发酵液作为EBPR碳源的动力学模拟

发酵液是一种优质的碳源,能够提高生物除磷系统(EBPR)的除磷效果.采用基于碳源代谢的修正ASM2模型,能够较好地模拟发酵液作为EBPR碳源的动力学变化规律.发酵液作为EBPR唯一碳源时,系统中的异养菌不仅不对聚磷菌(PAO)的生长构成竞争关系,反而促进PAO的生长.发酵液作为实际污水的补充碳源时,优化了污水中的碳源组成,创造了有利于聚磷菌生长的环境,使EBPR中聚磷菌达到微生物总量的40%以上,比实际污水作为碳源的EBPR中的PAO含量提高了3.3倍.     

Structural equation model of the influence of noise annoyance on miners’ unsafe behavior in coal mines

Miners' unsafe behavior (UB) is the main cause of coal mine accidents. Previous research has suggested that excessive noise in tunnels impacts miners' temperaments and safe production behavior. To explore the influence of noise on UB, four different dimensions of noise annoyance were identified: cognitive, emotional, communication, and physical mechanism annoyance. The coal mine noise annoyance scale, miners' unsafe behavior intention (UBI) scale, and miners’ UB scale were developed according to different dimensions. In the first survey, a total of 200 questionnaires were distributed in the two coal mines, and 193 valid questionnaires were recovered. Exploratory factor analysis was conducted, and invalid items were removed according to the analysis results to form the final scale. Then, 500 final scales were distributed in two Chinese coal mines, and 482 valid questionnaires were collected. Confirmatory factor analysis was conducted by collating the data of the second questionnaire. Based upon the structural equation model (SEM), the SEM of the influence of noise annoyance degree on UB and the path analysis with latent variable path analysis with the latent variables (PA-LVs) mediating model of the effect of UBI on UB were established, and the path coefficients with fitting data of the model were analyzed. The results indicated that the four dimensions of coal mine noise annoyance had a strong positive impact on UB, which was reduced by less noise annoyance. In addition, the Bootstrap method was used to verify the mediating method among the four dimensions of noise annoyance, cognitive annoyance, emotional annoyance, with communication annoyance indirectly affecting UB through UBI. The research results provide theoretical support for reducing the UB of miners and improving coal mine safety levels.     

Experimental and theoretical research on the inhibition performance of ethanol gasoline/air explosion by C6F12O

The safety issue of ethanol gasoline and the methods to control or weaken its explosion have attracted attention. To clarify the effect of C₆F₁₂O (perfluoro(2-methyl-3-pentanone)) on the explosion of ethanol gasoline-air mixtures and intrinsic mechanism, the explosion overpressure and flame propagation behavior under different equivalence ratios (φ = 0.6–0.8) and C₆F₁₂O concentrations (χ_inh = 0–4.0%) were experimentally obtained. The detailed inhibitor reaction process was also obtained by CHEMKIN based on a new assembly kinetic mechanism. The results show that the effects of C₆F₁₂O on the explosion characteristics of ethanol gasoline varied with χ_inh and φ. For rich flames, C₆F₁₂O is more effective than and heptafluoropropane (C₃HF₇) and nitrogen (N₂) in suppressing explosions; for lean and equivalence ratio flames, the addition of C₆F₁₂O may result in more severe explosions. The decrease in chemical reactivity is mainly because the mole fractions of OH and H radicals and the proportion of paths H radicals involved decrease after adding C₆F₁₂O, and R1500: CF₃COF + H = CF₃CO + HF, R965: CF₂:O + H = CF:O + HF, R863: CF₃ + H = CF₂ + HF are main suppressing reactions.     

基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析*

为解决电力行业事故报告文本较长、语义复杂,难以进行有效文本识别问题,提出1种以BERT作为底层的预训练模型,并设计1种双重注意力机制编码器,结合BILSTM-CRF深度挖掘事故文本语义特征,从而实现文本智能分析。首先构建电力词典,通过对BERT预训练,进行BIO标注,然后引入BILSTM-CRF模型实现对文本标签智能分类,最后将该模型与现行其他4种深度学习模型进行对比。研究结果表明:该模型智能识别精确率、召回率及F1值(查准率)均达到约97%,较其他4种模型中效果最好的模型分别提高0.02,0.03,0.02。研究结果可为电力行业事故报告文本分析提供1种新思路。     

基于缓冲区重采样的LSTM林火预测模型*

为提高林火风险预测精度,挖掘地图上隐含的空间信息、时间序列上隐含的长期趋势和循环波动,提出1种基于缓冲区重采样的长短期记忆（LSTM）林火预测模型,选取15个与林火相关的影响因素,以方差膨胀因子为评价指标对其进行多重共线性检验,方差膨胀因子大于10的因素具有共线性,并采用信息增益率验证筛选结果的合理性。考虑到火灾的空间聚集特性,采用缓冲区分析与过采样相结合方法减少样本不均衡现象的影响,最终得到176 732条样本。对12个影响因素和研究时间段的火点建立LSTM预测模型,对森林火灾发生风险进行预测。研究结果表明:基于缓冲区重采样的LSTM林火预测模型有效考虑时空上隐含的信息,预测模型准确率为87.06%,特异性为97.99%,敏感度为76.12%,阳性预测率为97.43%,阴性预测率为80.41%,ROC曲线与AUC值均优于随机森林（RF）和支持向量机（SVM）这2种基准算法。维尔克松秩和检验发现,本文提出的模型与基准算法结果具有显著性差异。研究结果可为提高林火风险预测精度提供参考。     

北京市PM5.0可吸入颗粒物反演及其时空分布分析

分析城市可吸入颗粒物的空间特征及影响因素对于制定更为有效的可吸入颗粒物污染控制政策具有重要的现实意义.本研究在实际采样和遥感数据处理基础上,构造差值植被指数(DVI),通过建立TM影像与对应PM5.0颗粒物实测值之间的相关关系模型,对北京市2008~2010年3 a的PM5.0的分布进行了反演计算和精度分析;研究了NDBI和NDMI对PM5.0浓度的影响,进一步揭示了北京五环区域范围内可吸入颗粒物的时空分布特征.结果表明,采用DVI反演PM5.0分布的方法可行,反演精度在可接受范围内;整体上研究区2008年PM5.0颗粒物污染最轻,西南三环和四环以及东南三四环之间为颗粒物污染的高值区,西北五环附近颗粒物污染较轻;NDBI(归一化建筑指数)、NDMI(归一化水汽指数)与PM5.0可吸入颗粒物的分布分别呈现显著的负相关和正相关关系,且二者对PM5.0的影响相近.     

优势微生物菌群消减有机污泥作用效果分析

本文阐述用优势微生物菌群消解有机污泥的作用效果及原因,指出了优势菌群的构成类别和消解特点。实验结果表明,污泥消解率可达到55%,且过程对出水水质不产生负面影响,水质指标均符合GB18918-2002要求。     

超声波联合PAM对污泥脱水性能的影响

以污泥脱水性能与沉降性能为指标,研究了超声波调理联合PAM对污泥脱水性能的影响。实验结果表明,单独使用超声波调理的最佳调理时间为20 s,此时污泥比阻为0.68×109s2/g;采用PAM对污泥进行调理时,在浓度为0.1%,投加量为100 mg/L时污泥比阻最小,为0.32×109s2/g;采用超声波与PAM联合调理时,利用正交试验得到最适合的组合条件为:调理时间20s、PAM投加量100 mg/L、浓度0.15%。     

Multi-objective transportation route optimization for hazardous materials based on GIS

Safety and security are of paramount importance, it is important to optimize and improve the routes of trucks that carry hazardous materials. In this study, we not only ensure the risk in the network, but also consider the transportation cost and the factors such as buildings and emergency facilities around the routes. The Geographic Information System (GIS) is used to quantify the factors on each section in the network. We present an epsilon constrained multi-objective mixed-integer linear programming optimization model to find the robust and stable transportation optimization solutions. At the end, we complete a case analysis of the proposed methodology to determine the motorway segments in Jiangsu province, China and test the above algorithm on the network, which has 144 nodes and 388 sections. The results we get show that the factors of buildings play a very important role in the model, and the multi-objective mixed-integer linear optimization model is reasonable and performs good quality.     

Improved models of failure time for atmospheric tanks under the coupling effect of multiple pool fires

Fire accidents of chemical installations may cause domino effects in atmospheric tank farms, where a large amount of hazardous substances are stored or processed. Pool fire is a major form of fire accidents, and the thermal radiation from pool fire is the primary hazard of domino accidents. The coupling of multiple pool fires is a realistic and important accident phenomenon that enhances the propagation of domino accidents. However, previous research has mostly focused on the escalation of domino accidents induced by a single pool fire. To overcome the drawback, in this study, the failure of a storage tank under the coupling effect of multiple pool fires was studied in view of spatial and temporal synergistic process. The historical accident statistics indicated that the accident scenario of two-pool fires accounted for 30.6% in pool fires. The domino accident scenario involving three tanks is analyzed, and the typical layout of tanks is isosceles right triangle based on Chinese standard “GB50341-2014”. The thermal response and damage of a target tank heated by pool fires were numerically investigated. The volume of 500 m³, 3000 m³, 5000 m³ and 10000 m³ were selected. Flame temperature was obtained by FDS, and then was input onto the finite element model. The temperature field and stress field of target tanks were simulated by ABAQUS. The results showed that the temperature rise rate of the target tanks under multiple pool fires was higher than that under a single pool fire. The failure time of the tank under the coupling effect of multiple fires was lower than that under the superposition of multiple fires without the first stage. The stress and yield strength were compared to judge the failure of the target tank. The model of failure time for the tank under the coupling effect of pool fires was established. Through the verification, the deviation of this model is 4.02%, which is better than the deviation of 15.76% with Cozzani's model.