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.

Accuracy improvement of quantitative analysis of calorific value of coal by combining support vector machine and partial least square methods in laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) is a potential technology for online coal property analysis,but successful quantitative measurement of calorific value using LIBS suffers from relatively low accuracy caused by the matrix effect.To solve this problem,the support vector machine(SVM) and the partial least square(PLS) were combined to increase the measurement accuracy of calorific value in this study.The combination model utilized SVM to classify coal samples into two groups according to their volatile matter contents to reduce the matrix effect,and then applied PLS to establish calibration models for each sample group respectively.The proposed model was applied to the measurement of calorific values of 53 coal samples,showing that the proposed model could greatly increase accuracy of the measurement of calorific values.Compared with the traditional PLS method,the coefficient of determination(R2) was improved from 0.93 to 0.97,the root-mean-square error of prediction was reduced from 1.68 MJ kg-1 to1.08 MJ kg-1,and the average relative error was decreased from 6.7% to 3.93%,showing an overall improvement.

Numerical simulation of UCG process with the aim of increasing calorific value of syngas

The determination of operational parameters in the underground coal gasification(UCG)process should be considered in two aspects:first,the total coal in each UCG panel must be gasified and second,the calorific value of the produced gas should be acceptable.The main aim of this study is to present a model that meets these aspects and increasing the calorific value of syngas during this process.In order to achieve those aims,eight different increasing scenarios were devised for total gasification of coal per panel.These scenarios included:increasing oxygen injection rate(scenario 1),the amount of steam injection(scenario 2),operation time(scenario 3),cavity pressure(scenario 4),increase operation time and cavity pressure simultaneously(scenario 5),increase steam injection speed and oxygen injection rate simultaneously(scenario 6),increase in cavity pressure,operating time,steam injection rate and oxygen injection rate simultaneously(scenario 7)and also simultaneous increase in the operating time and steam injection rate(scenario 8).The results showed that for producing syngas with a higher calorific value,the following parameters had the most positive effects respectively:operation time,cavity pressure,steam injection rate and oxygen injection rate.Finally,the model validation was performed for the Centralia LBK-1 UCG pilot and the results showed that this model is very close to reality.

Demineralization mechanism and influence of parameters on high ash Indian coal by chemical leaching of acid and alkali solution

The current research was investigated the mechanism of coal demineralization and the effect of leaching parameters on high ash coal and study the characterization of pre and post-treated coal. The two high ash Indian coal selected from Mahanadi Coalfield Limited, Odisha, pulverized to 375, 230 and 180 gm particle size were undergone simultaneous acid and alkali treatment at a different concentration, temperature and time. The percent demineralization was increased with decrease the size of the particle and rises with leaching parameters. The investigation suggested 180 μm particle size offers efficient demineralization for both coals at 30% NaOH and 30% H2SO4 concentration. The alkali leaching leads to obtaining the demineralization 46% and 42% whereas acid treatment resulted in 34% and 32% of the original coal samples. The extent of demineralization was improved the calorific value of coal. Besides, the degree of demineralization was proved from the FTIR, XRF and FESEM-EDX analysis results. FTIR analysis result showed that the peak intensity of mineral band decreased by the leaching effect and the degree of demineralization was significantly obtained to large extent by the X-ray Fluorescence spectrometer; which elucidates major minerals removed from coal by the leaching effect of acid and alkali solution.

Characterization of Coal Spoil Recovered from Coal Mine Drainage Water

Barapukuria Coal Mine situated in the district Dinajpur. Bangladesh is playing an important role in the economy of this country by the mining of top quality coal. With coal mining, mine waste is also generated called coal spoil. Coal spoil can impose environmental threat if not treated carefully. In contrast, it can also be converted to value added product. In the present work, coal spoils collected from Barapukuria coal mine drainage water were investigated to determine the quality of the samples by physico-chemical analysis (proximate and ultimate analysis) as well as by heating value determination. 50% of carbon was detected in the samples after elemental analysis, with sulfur content less than 0.4%. Calorific value around 9300 btu/lb was obtained for the coal spoil. Moreover, moisture content, ash, volatile matter content and fixed carbon also provided fruitful information regarding the quality and economic prospect of the samples in comparison to the quality of Barapukuria coal.

基于仿生算法联合优化BP的燃煤发热量预测

【目的】燃煤发热量的精准预测和评价是煤质分析和热工计算的重要基础,目前神经网络预测燃煤发热量的模型虽能有效拟合非线性关系,但存在易陷入局部最小值、收敛速度慢等问题。【方法】为精准预测煤炭在工业锅炉燃烧过程中的发热量,提出一种由仿生算法FA-GA联合优化BP神经网络的燃煤发热量预测方法。将774组燃煤锅炉常用煤的工业分析及元素分析数据进行预处理,根据平均影响值对煤质指标进行特征变量筛选,最终建立FA-GA-BP的发热量预测模型,并从误差评价指标、迭代次数等方面对优化算法寻优能力和模型预测精度进行了检验。【结果】经过特征变量筛选后模型的预测精度提升至0.956 1;FA-GA联合算法与单一优化算法FA、GA、PSO相比,迭代次数显著减少,算法的全局搜索能力得到有效提升;FA-GA-BP模型与单一优化模型FA-BP、GA-BP、PSO-BP以及目前常用的发热量模型MLR和SVR相比,精度更高,相关系数可达0.984 5.【结论】FA-GA算法优化BP模型,针对燃煤锅炉中来自不同地区、不同煤种的发热量预测具有良好的效果,在理论上满足了工业误差要求。改进后的燃煤发热量预测模型可为有效监测入炉煤质实时变化提供一种新方法。

添加剂对废旧冰箱聚氨酯泡沫燃烧特性的影响研究

以废旧冰箱聚氨酯泡沫(PUF)为原料,选取玉米秸秆(CS)、木屑(WS)、煤粉(PC)作为添加剂制备成型燃料,采用热重分析仪和氧弹量热仪研究3种添加剂对PUF成型燃料燃烧性能及热值的影响,并采用Coats-Redfern积分法研究其动力学行为。结果表明:3种添加剂会使固定碳燃烧阶段最大失重速率峰值温度向低温区移动,会降低PUF成型燃料的残渣剩余量与活化能、提高热值。此外,添加质量分数为8%的WS的PUF成型燃料残渣量低、综合燃烧特性指标高、燃烧充分、活化能较低,PUF成型燃料的燃烧效率最佳。

基于改进自适应增强算法的混煤发热量预测方法

为解决传统多元线性回归(Multivariate linear regression,MLR)模型在煤炭发热量预测方面精度不足和适用性有限的问题,提出了一种基于改进自适应增强算法(Adaptive boosting,Adaboost)的煤发热量的预测模型。将随机森林(Random forest,RF)作为Adaboost的基学习器,以提高模型在工业煤质分析中的发热量预测精度和泛化能力。研究基于某电厂1万组入炉煤的工业分析数据,选取水分、挥发分、灰分和固定碳作为模型输入,建立煤炭低位发热量的预测模型。通过与传统的多元线性回归方程及其他非线性模型比较,模型展现出更高的预测精度和更好的泛化能力。大样本测试的实验结果表明,本模型的平均绝对百分比误差为0.5417%,均方根误差为0.1304 MJ/kg,拟合度(R^(2))达到0.9799,其在煤炭发热量预测方面优于其他模型。此外,200组真实的混煤工业分析数据的模拟验证,进一步确认了本模型较优的泛化性能。

长城三矿选煤厂从洗选矸石中回收低热值煤的工艺探索

长城三矿选煤厂洗选矸石中存在高密度低灰分的煤矸石,现有重介质分选工艺不能对其进行有效分选,直接废弃会造成煤炭资源浪费,直接燃烧产生的热量较少,并且会造成环境污染。通过对长城三矿选煤厂洗选块矸石进行试验分析,并调整末煤重介质旋流器锥角,对这部分低热值煤进行再回收利用,避免煤炭资源的浪费,提高了企业的经济效益。

基于梯度提升回归树算法的煤炭发热量计算

煤炭发热量是衡量煤质的关键指标,反映了煤炭充分燃烧时释放的能量。煤炭发热量可通过实验测定和计算途径获取,其中实验方法虽精确却复杂昂贵耗时。在实际应用中,通过多元线性回归估算得出发热量数据,但是这种方法计算的结果准确率较低。鉴于此,提出了一种基于梯度提升回归树(GBRT)的煤炭发热量计算方法,该方法是一种机器学习回归分析方法,能够有效克服多元线性回归模型在处理非线性数据时的局限性。在国际公认的COALQUAL煤质数据库上对提出的模型进行了验证和对比,结果显示:GBRT模型的预测误差(MAE、MSE、RMSE)均小于多元线性回归模型;拟合优度(R2=0.989)大于多元线性回归模型(R2=0.970)。说明GBRT是一种高效、准确的煤炭发热量预测模型,对于煤质评价具有一定的实际意义。

选煤厂智能配煤系统研究与应用

为解决传统配煤方式严重依赖人工经验、数据反馈较为滞后、操作复杂、劳动强度大等问题,在分析蒙大矿业选煤厂配煤系统现状的基础上,通过对智能配煤算法和控制进行研究,开发了一套智能配煤系统。该系统选用模糊控制进行比例配煤或煤质配煤,能够依据用户要求以及各产品仓的产品质量、储量情况,自动给出最优配煤方案。生产实践表明:该系统可根据煤质数据在线调节配煤比例,使蒙大矿业选煤厂混煤产品吨煤发热量提高了9.85 J,增加收益73.45万元/a,同时避免了“亏卡”现象带来的经济纠纷;实现减员3人,节约人工成本30万元/a。智能配煤系统的研究与应用,实现了选煤厂配煤的智能化、高效化与系统化,对产品煤质提升和选煤效率管控具有积极意义,且该系统具有可复制性,可广泛应用于类似条件的选煤厂配煤系统中。

多源测井数据预测煤层工业组分和发热量模型研究

煤层工业组分和发热量是评价煤质的基本依据,利用测井资料预测煤层工业组分和发热量可以弥补煤芯样试验分析的不足。利用宁夏某井田详查、勘探等不同阶段的数字测井和煤质化验数据,在研究煤质特征、测井响应特征和统计分析的基础上,建立了测井响应特征提取、样本集建立和数据处理方法与深度神经网络模型,通过对测试数据的预测结果和试验分析结果对比,验证了预测模型有效性。

6500t/d水泥项目烧成系统针对性优化设计

巴基斯坦Askari 6500t/d水泥生产线自投料以来各项指标优异,燃料为2种煤质差异较大的原煤搭配使用,暂时没有预均化设施,煤品质波动对烧成系统影响很大。结合原煤特点,通过对分解炉进行针对性定制设计,对烧成系统进行整体优化设计,使烧成系统各项性能指标达到了国外先进水平。

永陇矿区煤矸石分级分质利用分析

针对永陇矿区煤矸石的特点,将其分成不同粒径,根据热值不同用于发电、制砖、建材、铺路、井下填充等,实现了矿区资源变废为宝,促进了当地就业,形成了经济效益、社会效益、生态效益多方共赢的格局,但其综合利用仅处在价值链最低端,因此“十四五”末至“十五五”期间应注重煤矸石基础理论研究,响应国家政策,因地制宜发展新质生产力,提高产品附加值,推动企业产业结构转型升。

关于添加阻化剂预防印尼煤高温自燃的试验研究

为了较好地防治印尼煤在港口堆存过程中出现的高温自燃问题,对市场上主流的4种煤自燃阻化剂产品进行了效果评价试验,结果表明,4种阻化剂均有减缓热值损失的效果;当阻化液浓度在5%、阻化液的加入量为20 g时,阻化率都在40%以上,阻化寿命基本都能达到200 min。选出了效果相对较优的两种阻化剂进行了现场煤堆验证试验,结果表明,添加阻化剂能有效减缓煤堆低温氧化进程,按本次的添加量可以使印尼煤堆高温和冒烟着火出现的时期延缓约6 d。

统筹规划入洗末煤 合理控制产品质量

在潘三选煤厂的实际配煤流程中,可以利用现有工序标准的定量数据建立一个合理准确的模型,并通过一个实例进行统筹分析。这个模型在满足产品热值要求的前提下,实现末煤洗选比例有不同的取值方案。通过建立模型并分析,可以为决策者提供理论支持,使其合理组织生产,利用最低的入洗比例达到最佳的产品配煤效果。同时也为组织者提供数量标准作为参考,及时修正关键环节存在的问题。

煤炭质量常规化验中的注意事项

针对煤炭质量常规化验中采样阶段、制作阶段、化验分析阶段存在的问题及处理措施进行分析,讨论了煤质化验环节、发热量检测环节、硫含量检测环节、挥发检测环节中的误差控制措施,目的在于提高煤质常规化验结果的准确性和真实性,为同类煤质化验工作提供借鉴和参考。

某公司常用喷吹煤有效热值分析和性价比评价

高炉喷吹煤的燃烧环境不同于锅炉燃烧,为更好评价喷吹煤发热量,提出了有效热值法理论和基于有效热值法的性价比分析建模过程,对某公司常用喷吹煤资源进行了有效热值分析和性价比评价,为高炉喷吹煤采购提供指导。

煤炭热值对机组运行和电煤消费影响及其应对措施

煤质是影响电厂运行过程中节能环保水平以及电煤消费量的关键因素,从煤炭的发热量、灰分、水分、含硫含氮量等参数的变化分析了煤质对机组运行及电煤消费量的影响机理。结合江苏省主要燃煤发电企业近两年入炉煤热值、发电量及发电耗煤数据,定量分析了电煤热值变化对全省电煤消费量的影响,按照2022年全省煤电发电量测算,全省电煤年平均热值每提高418.4 kJ/kg,全年电煤消费总量减少约3.9×10^(6) t,减少CO_(2)排放约1.0×10^(7) t。因此,从强化源头管控、合理储配掺烧、实施更新改造、精细运行管理和优化存煤结构等方面,提出控制电煤热值的相关措施,降低机组发电煤耗,控制污染物排放,起到节能减排的作用。

研磨时间对不同煤种煤质特性的影响研究

煤炭一般分析试验煤样是服务于煤炭行业政策制定、科技创新、标准制修订、贸易结算等所需煤质指标的检测基体,煤样研磨属于制备一般分析试验煤样的必经过程,掌握研磨时间对煤质特性的影响可为保障煤炭质量检测结果的准确性提供技术支撑。针对不同变质程度的煤种,分析不同研磨时间对一般分析试验煤样粒度分布的影响,研究煤样的分析水、挥发分、发热量、黏结指数、坩埚膨胀序数度等项目检测结果随研磨时间的变化趋势。结果表明:褐煤和无烟煤样品的研磨时间小于90 s时,其0.2 mm筛上物产率较高,尤其在研磨时间为45 s时,褐煤与无烟煤样品的0.2 mm筛上物产率分别为14.6%和7.3%;研磨时间大于90 s后,其0.2 mm筛上物产率接近于0。随研磨时间的增加,烟煤样品0.2 mm筛上物产率均表现出升高趋势,且具有黏结性的烟煤样品在研磨时间45~240 s范围均含有少量的0.2 mm筛上物。研磨时间对褐煤和无烟煤的分析水影响较大,对不同煤种的挥发分、发热量影响较小,对低黏结性烟煤的黏结指数和坩埚膨胀序数影响较大;随着研磨时间的增加,黏结指数和坩埚膨胀序数均有下降,而黏结指数下降尤为明显。