. Maximum results obtained total moisture (0.44%), fixed carbon (89%), volatile matter (2.4%), sulfur content (undetected) and ash (7.2%). Then applied to the scale miniplant with continuous processes using multitube pyrolisator which are designed to operate in the temperature range 400-800°C and a flow rate of 240-730 kg/h, obtained coal cokes that meets industry quality standards, namely TM (0.42%), FC (90.40%), VM (2.16%), S (not detected), Ash (6.8%) incalori 6300 kcal/h, a flow rate of 240 kg / h and temperatures between 600-700°C

A novel model for cost performance evaluation of pulverized coal injected into blast furnace based on effective calorific value

The combustion process of pulverized coal injected into blast furnace involves a lot of physical and chemical reactions. Based on the combustion behaviors of pulverized coal, the conception of coal effective calorific value representing the actual thermal energy provided for blast furnace was proposed. A cost performance evaluation model of coal injection was built up for the optimal selection of various kinds of coal based on effective calorific value. The model contains two indicators: coal effective calorific value which has eight sub-indicators and coal injection cost which includes four sub-indicators. In addition, the calculation principle and application of cost performance evaluation model in a Chinese large-scale iron and steel company were comprehensively introduced. The evaluation results finally confirm that this novel model is of great significance to the optimal selection of blast furnace pulverized coal.

Online Estimation of Coal Calorific Value from Combustion Radiation for Coal-Fired Boilers

An online coal calorific value prediction method through combustion radiation monitoring for industrial boilers was presented. Multiband combustion radiation signals in visible, infrared, and ultraviolet ranges were monitored. Multi-scale variables were extracted from the signals as flame radiation features in time and frequency domains. Principal component analysis (PCA) was used to eliminate information redundancy and noise disturbance. Correlation between obtained key principal components and the coal calorific value was established by training a support vector regression (SVR) model. Particle swarm optimization (PSO) and genetic analysis (GA) methods were used to search for the best SVR construction parameters. Performance test results showed that the optimized PCA+SVR model-based coal calorific value prediction results had a mean error of 110.9 kcal/kg relative to the lab analysis results, while the standard deviation (STD) was 151.9 kcal/kg. In order to reveal dynamic correlations among the multi-scale feature variables, dynamical principle component analysis (DPCA) was further used. Good consistence was obtained between the coal calorific values predicted by the DPCA+SVR model and the lab analysis results. The mean absolute error and the STD of the coal calorific values predicted by the DPCA+SVR model were diminished to 98.0 kcal/kg and 129.4 kcal/kg, respectively. The presented online coal calorific value monitoring system is highlighted by its low cost, easy installation, robustness to harsh application environment, and is expected to supply meaningful data for coal fired combustion process diagnostics and help realize more reliable regulation of the combustion process.

A Method for Combustion Calculation Based on Proximate Analysis and Net Calorific Value of Coal

For the combustion calculation of coal fired boiler, a new concept of oxygen consumption for combustible material was proposed which was different from the traditional oxygen consumption for elements. A mathematical model was developed for the combustion calculation. The calculation formulas of theoretical air and flue gas volume were established through reasonable hypothesis and simplification, which were based on proximate analysis and net calorific value of coal. In contrast to the results of the elemental analysis for the 15 different kinds of coals which are typical in China, the average relative error of formulas is lower than 2%, the biggest relative error is 7.11%, which can be used for the control over boiler operation, testing and debugging, and calculation of heat transfer.

Development and Application of A Coal Combustion Life Cycle On-line Monitoring System

In China, coal quality fluctuates and deviates greatly from the original design in many large-scale coal-fired power plants. To maintain a secure level and combustion efficiency, a “coal combustion life cycle online monitoring system” (CLMS) has been developed and installed on a 215MW boiler in China. Ten temperature detectors have been installed at the heights of three-layer burners along the furnace. To improve the accuracy of measuring unburnt carbon in fly ash, an adjustable microwave test facility was established. An on-line measurement device for unburnt carbon in ash has been newly developed and attached to the convection flue gas pass. To optimize combustion processes, a real-time neural network prediction model for coal calorific value has been developed. By calculating and analyzing the data in computer workstations, safety evaluation of burner ignition, economic analysis and energy loss analysis can be obtained in real time. As for fluctuation in coal quality, application of the system has improved boiler efficiency more than 1.0%, and reduced NOx by ~10–15%.

Comparative study of regression modeling methods for online coal calorific value prediction from flame radiation features

In this paper, multiple regression methods are presented and compared for online coal calorific value prediction from multi-spectral flame radiation features. Several statistical approaches including principle component analysis (PCA), independent component analysis (ICA) and partial least squares analysis (PLSA) were used in linear and nonlinear regression analyses. Analyzing results show that nonlinear regression model can better approximate the relationship between the coal calorific value and the flame radiation features than linear regression model. In linear regression analysis, the performance of the linear coal calorific value prediction models was not improved by involving the statistical approaches. In nonlinear regression analysis, however, the performance of the prediction models was significantly improved when combined with the statistical approaches. The variation of coefficients of multiple regression showed that only the PLSA-based nonlinear regression model can discriminate useful feature components from useless feature components. The PLSA-based nonlinear regression model showed the best performance for coal calorific value prediction with the number of features reduced to about a third of that in the other models. With the PLSA-based nonlinear regression model, online coal calorific value prediction from the multi-band flame radiation features under the operating conditions used by the industrial boiler has the mean absolute error, standard deviation of the absolute errors, mean relative error and standard deviation of the relative errors of 148.76kJ/kg, 291.86kJ/kg, 0.76% and 1.53%, respectively.

Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network

The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including hidden neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale.

Effect on Coal Physicochemical Properties from Added Foreign Chemicals

Coal additives can promote coal burning and improve the utilization of coal, thus effectively saving coal resources. Coal additive was found to improve coal calorific value obviously by the tests of coal calorific value with two kinds additives, the maximum of calorific value could be increased by 19% , which indicated that the additives promoted coal combustion . By comparing the experimental data , when added directly , it was the most efficient to improve combustion heat for the dose group of 1% coal formulations. while immersed added, it was 0.5% dose formulations to improve coal combustion heat the most efficiently. The effect of immersed added was better than that of added directly. Through SEM micrographs analysis of coal sample experiments, surface roughness and angle were outstanding for not added coal additives. There are similar in shape of pulverized coal comparing the direct added additive with not added additive. The morphology of coal samples changed while dipping additives on them, coal was partially eroded , spherical trend , which increased the surface area, it was helpful for burning.

Study of the correlation between the coal calorific value and coal ash content using X-ray fluorescence analysis

In this paper we have studied the possibility of determining the chemical elements in coal samples using X-ray fluorescence analysis and have found a relationship between the coal calorific value and its ash content with the coal moisture accounting. The amount of coal ash can be determined by the content of the basic chemical elements, such as Si, Sr, Fe, and Ca. It was concluded that the calorific value of coal can be estimated from the ash content in coal without the calorimetric measurements. These correlation coefficients were calculated for coal from several coal mines in Mongolia. The results are in good agreement with the results of chemical analysis.

A robust framework to predict mercury speciation in combustion flue gases

Mercury emissions from coal combustion have become a global concern as growing energy demands have increased the consumption of coal. The effective implementation of treatment technologies requires knowledge of mercury speciation in the flue gas, namely concentrations of elemental, oxidized and particulate mercury at the exit of the boiler. A model that can accurately predict mercury species in flue gas would be very useful in that context. Here, a Bayesian regularized artificial neural network (BRANN) that uses five coal properties and combustion temperature was developed to predict mercury speciation in flue gases before treatment technology implementation. The results of the model show that up to 97 percent of the variation in mercury species concentration is captured through the use of BRANNs. The BRANN model was used to conduct a parametric sensitivity which revealed that the coal chlorine content and coal calorific value were the most sensitive parameters, followed by the combustion temperature. The coal sulfur content was the least important parameter. The results demonstrate the applicability of BRANNs for predicting mercury concentration and speciation in combustion flue gas and provide a more efficient and effective technique when compared to other advanced non-mechanistic modeling strategies.

Estimation of Calorific Value of Coals Using BP and Elman Neural Networks

Coal quality analysis is an important part in the fuel management of power system. The coal calorific value (CCV) is the basis to appraisal of coals heat balance calculation, coal consumption, heat efficiency and improving of heat utilization. There exist many methods on estimation of CCV. Artificial neural networks have many advantages in this area. This paper described the network structure, the mathematical model and the algorithm flow of BP neural network (BPNN) and Elman neural network (ENN) which are both used for the CCV estimation. Results show that both BPNN and ENN can well reflect the non-linear relationship between CCV and other factors of coal quality. And ENN is more accurate to predict CCV, with smaller absolute error.

Geochemical Characteristics and Comprehensive Utilization of the Main Coal Seams in Wubu Mining Area, Northern Shaanxi Province

Use a variety of coal analysis methods on the Wubu mining area main coal seams S1 and T1 in proximate analysis, elemental analysis, and technologic property analysis, the results show that the main coal seams is a kind of medium ash, medium volatile coal, especially low-medium sulfur, high calorific value coal. They have a good chemical properties and technologic properties, and can be used for coking, civilian, steam coal.

Effects of nano-TiO2 on combustion and desulfurization

Nanosized titanium oxide powder was prepared via the sol–gel process and characterized by transmission electron microscope. The effects of nano-TiO2 on combustion characteristics of lignite, desulfurization in combustion and the properties of ashes were investigated. The calorific value of coals and the fusion point of the coal ashes were measured by calorimeter and ash fusion point determination meter; the components of coal ashes and the contents of combustible matters in ash were determined by chemical methods; the pore-size distribution and specific surface area of the coal ash were analyzed by surface area analyzer. A thermogravimetric analyzer was used to investigate the effect of nano-TiO2 on combustion. The results showed that the calorific value of the coal and the fusion temperature of the coal ash were lowered by adding CaO, while on the other hand adding nano-TiO2 to coal increased the calorific value and the melting temperature effectively. Meanwhile, the coal combustion efficiency and desulfurization in combustion could be effectively improved by the co-action of TiO2.