火焰原子吸收光度法测定废水中总铬

阐述了硝酸－高氯权消解、火焰原子吸收法直接测定工业废水中总铬。方法特征度０．０５４ｍｇ／Ｌ（１％吸收）,检测限０．０３６ｍｇ／Ｌ,加标回收率９６％～１０２％,相对标准左１％～１７％。     

滑移装置抑制甲烷爆炸影响分析

为减少瓦斯二次爆炸带来的危害,研发新型抑爆弹性滑移装置,并将滑移装置与固定装置对比,分析其对9.5％甲烷/空气预混气体爆炸抑制效果.结果表明:滑移装置抑爆效果优于固定装置;滑移装置能缩短火焰燃烧时间,固定装置超压峰值高于滑移装置;由于轻碳板反向速度提高,弹性系数为0.85 N/mm的滑移装置对火焰和超压抑制效果优于弹性...     

基于腐蚀损伤表征参数的铝合金预腐蚀疲劳裂纹扩展修正模型

服役环境中的腐蚀介质会在铝合金表面形成以腐蚀坑为代表的典型点蚀形貌。在疲劳载荷作用下,这些腐蚀坑会有助于裂纹的萌生,加速并影响其扩展行为,威胁结构安全。考虑腐蚀坑的影响,建立了一个基于腐蚀损伤表征参数的线弹性裂纹扩展表征模型。利用航空结构中常见承力构件所采用的LD2铝合金进行验证性实验。结果表明,所建立的模型能够反映出腐蚀损伤对于裂纹扩展行为的影响,对于铝合金构件的腐蚀疲劳寿命预测和损伤容限评估具有参考价值。     

基于遗传算法的人工神经网络在降水酸度预测中的应用

在误差反向传播(Back Propagation)算法的人工神经网络结构模型的基础上，应用遗传算法训练神经网络权重，实现网络结构的优化。用优化后的BP人工神经网络建立了江西省南昌市的降水酸度预测模型。并将该模型预测结果与BP算法和多元线性回归法的预测结果进行了比较。检验结果表明：基于遗传算法的人工神经网络优于BP算法及多元线性回归法，具有良好的预测效果。     

基于神经网络综合建模的区域城市群发展脆弱性评价——以滇中城市群为例

城市脆弱性是评价城市发展韧性状况的有效测度。目前我国对于城市脆弱性的研究以运用统计学方法评价特殊城市为主,尚未形成对区域城市群具有普遍适用性的科学客观评价方法。以滇中城市群为例,从环境系统、经济系统、社会系统三个方面综合构建城市脆弱性评价体系,采用熵值法和BP神经网络综合建模方法,对2007—2016年10年间滇中城市群的城市脆弱性进行评价。结果表明:滇中城市群的城市脆弱性总体呈现下降趋势,但城市组团之间差异较大,呈现出发展中的不均衡性。评价结果对滇中城市群韧性发展规划具有重要指导意义,为区域城市群发展脆弱性研究提供一种科学评价方法。     

Dust cloud evolution and flame propagation of organic dust deflagration under low wall influence

The present study discusses experiments on organic dust explosions in a setup with low wall influence. The proposed apparatus decouples the dust dispersion and the deflagration event in two separate compartments. The use of a continuous-wave laser to illuminate the centre plane of the observation chamber allows capturing both, the dust cloud and the flame during the same experiment and eliminates typical problems caused by the limited dynamic range of high-speed cameras. A k-means clustering method is used for image segmentation to obtain the spatial extent and the propagation velocities of the unreacted particle cloud and the flame zone. Spatially resolved velocities are calculated by the additional use of an optical flow method. The main goal of the presented setup and image processing method is to provide high quality validation data for the development of numerical models on dust deflagration.     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.