利用复介电常数和BP人工神经网络进行浓度预测的研究

制备了一定含水率不同浓度的CuCl2的PVA样品,用微波矢量网络分析仪和微波传感器测量S11参数,计算得到相对复介电常数。以样品相对复介电常数的实部、虚部及对应频率作为输入,以CuCl2溶液的浓度作为输出,建立BP人工神经网络模型。用训练样本集对网络训练后,检验样本的预测结果与实际值最大误差为0.97%。结果表明,利用复介电常数和BP人工神经网络进行浓度预测是一种很好的方法,进而为环境监测提供了方法依据。     

脉冲电晕等离子体活化纯CO2的反应

研究了在常温常压下，脉冲电晕等离子体对温室气体CO2的活化与转化分别考察了反应气体流速、脉冲电压峰值、放电频率对纯CO2转化的影响．结果表明，CO2主要分解为CO和O2，另有少量积碳和臭氧产生；随着反应气体流速的增加，CO2转化率、CO产率和选择性降低；随着脉冲电压峰值和放电频率的升高，CO2转化率、CO产率增大，CO选择性降低．     

秦巴山地西部地区2015—2018年大气污染物变化特征及潜在来源分析

为了研究我国南北过渡带西部地区大气污染物变化特征，选取了秦巴山地西部陇南市2015-2018年大气主要污染物监测资料和气象数据，分析了各污染物的浓度水平、变化特征及与气象条件的关系，并利用HYSPLIT后向轨迹模式分析了该地区污染物潜在来源（WPSCF和WCWT）.结果表明：4年来，秦巴山地西部地区空气环境质量较好，空气质量优良率达94.24%，6项大气污染物SO₂、NO₂、CO、O₃、PM_2.5、PM₁₀平均浓度分别为19.02、23.35、701.41、83.11、30.57、56.50 μg·m^-3，均未超过国家二级标准.PM₁₀是该地区首要污染物中天数最多的，达94 d.污染物SO₂、NO₂、CO、PM_2.5和PM₁₀的季节变化和日变化特征具有较好一致性，呈"U"型变化，O₃呈单峰型，在夏季和午后浓度到达高值.颗粒污染物PM₁₀、PM_2.5与气态污染物SO₂、NO₂、CO均呈现较好正相关，气温、降水量和风速风向对污染物浓度有较大的影响.该地区冬季空气质量明显差于其他季节，颗粒物浓度主要受供暖期化石燃料燃烧和外来气流的影响.     

Model calculating annual mean atmospheric dispersion factor for coastal site of nuclear power plant

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利用人工神经网络对空气中O3浓度进行预测

将人工神经网络应用于对空气中O3的浓度预测,提出了完整的预测模型,选取风速、风向、相对湿度、云量、平均气温、最高气温等6项气象因子作为输入量,经过两个月的预测实验,结果表明,实测值与预测值的平均相对误差为21.49%,相关系数为0.837.表明人工神经网络对O3的浓度预测是一种有效的工具.     

基于BN的锅炉超压爆炸事故原因推理与故障诊断

分析了锅炉爆炸的原因,讨论了事故树与贝叶斯网络的特点,编制了以"锅炉超压爆炸事故"为顶事件的事故树,将其转化为锅炉超压爆炸事故贝叶斯网络拓扑结构,利用贝叶斯网络的技术优势,针对锅炉超压爆炸事故贝叶斯网络拓扑结构分别进行了原因推理与诊断推理。实例应用表明,在原因推理中,根节点V_1(压力上升)发生时锅炉超压爆炸事故发生的可能性最高;在诊断推理中,当锅炉超压爆炸事故发生时,根节点V_1(压力上升)一定发生,其次为V_4(定压不准),V_(11)(未装疏水管)发生的可能性最小,为企业进行锅炉超压爆炸事故预测与故障诊断提供了理论依据。     

煤尘润湿接触角的BP神经网络估算方法研究

通过采集和测定35个矿区煤样品的化学组成、结构参数和润湿接触角,构建了以13个影响因子为输入参数和以接触角为输出目标的3层BP人工神经网络,并利用该模型估算煤尘润湿接触角。结果表明,隐含层节点数为19时,接触角估算值与实测值的决定系数R2=0.957,平均相对误差为4.59%,表明基于BP神经网络建立的煤尘润湿接触角估算模型具有很高的精度。     

Elman神经网络在矿井突水水源判别中的应用

矿井突水是矿建与生产过程中最具威胁的自然灾害之一,准确判别突水水源是防治水害的关键。选取6种离子的质量浓度作为突水水源的判别因素,将河南省焦作矿区不同水层的39组水化数据以2种样本设计方案进行Elman神经网络模型的构建与检验。以不同的35组水源样品作为训练样本,运用Matlab软件进行Elman神经网络训练,将所建立的判别模型应用于(相应的)4组待测样本的判别,并与DDA、FDA、Bayes三种判别方法的判别结果进行分析比较。2种方案应用结果表明:将具有非线性动态特征的Elman神经网络应用于突水水源判别,在结合相应的水文地质条件前提下,可以准确判断突水来源;矿井多年的开采促使地下各水层水质呈动态变化,Elman神经网络判别模型能够反映这种变化特性,对探寻地下水运移与演化具有一定的应用价值。     

A framework for sustainable supply chain in emerging economies

Sustainable supply chains (SSC) are an essential topic in emerging economies for achieving sustainable development. Because of their rapid economic growth, emerging economies are experiencing both sides of this development: the well‐being enhancement and the environmental impact. However, decision makers and practitioners do not have robust models for managing supply networks that consider the sustainability complexities. This study aims to provide a better understanding of SSC in emerging economies and proposes a generic framework to improve the decision‐making process. Based on a review of the literature, a decision framework for sustainability is proposed. A green sustainable decision concept is also discussed in the SSC literature. This study has implications for scholars, decision makers, and practitioners interested in advancing the SSC field based on an extension of the framework.     

Climatic Controls on Watershed Reference Evapotranspiration Varied during 1961–2012 in Southern China

Reference evapotranspiration (ET_o) is an important hydrometeorological term widely used in understanding and projecting the hydrological effects of future climate and land use change. We conducted a case study in the Qinhuai River Basin that is dominated by a humid subtropical climate and mixed land uses in southern China. Long‐term (1961–2012) meteorological data were used to estimate ET_o by the FAO‐56 Penman–Monteith model. The individual contribution from each meteorological variable to the trend of ET_o was quantified. We found basin‐wide annual ET_o decreased significantly (p < 0.05) by 3.82 mm/yr during 1961–1987, due to decreased wind speed, solar radiation, vapor pressure deficit (VPD), and increased relative humidity (RH). However, due to the increased VPD and decreased RH, the ET_o increased significantly (p < 0.05) in spring, autumn, and annually at a rate of 2.55, 0.56, and 3.16 mm/yr during 1988–2012, respectively. The aerodynamic term was a dominant factor controlling ET_o variation in both two periods. We concluded the key climatic controls on ET_o have shifted as a result of global climate change during 1961–2012. The atmospheric demand, instead of air temperature alone, was a major control on ET_o. Models for accurately predicting ET_o and hydrological change under a changing climate must include VPD in the study region. The shifts of climatic control on the hydrological cycles should be considered in future water resource management in humid regions.