二乙基羟胺-臭氧体系降解邻苯二甲酸二甲酯的机理学研究

本研究采用二乙基羟胺（DEHA）促进臭氧（O3）降解水中邻苯二甲酸二甲酯（DMP）,并建立了强化O3氧化能力的O3/DEHA体系。主要探索了在O3/DEHA体系中pH、DEHA投量、臭氧通量和DMP投量对该体系的影响及体系的氧化机理,并通过气相色谱/质谱（GC/MS）对DMP的降解产物进行了分析。结果表明：DEHA能够有效促进臭氧的分解且在酸性条件下（pH=3）明显提高DMP的降解率,DEHA投量为25μΜ时促进效果最佳;DMP降解速率随臭氧通量的增加而增加,随DMP浓度的增加而减小,通过动力学计算,该反应符合拟一级动力学反应;投加叔丁醇（TBA）能够明显抑制DMP的降解,验证了在酸性条件下,·OH对DMP的降解起主要作用;通过产物分析可推导出O3/DEHA体系中DMP 的降解途径主要从苯环上碳链的断裂开始,中间生成酸类和脂类化合物,最终矿化为水和二氧化碳。     

钛合金动态本构模型参数敏感度分析及识别方法

为分析钛合金动态本构模型参数的敏感度,克服了传统单因素分析法的弊端,采用拉丁超立方抽样方法在整个参数空间中抽样,利用非参数统计方法中的Spearman秩相关分析法对本构参数随机输入样本集与其对应的目标函数输出结果集作相关性分析。建立了用Spearman秩相关系数等效求解参数敏感度的表达式,进而实现了参数敏感度的整体性分析,提高了参数敏感度分析结果的可靠性。基于参数敏感度分析结果,为减少本构模型参数识别的计算量,快速、精确、可靠地搜索最优解,本文建立了基于改进小生境算法、可疑峰值点判断策略和局域精确搜索技术的改进遗传算法。该方法克服了基本遗传算法存在的无法同时满足精度、可靠性和节省计算时间的缺陷,避免了容易产生早熟现象,增强了局部寻优能力。数值计算结果表明：本文提出的方法显著提高了遗传算法的全局搜索能力且计算精度高。本文提出的参数敏感度整体性分析方法和改进遗传算法不仅能很好地实现钛合金的动态本构参数识别,而且对其它工程材料本构参数的高精度识别具有重要参考价值。     

超声波灭活水中微生物试验研究

为了探讨超声波灭菌作用机制,进一步为超声波用于净水厂污泥无害化处理提供技术支持,试验采用实验室模拟水样,研究了超声功率(250~1 500 W)、频率(25 k Hz和40 k Hz)对大肠菌群灭活效果、超声空化强度和羟基自由基产量的影响.结果表明:相同时间和频率下,在超声功率为250~1 500 W时,大肠菌群灭活率随着功率的增大先逐渐上升,达到1 250 W后,灭活效果变化不明显;相同时间和功率条件下,40 k Hz灭活效果优于25 k Hz.超声空化强度和羟基自由基的产量与灭活率规律相似,超声灭活率随着超声过程中超声空化强度和羟基自由基产量的增大而增大.通过投加自由基掩蔽剂碳酸氢钠(Na HCO3)掩蔽自由基氧化作用判定羟基自由基在超声灭菌过程中的确产生了灭活作用,但作用比较弱.     

3维Copula函数在降雨特征多变量频率分析中的应用

鉴于径流数据缺乏且难以长期监测而降雨数据相对完整,通常假定降雨和径流同频率,采用设计降雨进行水文分析计算,但此方法很难真实全面地反映降雨变化特征.为此,提出一种基于3维Copula函数的降雨特征多变量频率分析方法.首先利用降雨强度法将连续的降雨时间序列分割成若干个降雨事件,采用年最大值法取样,统计出表征雨量的特征变量,然后引入3维Copula函数构建降雨特征3变量联合概率模型,并以广州1961~2012年历史降雨数据为例进行分析.结果表明,基于3维Copula函数的多变量分析方法计算简单、可靠性高,可以进行3种不同降雨特征变量的组合分析,得到各种不同量级变量的遭遇概率和条件概率,能够更全面地反映降雨特征并更好地满足水文分析计算需求.     

Creep behavior and failure mechanisms of CVI and PIP SiC/SiC composites at temperatures to 1650 °C in air

Creep properties of 2D woven CVI and PIP SiC/SiC composites with Sylramic™-iBN SiC fibers were measured at temperatures to 1650 °C in air and the data was compared with the literature. Batch-to-batch variations in the tensile and creep properties, and thermal treatment effects on creep, creep parameters, damage mechanisms, and failure modes for these composites were studied. Under the test conditions, the CVI SiC/SiC composites exhibited both matrix and fiber-dominated creep depending on stress, whereas the PIP SiC/SiC composites displayed only fiber-dominated creep. Creep durability in both composite systems is controlled by the most creep resistant phase as well as oxidation of the fibers via cracking matrix. Specimen-to- specimen variations in porosity and stress raisers caused significant differences in creep behavior and durability. The Larson-Miller parameter and Monkman-Grant relationship were used wherever applicable for analyzing and predicting creep durability.     

基于光纤陀螺的水布垭水电站大坝面板挠度变形规律分析

水布垭水电站混凝土面板堆石坝采用光纤陀螺(FOG)监测面板挠度变形。通过对监测资料分析,全面揭示了水布垭面板堆石坝面板挠度变形特点和一般规律,即:面板挠度变形整体趋于收敛,坝高1/3附近和运行低水位高程附近面板反弹出现峰值。监测成果为大坝安全运行提供重要依据。     

原水输水渠道中粉末活性炭吸附污染物的模拟研究

在原水输水管渠中投加粉末活性炭,对污染原水进行预处理,已经成为水源地突发污染事故条件下的应急处理技术手段。本文提出了原水输水渠道中粉末活性炭动态沉降条件下,粉末活性炭—污染物耦合数学模型,以水源地硝基苯浓度超标为例(分别超标2、5和10倍),模拟了粉炭对硝基苯的吸附净化效果。得出基本结论为:粉炭吸附污染物的效果既与水流条件(实际水流剪应力或供水水量)有关,也与输水渠道的断面水深有关。在相同的粉炭投加浓度条件下,水流剪应力越低,断面水深越低,污染物吸附平衡浓度就越高。当水源地水质严重超标时(超标5–10倍),应将增加粉末活性炭投加量作为解决方案;而当水源地水质超标不明显时(超标2倍),可在合理调控供水水量和粉炭投加量的条件下,达到供水水质要求。该研究建立的数学模型,对突发污染事故条件下城市应急供水,具有借鉴意义。     

Adsorption behavior of activated carbon derived from pyrolusite-modified sewage sludge: equilibrium modeling, kinetic and thermodynamic studies

Activated carbon was developed from sewage sludge using pyrolusite as an additive. It was demonstrated that the removal efficiency of two synthetic dyes (Tracid orange GS and Direct fast turquoise blue GL) by the produced adsorbent was up to 97.6%. The activated carbon with pyrolusite addition had 38.2% higher surface area, 43.8% larger micropore and 54.4% larger mesopore production than ordinary sludge-based activated carbons. Equilibrium adsorption isotherms and kinetics were also investigated based on dyes adsorption tests. The experimental data were analyzed by the Langmuir and Freundlich models of adsorption, and the results fitted well to the Langmuir isotherm. The kinetic data have been analyzed using pseudo-first-order, pseudo-second-order and intraparticle diffusion equation. The experimental data fitted very well with pseudo-second-order kinetic model. Activation energies for the adsorption processes ranged between 8.7 and 19.1 kJ mol 1. Thermodynamic parameters such as standard free energy (deltaG0), standard enthalpy (deltaH0) and standard entropy (deltaS0) were evaluated. The adsorption of these two dyes on the activated carbon was found to be a spontaneous and endothermic process in nature.     

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.     

Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Understanding the traffic patterns of construction workers on high-risk construction sites is important for implementing behaviour-based safety management. However, safety risks in worker trajectories are a complex system with high uncertainty. This resulted in few studies focusing on analysing the spatial–temporal risk in workers' trajectories from a systematic perspective. This study designs a new framework to explore the spatial–temporal patterns of safety risks in the trajectories of construction workers based on complex network theory. First, an integrated site safety risk classification method by combining hazard sources and group trajectory distribution is developed to accurately describe the spatial distribution of site risks. Second, a new complex network chronnet is used to construct complex networks containing risk information for spatial–temporal analysis. Finally, key risk areas and risk transition patterns are identified through the analysis of network measures. The study also developed a computational program that allows the network to be constructed and analysed in real-time. The feasibility and effectiveness of the method are verified through a case study. The results show that the method can reveal the risk distribution at the micro level, and explore the risk clustering and transition features in worker trajectories at the macro level. The study allows for an accurate analysis of dynamic risk patterns in construction workers' trajectories from a systematic perspective. It can also provide theoretical and practical support for personalized and adaptive behaviour-based safety management for construction workers.