基于基团贡献法的有机化合物好氧生物降解预测模型研究

从MITI-Ⅰ试验中筛选出587种不同类型有机化合物的可用数据,通过对这些物质的结构进行拆分,随机选择其中50种化合物作为验证集,另外537种作为训练集,利用多元线性回归(MLR)和支持向量机(SVM)2种计算方法分别建立模型。结果表明,芳香酸、醛、芳香碘和叔胺等功能基团对有机化合物的好氧生物降解性影响较大;MLR模型总体预测正确率为81.43%,验证集正确率为82%,SVM模型总体预测正确率为87.90%,验证集正确率为86%。所建立的2种定量结构与生物降解性关系(QSBR)模型有效,可用于化学品的好氧生物降解性评价。     

前置反硝化-硝化生物滤池工艺物料平衡分析

针对前置反硝化(DN)-硝化(CN)生物滤池工艺进行碳(COD)、氮、磷平衡分析,建立了该工艺的物料衡算方法。分析结果表明:DN池中反硝化的碳源主要是甲醇,占DN池反硝化碳源量的70.8%。入流总氮中有13.7%通过CN池的同步硝化反硝化过程去除;入流COD中有46.4%通过CN池中异养菌的好氧氧化去除;入流磷总量约有75.9%被微生物利用。CN池中硝化过程耗氧量为29.0%,占供氧量的比例最大。     

太原地区煤矸石作为建筑回填材料的试验研究

为了利用煤矸石作为建筑回填材料,控制其压实质量,以室内土工试验的方法,对太原地区某矿煤矸石进行了物理、力学等指标的试验研究。结果证明,所用煤矸石性能良好,性质较稳定,适合作为回填材料。并回归出该煤矸石的应力-应变关系式,得出了物理力学参数。     

多类支持向量机在煤矿安全评价中的应用研究

针对矿井子系统诸多、环境复杂、影响因素多变和在现实条件下难以获得大量煤矿样本的情况,提出将对非线性、小样本问题有较高处理能力的支持向量机理论引入到机制评价中,并在归纳了支持向量分类机从一对多到一对一再到决策树模式的多渠道多层次分类原理基础上,建立了基于多分类支持向量机原理的煤矿安全多层次评价模型,同时通过提取影响煤矿安全因素的特征参数,引人类权重因子和样本权重因子,较好地解决了训练样本类别数量不平衡、数据干扰导致的错分问题,实现了对煤矿安全较高准确率和较高效率的评价.     

网状多孔材料防爆性能研究进展

易燃易爆危险化学品事故影响大、危害广,给人们生活和企业生产带来了较大影响,抑制危险化学品火灾爆炸愈来愈引起人们的关注.介绍了多孔材料的基本信息,重点综述了网状多孔抑爆材料防爆性能的实验和数值模拟研究现状,提出了研究中存在的问题:(1)实际应用中,由于容器内部形状限制或内部仪器对容器空间的占用,填充材料时不可能都选取最佳的填充密度和留空率,应根据实际情况选择,但目前尚没有作为材料抑爆性能主要因素的填充密度、留空率与抑爆压力间的数学经验公式;(2)实验测得的抑爆压力为瞬时的间隔值,不一定是过程中的最高压力,由于材料的不透明性,高速摄像机不能完全观察到火焰具体的传播状况.最后结合最新的研究成果提出实验和数值模拟的设想,为以后的研究提出参考.     

建材企业作业场所职业病危害分析与防治

建材行业作为我国的基础原材料行业,在国民经济中占有重要的地位。随着近年来建材行业的逐步做大做强,建材行业的安全生产形势尤其是职业病危害及防治也不容小觑。本文围绕建材行业生产特点,对作业场所职业病危害与防治所面临形势进行了讨论,提出了相应职业病危害控制措施及建议。     

湿法粉碎制备纳米RDX安全性分析与性能表征

从热传导理论分析入手,以回收的RDX为原料,选择易获取的水为导热介质,湿法粉碎制备了纳米级RDX样品。利用纳米激光粒度仪、场发射扫描电镜和卡斯特型落锤仪对样品粒径大小、外观特征、感度大小进行了观察和测试。实验结果表明,湿法制备的纳米级RDX感度明显降低,安全性提高。由于本方法成本低廉,使得大规模回收的RDX再次利用成为可能,解决了RDX不方便储存运输使用等难题,扩大了其使用范围。     

基于层层自组装技术制备的吸油型聚氨酯泡沫材料及其性能研究

利用高效吸油材料处理溢油事故是一种简单有效的方法。针对现有吸油材料吸油效率较低、制备方式复杂的问题,采用层层自组装技术,将钠基蒙脱土填充的涂层构筑于软质聚氨酯泡沫表面,经过后续疏水改性处理,制备了具有超疏水超亲油表面的聚氨酯泡沫材料。材料测试结果表明:组装4层涂层改性的聚氨酯泡沫水接触角达到135°,对不同种类油品的吸油重量能达到自身重量的30~60倍;油水混合物中的吸油效果也显示其能有效处理水面、水下油污。宏观燃烧实验测试结果表明制备的改性聚氨酯泡沫具有良好的阻燃性能。采用上述简单易行的制备技术制得的吸油材料,可成功应用于油水分离,具有良好的应用前景。     

Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method

With the development of natural gas transportation systems, major accidents can result from internal gas leaks in pipelines that transport high-pressure gases. Leaks in pipelines that carry natural gas result in enormous financial loss to the industry and affect public health. Hence, leak detection and localization is a major concern for researchers studying pipeline systems. To ensure the safety and improve the efficiency of pipeline emergency repair, a high-pressure and long-distance circular pipe leakage simulation platform is designed and established by similarity analysis with a field transmission pipeline, and an integrated leakage detection and localization model for gas pipelines is proposed. Given that the spread velocity of acoustic waves in pipelines is related to the properties of the medium, such as pressure, density, specific heat, and so on, this paper proposes a modified acoustic velocity and location formula. An improved wavelet double-threshold de-noising optimization method is also proposed to address the original acoustic wave signal collected by the test platform. Finally, the least squares support vector machine (LS-SVM) method is applied to determine the leakage degree and operation condition. Experimental results show that the integrated model can enhance the accuracy and precision of pipeline leakage detection and localization.     

The role of weekly high‐activated positive mood,context, and personality in innovative work behavior: A multilevel and interactional model

This article proposed and tested a multilevel and interactional model of individual innovation in which weekly moods represent a core construct between context, personality, and innovative work behavior. Adopting the circumplex model of affect, innovative work behavior is proposed as resulting from weekly positive and high‐activated mood. Furthermore, drawing on the Big Five model of personality and cognitive appraisal theory, openness to experience and support for innovation are proposed as individual and contextual variables, respectively, which interplay in this process. Openness to experience interacts with support for innovation leading to high‐activated positive mood. Furthermore, openness interacts with these feelings leading to greater levels of innovative work behavior. Overall, the model entails a moderated mediation process where weekly high‐activated positive mood represents a crucial variable for transforming contextual and individual resources into innovative outcomes. These propositions were tested and supported using a diary methodology and multilevel structural equation modeling, on the basis of 893 observations of innovative work behavior and moods nested in 10 weekly waves of data. This information was collected from 92 individuals of diverse occupations employed in 73 distinct companies. Theoretical and practical implications are discussed. Copyright © 2013 John Wiley & Sons, Ltd.