近10 a武汉市城市热岛效应演变及其#br# 与土地利用变化的关系

城市化进程的加快，农村人口大量涌入城市，城市布局及局部气候改变等因素使得城市热岛效应问题日益突出，已成为当前城市环境研究热点之一。以武汉市为例，应用遥感技术与地理信息系统技术，选取2004~2015年5个时相Landsat系列影像数据，利用单窗算法反演地表温度，并以此为基础进行热岛强度分级，获取了近10 a武汉市城市热岛效应变化结果，并分析了武汉市11个辖区城市热岛效应动态变化特征及热岛效应与土地利用变化的关系。研究结果表明：（1）自2007年后，武汉市老城区热岛面积持续减少，而新城区热岛面积则持续增加，呈现出以老城区为中心向新城区扩张的趋势，至2015年，新老城区热岛面积仅相差20.74 km2；（2）东西湖区、蔡甸区、江夏区与洪山区是近些年城市热岛面积增长较为显著的辖区，其中江夏区的热岛面积年际变化最大，最高值达95.42 km2；（3）城市热岛效应与土地利用类型的面积年际平均值拟合关系显示，2004~2015年，城市热岛效应与建筑用地的R2值最大，为0.681 2，建筑用地面积的增加是城市热岛强度面积扩张的重要影响因素。     

采用支持向量机算法优化电化学处理油田污水的工艺参数

采用支持向量机(SVM)算法,将Box-Behnken设计法与支持向量回归算法(SVR)实验参数优化软件相结合,优化电化学去除油田污水COD的工艺参数。通过量子粒子群算法对SVM算法参数进行优化,从建立的回归模型中找到工艺参数的全局最佳点:电解时间60 min,电解电流3 A,三维电极填充料中石英砂质量695 g。模型得到的COD理论最优去除率为92.48%,验证实验得到的COD去除率为91.43%。     

基于遗传神经网络的除湿机故障诊断与寿命预测

目的识别除湿机的性能状态和预测吸附剂的剩余寿命。方法针对除湿机故障过程缓变的特点,提出一种基于数据驱动的遗传神经网络模型。首先,为解决设备失效程度划分模糊的问题,由5个热力参数组成反映吸附剂劣化程度的特征向量,关联分析得到除湿机的5类故障模式。其次,利用遗传神经网络建立状态参数和故障模式的映射关系。最后,对表征设备吸附能力的主参数进行外推预测。结果训练好的诊断网络可准确地识别出设备的劣化程度及其演变过程,预测网络的预测精度非常高。结论该方法可有效地实现对除湿机的故障诊断与预测。。     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

模糊控制技术在污水处理中的应用

针对污水处理过程中采用传统的流量程序控制和时间程序控制的不足,提出了一种基于单片机的模糊控制方法。该方法以污水流量、溶解氧(DO)浓度和污泥回流比为主要被控对象,以DO浓度为主要控制参数,通过离散计算和在线查表的模糊推理方法可得到最佳的风机转速,使污水处理生化池内DO浓度保持在最佳状态,同时还通过控制曲线展示了当输入污水流量发生扰动时对DO浓度的控制精度,使污水处理质量保持稳定。通过实际应用表明,该控制方法不但能确保出口净化水水质达标,还可以节约15%的电能。     

Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission

Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.     

Fast increasing of surface ozone concentrations in Pearl River Delta characterized by a regional air quality monitoring network during 2006-2011

Based on the observation by a Regional Air Quality Monitoring Network including 16 monitoring stations, temporal and spatial variations of ozone(O3), NO2and total oxidant(Ox) were analyzed by both linear regression and cluster analysis. A fast increase of regional O3concentrations of 0.86 ppbV/yr was found for the annual averaged values from 2006 to 2011 in Guangdong, China. Such fast O3increase is accompanied by a correspondingly fast NOx reduction as indicated by a fast NO2 reduction rate of 0.61 ppbV/yr. Based on a cluster analysis, the monitoring stations were classified into two major categories – rural stations(non-urban) and suburban/urban stations. The O3concentrations at rural stations were relatively conserved while those at suburban/urban stations showed a fast increase rate of 2.0 ppbV/yr accompanied by a NO2 reduction rate of 1.2 ppbV/yr. Moreover, a rapid increase of the averaged O3 concentrations in springtime(13%/yr referred to 2006 level) was observed, which may result from the increase of solar duration, reduction of precipitation in Guangdong and transport from Eastern Central China. Application of smog production algorithm showed that the photochemical O3production is mainly volatile organic compounds(VOC)-controlled. However, the photochemical O3production is sensitive to both NOx and VOC for O3pollution episode. Accordingly, it is expected that a combined NOx and VOC reduction will be helpful for the reduction of the O3 pollution episodes in Pearl River Delta while stringent VOC emission control is in general required for the regional O3 pollution control.     

基于智能优化算法的河流水质模型参数的优化简

为探索河流水质模型参数新的求解方法，根据有限的实测数据，分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法，对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合，并与实测结果进行了比较。结果表明，将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高，而且相对稳定，从而为河流水质模型参数的优化提供了一种新方法。     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．DNS32的产量，分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分（玉米粉、豆饼粉、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L，豆饼粉25．638g／L和K。HPO。3．265g／L时，预测发酵活菌最大生物量为7．079×10^8CFU／mL，实测量为7．194×10^8CFU／mL；人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L时，预测最大值为7．199×10^8CFU／mL，实测量为7．244×10。CFU／mL；最终确定培养基配方：玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L，CaCO3为3．000g／L，MgSO4·7H2O和NaCl均为0．200g／L；优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36．6％。结果表明，在阿特拉津降解菌DNS32发酵培养基组分优化方面，响应面法和基于人工神经网络的遗传算法都是可行的，基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。