The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Ionic Liquid Assisted Extraction of Nitrogen and Sulphur-Containing Air Pollutants from Model Oil and Regeneration of the Spent Ionic Liquid

Removal of air pollutants, such as nitrogen and sulphur containing compounds from a model oil (dodecane) was studied. An ionic liquid (1-ethyl-3-methylimidazolium chloride [C2mim] [Cl]) was used as an extractant. Liquid-liquid extraction by using 1-ethyl-3-methylimidazolium chloride [C2mim] [Cl] was found to be a very promising method for the removal of N- and S-compounds. This was evaluated by using a model oil (dodecane) with indole as a neutral nitrogen compound and pyridine as a basic nitrogen compound. Dibenzothiophene (DBT) was used as a sulphur compound. An extraction capacity of up to 90 wt% was achieved for the model oil containing pyridine, while only 76 wt% of indole in the oil was extracted. The extraction capacity of a model sulphur compound DBT was found to be up to 99 wt%. Regeneration of the spent ionic liquid was carried out with toluene back-extraction. A 1:1 toluene-to-IL wt ratio was performed at room temperature. It was observed that, for the spent ionic liquid containing DBT as a model compound more than 85 wt% (corresponding 3852 mg/kg) could be removed from the oil. After the second regeneration cycle, 86 wt% of the DBT was recovered from the ionic liquid to toluene. In the case of indole as the nitrogen containing species, more than 99 wt%, (corresponding to 2993 mg/kg) of the original indole was transferred from the model oil to the ionic liquid. After the first-regeneration cycle of the spent ionic liquid, 54 wt% of the indole–in-IL was transferred to toluene. Thus, both extractions of nitrogen and sulphur model compounds were successfully carried out from model oil and the back-extraction of these compounds from the ionic liquids to toluene demonstrated the proved the concept of the regeneration point of view.

The Impact on Extraction Effect of Polysaccharide from Ganoderma lucidum by Different Pretreatment

[Objectives] The research aimed to study the impact on extraction effect of polysaccharide from Ganoderma lucidum( lingzhi) by different pretreatment methods. [Methods] The impacts on extraction of G. lucidum polysaccharide by soaking,microwave and air flow fine pulverization were contrasted,and the extraction effect of G. lucidum polysaccharide by combining the optimal pretreatment manner with hot water extraction,alcohol extraction,alkali extraction,ultrasonic binding enzyme extraction,and microwave extraction was compared. Finally,the property of G. lucidum polysaccharide obtained after air flow fine pulverization pretreatment was detected and analyzed by high performance liquid chromatography. [Results] The optimal pretreatment method was air flow fine pulverization. Compared with traditional method-direct extraction( coarse grinding combining hot water extraction),crude yield changed little,while polysaccharide content and yield were improved by 114% and 104%. The best combination manner was air flow fine pulverization pretreatment combining with alkali extraction. Compared with traditional method,crude yield,polysaccharide content and yield were improved by 76%,78% and 215% respectively. The property of G. lucidum polysaccharide obtained after air flow fine pulverization pretreatment was detected and analyzed by high performance liquid chromatography. It was found that the treatment method had little impact on the property of G. lucidum polysaccharide. [Conclusions]Air flow fine pulverization pretreatment could greatly improve extraction effect of G. lucidum polysaccharide,which mainly improved the content and yield of G. lucidum polysaccharide,and extraction was more complete,with less impact on the property of the extracted polysaccharide. It was speculated that air flow fine pulverization pretreatment mainly destroyed mechanical support wall membrane structure of G. lucidum fine powder,making that intracellular functional substances completely dissolved out of the cell,and the content would be studied in follow-up experiment.

Discharge characteristics of coal and extraction residue from direct coal liquefaction in partial fluidization silo

Gasification of extraction residue(ER) from direct coal liquefaction with pulverized coal is an efficient way for the utilization of carbonaceous wastes, which improve the overall efficiency of direct coal liquefaction technology. The discharge characteristics of ER mixing with pulverized coal is important paraments for its gasification process, which is seldom studied in the literature. In this study, the discharge characteristics of the pulverized coal(M1) as well as its mixture with ER(M2) were systematically investigated in an atmospheric pressure partial fluidization silo with different fluidization apparent velocity. It was observed that although M2 is a viscous powder with lower flowability than M1, the mass flow rate of M2 is 65% higher than M1 at the 3.7 mm·s-1apparent gas velocity. M2 exhibits the properties of Geldart A type powder, which improves the mass flow rate and stability of the discharged material. The mass flow rate of both M1 and M2 first increases and then slowly decreases with the increase of apparent gas velocity of the fluidizing air, which means the discharge process of M1 and M2 can be optimized by the apparent gas velocity.

Artificial neural network techniques to predict the moisture ratio content during hot air drying and vacuum drying of Radix isatidis extract

Background:To predict the moisture ratio of Radix isatidis extract during drying.Methods:Artificial neural networks were designed using the MATLAB neural network toolbox to produce a moisture ratio prediction model of Radix isatidis extract during hot air drying and vacuum drying,where regression values and mean squared error were used as evaluation indexes to optimize the number of hidden layer nodes and determine the topological structure of artificial neural networks model.In addition,the drying curves for the different drying parameters were analyzed.Results:The optimal topological structure of the moisture ratio prediction model for hot air drying and vacuum drying of Radix isatidis extract were“4-9-1”and“5-9-1”respectively,and the regression values between the predicted value and the experimental value is close to 1.This indicates that it has a high prediction accuracy.The moisture ratio gradually decreases with an increase in the drying time,reducing the loading,initial moisture content,increasing the temperature,and pressure can shorten the drying time and improve the drying efficiency.Conclusion:Artificial neural networks technology has the advantages of rapid and accurate prediction,and can provide a theoretical basis and technical support for online prediction during the drying process of the extract.

Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals.Unfortunately,personal air quality measurement remains challenging.In this study,we investigate the use of first-person photos for the prediction of air quality.The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction.AirStackNet consists of two layers and four regression models,where the first layer generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.

岩巷综掘面抽尘参数影响粉尘污染扩散规律研究

抽尘系统是岩巷综掘面长压短抽通风方式中控除粉尘污染的重要组成部分,而确定适宜的抽尘参数是实现有效除尘的关键。利用Solidworks软件对某矿632岩巷综掘面进行了物理建模,并进行了ICEM-CFD网格划分,通过网格无关性检验确认了后续所用的网格模型,运用FLUENT计算流体力学软件对不同抽尘位置Dn(n分别为0、1/4、1/2、3/4、1)与抽风量(300~350 m^(3)/min)影响粉尘污染扩散状况进行了数值模拟,并通过仿真试验验证了数值模拟结果的准确性。结果表明:抽尘位置为D0、D1/4、D1/2、D3/4时,巷道内风流整体均处于较为紊乱的状态,粉尘污染经风流运载最终均扩散至整个巷道;抽尘位置为D1时,在抽风与压风协同作用下,距迎头4.6~6 m范围内形成了风向指向迎头、流动均匀且覆盖巷道全断面的有效控尘风幕,粉尘污染被风幕控制在掘进机司机与迎头之间的封闭空间内。抽风量在330 m^(3)/min及以上时,在司机处均形成了有效控尘风幕。为了在有效控尘的前提下尽可能降低能耗,最优抽风量选取最小值330 m^(3)/min。

随机误差传递的高空气象秒级数据快速校验方法

【目的】为保证气象预报结果的准确性。【方法】提出了考虑随机误差传递的高空气象秒级数据快速校验方法。对高空气象秒级数据进行预处理,包括数据中心化处理、特征点提取。数据中心化的处理方式为实施各组数据的加权平均处理,获得新的数据组作为基准数据;特征点提取使用的处理方法为遍历法,需要遍历全部数据点。考虑数据获取过程中的随机误差传递情况,对预处理后的高空气象秒级数据实施空间一致性检查、内部一致性检查。应用集合经验模态分解EEMD(Ensemble Empirical Mode Decomposition)算法与对比源反演CSI(Contrast Source Inversion)算法构建基于CSI-EEMD的高空气象秒级数据快速校验模型,实施数据得到快速校验。【结果】测试结果表明,该方法能够实现4个气象站数据的快速校验,校验结果的均方根误差与平均绝对误差均低于0.1,实现了数据的质量控制。【结论】将该方法应用于气象站数据校验中,可以实现精准度的数据校验,具有一定的实用价值。

搅拌子吸附-热脱附-气相色谱-质谱法分析北京大气中33种多氯联苯

用包覆聚二甲基硅氧烷(PDMS)吸附层的搅拌子作被动大气采样器,建立了搅拌子吸附-热脱附-气相色谱-质谱法(SBSE-TD-GC-MS)分析大气中多氯联苯(PCBs)的方法。优化后的热脱附温度为280℃,热脱附时间为6 min,聚焦冷阱的温度为-40℃,33种PCB单体的热脱附残留比例低于7%。验证了方法的线性、精密度和重复性。标准曲线5个点线性关系良好,33种PCB单体的相关系数r>0.998。方法精密度验证的相对标准偏差(RSD)为0.79%~20.2%(n=8),日内重复性实验的RSD为0.43%~29.8%(n=10),日间重复性实验的RSD为3.20%~29.6%(n=20),方法检出限(LOD)为0.01~2.51 pg,回收率为98%±18%。将建立的方法用于北京大气中PCBs的分析。检出8种PCB单体,质量为2.94~129 pg。在32 d的采样周期内PCB-52、PCB-123、PCB-155一直处于线性吸附阶段,而PCB-1和PCB-3经过了线性吸附阶段、曲线吸附阶段和平衡阶段。研究结果表明,SBSE-TD-GC-MS可以用于大气中PCBs的样品采集和快速分析。

注浆压力对顺层抽采钻孔封孔质量影响研究

为研究不同注浆压力下各顺层钻孔的封孔质量,以轩岗煤电有限责任公司刘家梁煤矿2214工作面为试验对象,根据矿井煤层实际赋存情况,采用COMSOL数值模拟软件,模拟不同注浆压力下浆液在煤层中的扩散范围,数值模拟结果表明,在1~4 MPa注浆压力范围内,浆液在煤层中的扩散半径与其注浆压力的大小成正相关关系,而浆液扩散半径的增加幅度随注浆压力的增加呈现逐渐减小的变化趋势。同时在钻孔始封深度、封孔长度等封孔参数一定的情况下,按照1、2、3和4 MPa四个等级进行现场顺层钻孔封孔试验,并利用漏气率对各抽采钻孔的封孔质量进行评价和对比,现场试验表明,采用1 MPa和2 MPa注浆压力的顺层钻孔分别有4个和2个钻孔出现漏气,其平均漏气率分别为32.76%和28.65%,而采用3 MPa和4 MPa注浆压力的钻孔均未出现漏气现象。综合数值模拟及现场试验结果表明,该工作面顺层钻孔封孔时其注浆压力应不小于3 MPa。

瓦斯抽采钻孔封堵一体化封孔技术研究及应用分析

针对地应力存在易影响煤层瓦斯抽采钻孔、易造成钻孔围岩出现衍生裂隙,进而引发钻孔漏气,影响瓦斯高效抽采的问题。为有效解决上述问题,以山西某矿5310综采工作面瓦斯抽采工程概况为例,深入分析了该工作面瓦斯抽采钻孔在使用后期出现失效的原因,研究了钻孔封堵一体化技术,设计了三囊袋封堵一体化装置,确定了最优裂隙堵漏材料配比方案,并进行了工业性试验,试验结果表明:与传统的两堵一注技术相比,应用封堵一体化技术后,钻孔瓦斯抽采平均浓度可从17.2%提升到59.6%,瓦斯抽采效果明显改善,有效消除了矿井安全生产隐患,更好地保障了矿井安全生产。

空气—双氧水联合氧化工艺选择性浸出废磷酸铁锂材料中的锂

采用空气—双氧水联合氧化工艺选择性浸出废磷酸铁锂材料(废磷酸铁锂电池正极材料粉末)中的锂,经沉锂后以碳酸锂的形式回收。实验结果表明,在液固比为4 mL/g、H_(2)SO_(4)与Li的摩尔比为0.5、搅拌转速为250 r/min、反应温度为50℃的条件下空气曝气300 min,再于相同反应温度和搅拌转速下滴加H_(2)O_(2)(H_(2)O_(2)与Li的摩尔比为0.29)反应120 min,锂、铁和磷的浸出率分别为93.47%、17.26%和19.83%。该工艺较单独双氧水氧化工艺可减少75%以上的双氧水用量,大幅降低了回收成本。溶解氧浓度对浸出体系中Fe^(3+)的存在方式有重要影响:在较高浓度(通空气)下以磷酸铁为主;在较低浓度(未通空气但接触空气)下以氢氧化铁为主。

基于多尺度时空优化的空气质量预测方法

本文提出一种基于多尺度时空优化的空气质量预测方法(multi-scale spatial-temporal network for air quality prediction,MSSTN-AQP),结合空气质量系统中存在的长短期时间依赖关系和动态空间依赖性,提高长期空气质量预测的准确性。首先,通过构建多尺度时空特征提取模块,从多源异构数据中提取时空特征。其次,构建动态空间特征提取模块。通过将图卷积网络与注意力机制进行有效结合,捕捉空气质量网络中的全局空间特征,用于对多种空间依赖关系的联合建模。最后,构建时间特征提取模块,对Transformer模型进行改进与优化。自适应时间Transformer模块主要用于模拟跨多个时间步长的双向时间依赖关系。此外,将上述时空特征提取模块进行有效集成化,构建端到端的空气质量预测模型。为了验证模型的有效性,在两个真实数据集中进行实验验证。实验结果表明,MSSTN-AQP在预测精度上更具优势,尤其是在长期的空气质量预测任务中优势更加明显。

基于辐照度分析的干式空心电抗器局部放电定位方法

干式空心电抗器局部放电的定位对于保证电能质量和节省维修成本具有重要意义。针对现有局部放电定位方法在电抗器实际应用中精度较低的问题,提出一种基于辐照度分析的干式空心电抗器局部放电定位方法。首先,根据干式空心电抗器典型结构建立三维光学仿真模型,分析不同位置放电光源在包封底部的辐照度分布特性,基于最大方差理论对辐照分布矩阵进行特征提取,构建光学仿真数据集;然后,采用交叉验证的方法对随机森林和反向传播神经网络模型进行参数调优,使用测试集评估模型的定位效果;最后,利用干式空心电抗器局部放电实验平台,对定位方法进行实测验证。结果表明,2种模型在实验数据集下的定位准确率均高于90%,相比于反向传播神经网络,随机森林的定位准确率更高且推理时间更短,验证了基于辐照度分析的放电定位方法的有效性。

下向低浓钻孔“抽-注”排水排渣抽采一体化技术研究

为了防止永夏矿区陈四楼煤矿煤与瓦斯突出事故的发生,在采用水力排渣钻孔进行抽采瓦斯时,发现在钻孔内存在着大量的水和煤渣,堵塞了煤体内部的孔隙通道,对煤层瓦斯运移和钻孔孔壁的稳定性产生不利影响,从而严重影响了瓦斯抽采的效果。针对煤矿急需解决的俯孔排水排渣问题,提出采用压风吹孔技术,将孔内水和煤渣带出孔外,疏通钻孔堵塞通道。该技术在陈四楼煤矿井下现场应用效果良好,吹孔后钻孔平均在抽浓度由11.86%提升到了18.73%~56.38%,低浓钻孔提升效果明显;代替了以往水力排渣钻进技术,达到高效抽采瓦斯、保证煤矿安全生产的目的,对钻孔抽采瓦斯过程中能够及时排出水和煤渣有一定指导意义。在此基础上,形成的一套下向低浓钻孔“抽—注”排水排渣一体化技术及工艺,为下向低浓钻孔排水排渣工作提供参考借鉴。

综掘工作面风幕阻尘效果影响因素研究

目前综掘工作面粉尘污染的研究多集中于单一因素对综掘工作面风幕阻尘效果的影响,而未充分考虑各因素间的交互作用,使得压风分流技术的工程应用效果欠佳。为明确附壁风筒径向出风距离、径向出风比及轴向出风距离对风幕阻尘效果的影响,以潘三矿810西翼机巷综掘工作面为研究对象,运用Fluent软件对径向出风距离为10~25 m、径向出风比为0.6~0.9及轴向出风距离为6~12 m条件下的风流分布和粉尘扩散情况进行数值模拟。结果表明:(1)随着径向出风距离增大,径向涡流风幕在巷道内的转变更充分,综掘机司机前端的风流分布越均匀,更有利于形成风速方向均指向工作面的轴向阻尘风幕。当径向出风距离为10 m时,距工作面7 m断面内涡流特性明显,风速方向紊乱;当径向出风距离为25 m时,距工作面7 m断面内,风流分布趋于均匀,风速方向均指向工作面,形成了能够覆盖全断面的轴向阻尘风幕。(2)随着径向出风比增大,整流风筒轴向风流风量减小,轴向风流风速和射流强度降低,轴向风流对综掘工作面前端气流的扰动减弱;径向出风比越大,越有利于形成风流方向指向工作面且能覆盖全断面的轴向阻尘流场,即轴向阻尘风幕。(3)径向涡流风幕的阻尘能力随径向出风比的增大先增强后减弱,轴向阻尘风幕的阻尘能力随径向出风比的增大而不断增强。(4)在采取压风分流风幕阻尘技术后,当压风总量为300 m^(3)/min,吸风量为400 m^(3)/min,附壁风筒径向出风距离为20 m,径向出风比为0.9,整流风筒轴向出风距离为8~10 m时,能很好地将粉尘聚集在吸尘口附近,达到高效控尘除尘的目的。在810西翼机巷综掘工作面进行现场测试,测点风速和粉尘质量浓度实测值与模拟值基本一致,高浓度粉尘被有效阻控于工作面前端,隔尘效果较为明显,验证了数值模拟的有效性。

基于无机纳米封孔材料提高煤层瓦斯抽采率的试验研究

为解决钻孔使用传统材料封孔产生裂隙导致瓦斯抽采率低、钻孔寿命有限的问题,分析了抽采钻孔漏气途径与周围应力分布,确定钻孔漏气的主要部位;通过向硅酸盐无机材料中添加纳米氧化物对其改性,得到一种能够长期保持水分且不凝固、不收缩等特点的无机纳米封孔材料;基于山西竹林山煤业有限公司53032巷道,开展顺层钻孔封孔及后续补浆封孔试验。结果表明:无机纳米封孔材料试验组平均瓦斯抽采浓度整体优于普通封孔水泥对照组,封孔16 d后,试验组平均瓦斯抽采纯量为0.04~0.06 m^(3)/min,而对照组平均瓦斯抽采纯量为0.02~0.04 m^(3)/min,试验组的封孔质量优于对照组钻孔。试验组经过补浆封孔,平均瓦斯抽采浓度提高了20%,补浆封孔可以提高钻孔的封堵质量和瓦斯抽采的平均浓度。

基于深度融合模型的气膜密封端面状态识别方法

气膜密封装置是工业领域应用广泛的一种密封技术,其可靠的密封性能对于设备正常运行至关重要。气膜密封装置的动静密封环接触端面相对运动会产生摩擦,摩擦过程会产生复杂的声发射信号,这些信号往往隐含密封端面运行状况的重要信息。采用传统的方法往往难以准确识别和分类这些微弱的特征信号,因此需要开发更高精度的故障诊断方法。针对机械密封动、静环端面摩擦状态难以识别这一问题,以气膜密封装置为研究对象,提出了一种基于深度融合模型的气膜密封端面状态识别方法。首先,采用声发射传感器及采集设备,对密封端面的声发射信号进行了采集;其次,利用小波包变换方法对采集到的信号进行了滤波处理,并提取了时域和频域的微弱特征;然后,将深度随机森林(DRF)作为分类层融入卷积神经网络(CNN)形成了融合模型,对预先处理过的密封装置运行状态的特征信息进行了识别和分类;最后,根据实验的泄漏量,使用混淆矩阵和受试者工作曲线分析了两种模型的特征提取能力。研究结果表明:CNN-DRF融合模型对于密封端面声发射信号的两种特征识别精度分别为96%和98%,与传统的CNN模型相比,其可以充分提取信号特征信息,具有更出色的故障诊断能力。

混合LSTM与卷积神经网络的氧气提取率预测方法

针对空分装置系统的运行参数量大、氧气提取率预测研究欠缺的问题,提出一种基于卷积神经网络(CNN)、长短期记忆网络(LSTM)以及混合LSTM和CNN的氧气提取率预测方法。将氧气提取率作为预测目标,基于卷积神经网络、LSTM、混合LSTM与卷积神经网络模型对其进行建模,并应用于空分装置系统运行采集的数据中。使用平均绝对百分比误差、均方根误差和平均绝对误差等指标来评价预测模型的精度,并使用模型训练时间以及模型收敛速度评估模型性能。实验结果表明,采用混合LSTM和卷积神经网络的氧气提取率预测方法的效果明显优于其他两种模型。