地质动力区划预测煤与瓦斯突出

为了确定煤与瓦斯突出矿井的突出危险区域,威胁区域和安全区域,提出基于地质动力区划的多因素模式识别概率预测方法预测煤与瓦斯突出的新思路。以鸡西滴道矿立井为研究对象,利用地质动力区划方法确定不同尺度和级别构造运动的特征,建立板块构造学说与矿井工程实际的联系,将对矿井煤与瓦斯突出产生影响的因素为参数,采用多因素模式识别概率预测方法划分研究区域内的危险区域。研究表明该方法对煤与瓦斯突出区域预测的合理性与有效性,可以在实际工程中应用推广。     

蚀刻液水合肼还原除铜

将电路板厂废弃的蚀刻液,经氢氧化铜沉淀法回收大部分铜后,再采用水合肼还原,进一步除铜。反应温度为50℃,水合肼质量分数为3．0％,溶液pH为6．0,废液中铜的去除率可达98.5％,处理后废液中铜的质量浓度低于0．2g／L,可作为碱性蚀刻液重复利用。     

基于ICEEMDAN和MC-CNN的矿山声发射信号识别分类方法

为精准识别地下矿山声发射事件,采用基于改进的完全集合经验模态分解模型(ICEEMDAN)和多通道卷积神经网络(MC-CNN)模型对声发射信号进行处理后得到分量图,根据各通道输入分量峭度值赋予不同权重,并利用卷积神经网络对输入数据进行训练,最终采用五折交叉实验方法验证该分类识别方法的可行性及有效性.结果表明:基于ICEE...     

南京青奥会环境保障经验及后青奥环境监管对策研究

文章介绍了南京在青奥会环境保障方面的管控措施与取得的成效,认为南京青奥环境保障成功的关键在于高效畅通的运行机制、强力有效的控污手段、不遗余力的执法监管、紧密配合的区域联防,以及科技支撑的科学决策。最后,从构建南京都市圈生态环保一体化格局、实施重点片区工业布局优化调整、淘汰落后产能、建立环境执法高压严管长效机制等方面,提出了后青奥时代南京加强环境监管、改善环境质量的对策与建议。     

福建泉州湾有机氯农药的多介质迁移与归趋

以α-HCH与p,p'-DDT为主要研究对象,以泉州湾及其重要汇水流域--晋江流域为研究区域,构建了Level Ⅲ多介质非平衡稳态逸度模型,对该区域α-HCH与p,p'-DDT在各环境相中浓度、容纳能力、储量分布及各相间的迁移通量进行了计算分析,并对模型关键输入参数的灵敏度进行了探讨.结果显示：α-HCH与p,p'-DDT在土壤相,水相以及沉积物相中的模拟计算浓度与野外样品实测平均浓度吻合度较高,验证了模型的有效性;当研究系统达到平衡时,环境各相对α-HCH与p,p'-DDT容纳能力由大到小分别为沉积物,土壤,水及空气; α-HCH在土壤与沉积物中的储量之和为总储量的97.42%, p,p'-DDT则为99.89%,是其主要的汇区;α-HCH与p,p'-DDT从研究区域迁移消逝的主要途径为水的平流输出,在环境相间迁移过程中,α-HCH的主要迁移途径为水体向大气的迁移,而p,p'-DDT的主要迁移途径为水体向沉积物的迁移;灵敏度分析指出辛醇-水分配系数对数值logK_ow是影响污染物在环境相中浓度分布的最主要因素.     

人工神经网络在矿井多水源识别中的应用

以焦作矿区水化数据为例,选用Na++K+、Ca2+、Mg2+、Cl-、SO2-4、HCO-36种水化离子浓度作为识别矿井水源的依据,运用Matlab软件分别建立BP神经网络与Elman神经网络的矿井多水源识别模型。应用结果表明:与静态的BP神经网络相比,在结构上增加承接层的Elman神经网络在训练和仿真中拟合能力更强,识别精度更高和泛化能力更好;矿井地下水随着地下开采与扰动具有动态性,将具有非线性动态特征的Elman神经网络应用于矿井多水源的识别,对准确判断突水来源和分析地下水运移规律具有一定的辅助和指导意义。     

溧阳电站整流锥支撑系统施工安全影响因素识别研究

溧阳电站整流锥施工难度大,支撑系统安全性的高低直接关系整流锥浇筑安全。为实施安全重点监控,基于人员操作、工作环境、设备设施、现场管理等四个方面,系统分析整流锥支撑系统施工安全影响因素;综合运用二元语义基本理论,集结专家语言评价信息,结合DEMATEL(传统决策试验和评价实验法)核心思路,提出一种考虑因素间关联性的影响因素识别方法;辨析溧阳电站整流锥支撑系统施工安全影响因素重要程度与因果关系。结果表明:现场监督是影响其施工安全最重要的外部因素,基本素质是最易影响其他因素的原因因素,验收管理是最易受其他因素影响的结果因素。     

Biodegradation of N,N-bis(carboxymethyl)-L-glutamate and its utilization as sole source of carbon,nitrogen, and energy by a Rhizobium radiobacter strain in seawater

Biodegradation of N,N-bis(carboxymethyl)-L-glutamate and its utilization by micro-organisms in marine environments were studied in Organization of Economic Development and Cooperation (OECD) screening tests and with pure bacterial cultures. Biodegradation in excess of 60% was achieved in closed bottle tests in less than 60 days, suggesting complete degradation. The bacterial strain isolated from marine sandy sediment and utilizing N,N-bis(carboxymethyl)-L-glutamate as sole source of carbon, nitrogen, and energy was identified as Rhizobium radiobacter. The strain was capable to mineralize N,N-bis(carboxymethyl)-L-glutamate in seawater and in synthetic media containing 0–60 g L^?1 sea salts. Growth and adaptation studies indicated that N-carboxymethyl-L-glutamate and L-glutamate are main metabolites.     

Preconcentration of cadmium and copper ions on magnetic core–shell nanoparticles for determination by flame atomic absorption

Magnetic core–shell nanoparticles modified by (3-aminopropyl) trimethoxy silane were prepared and used as adsorbent for the extraction and preconcentration of cadmium and copper ions. The ions were desorbed with nitric acid followed by determination with flame atomic absorption spectrometry. The extraction conditions were investigated systematically. The method was applied for the determination of Cd(II) and Cu(II) ions in different water samples. The accuracy was also evaluated through analysis of certified reference material.     

Exploration of spatial morphometry and socioeconomic variables in modelling urban land use change

The incorporation of land use (LU) data with socioeconomic data is a main issue in modelling. This is as a result of difference in data model and scale. This study proposed and tested the change–pattern approach, which allows the incorporation of these data sets in modelling LU change. Focusing on LU dynamics for a selected part of the Thames Gateway within the City of London, the approach tested two different methods of input selection for the modelling operations. Variables selected from these two methods serve as inputs into several neural networks tested in order to identify the direction of change for each of the LU types within the study area. The result shows that direction of LU change across the study area could be identified when spatial morphology of the area and socioeconomic variables are considered. Some classes of change could be identified fairly accurately using landscape metrics indicating level of fragmentation, extent of LU patches, shape complexity of LU patches in combination with some socioeconomic variables.