曝气生物滤池处理焦化废水脱氮的研究

用曝气生物滤池处理焦化废水的研究表明,在曝气量(Q)=0.25L/min,温度(T)=20℃,pH=8,反冲洗周期为3个运行周期内,NH3-N的去除速率达44 mg/L·h.同时在滤池中出现了亚硝酸盐积累现象,且受反冲洗的影响较大.     

微波辅助空气氧化水溶液中的PCP

采用活性炭为催化剂,对微波辅助空气氧化水溶液中的五氯酚进行了处理研究.考察了活性炭投加量、微波功率、辐射时间和通气量对溶液中五氯酚的去除率的影响.结果表明,在通气量为0.2 L/min,微波功率800 W和微波辐射60 min时,五氯酚的去除率可达到90%以上;对微波辐射前后的滤液进行紫外扫描和pH分析,可证实五氯酚被降解.     

低温等离子体催化氧化甲烷的实验研究

采用电容耦合等离子体和催化剂协同作用对干空气中的甲烷进行了氧化实验，并和没有放置催化剂时进行了对比，结果表明，放置催化剂后甲烷的分解效率明显提高，反应产物中CO2的选择性增加，副产物NO和NO2的浓度减少．反应所需的能耗降低。甲烷的最终氧化产物为CO、CO2和H2O。     

人工湿地处理系统中多环芳烃的行为

采用Oasis HLB柱固相萃取的前处理技术,以气相色谱质谱联用仪（GC/MS）的分析方法,对我国北方永定河上游黑土洼人工湿地中多环芳烃污染的特征以及分布规律进行了研究.结果表明,该湿地系统以低环数多环芳烃污染为主,其中浓度最高的是菲和蒽,未检出高环数（5环、6环）多环芳烃.通过比较进水和出水的浓度,湿地系统总体上不能有效去除进水中的多环芳烃.但是比较不同工艺单元进出水浓度,复氧、植物根系及微生物等均影响到多环芳烃的去除效果,去除率在28%～65%之间.     

污水厂Carrousel氧化沟溶解氧和速度分布的研究

对长沙某污水处理厂的Carrousel氧化沟进行现场测试，获得不同截面的溶解氧和速度的数据，探讨了表曝机台数、沟深、沟的长宽比、进出水位置对溶解氧和速度的影响。初步掌握了该Carrousel氧化沟内溶解氧和速度的变化规律，认为通过调节倒伞形表面曝气机，可以在沟内不同区段创造好氧与缺氧的环境，为最终实现同步硝化反硝化的高效运行做准备。     

啤酒生产废水处理设计及运行研究

分析了年产15万t啤酒的生产废水情况,介绍了采用IC厌氧反应器-一体化氧化沟处理该生产废水(7 200 m3/d)的工艺路线和主要设计参数,讨论了废水处理系统的运行情况.其中IC厌氧反应器的HRT为8 h,运行温度为35～37 ℃;一体化氧化沟包括氧化沟段和沉淀池段,总尺寸为40 m×24 m×6 m.IC厌氧反应器COD容积负荷在7.78 kg/(m3·d)时,COD去除率为84%左右.整套废水处理系统的COD总去除率为98%左右,出水COD小于80 mg/L.厌氧所产生的沼气可用于废水的升温.     

二氧化氯的发生技术

本文对国内外ＣｌＯ２的发生技术进行了综合评述，详尽地介绍了作者自行研制的ＣｌＯ２发生技术和装置，认为我国推广应用ＣｌＯ２消毒处理饮用水的时机已经成熟。     

Kinetics of heat-assisted persulfate oxidation of methyl tert-butyl ether (MTBE)

The kinetics of heat-assisted persulfate oxidation of methyl tert-butyl ether (MTBE) in aqueous solutions at various pH, temperature, oxidant concentration and ionic strength levels was studied. The MTBE degradation was found to follow a pseudo-first-order decay model. The pseudo-first-order rate constants of MTBE degradation by persulfate (31.5 mM) at pH 7.0 and ionic strength 0.11 M are approximately 0.13 x 10(-4), 0.48 x 10(-4), 2.4 x 10(-4) and 5.8 x 10(-4) S(-1) at 20, 30, 40 and 50 degrees C, respectively. Under the above reaction conditions, the reaction has an activation energy of 24.5 +/- 1.6 kcal/ mol and is influenced by temperature, oxidant concentration, pH and ionic strength. Raising the reaction temperature and persulfate concentration may significantly accelerate the MTBE degradation. However, increasing both pH (over the range of 2.5-11) and ionic strength (over the range of 0.11-0.53 M) will decrease the reaction rate. Reaction intermediates including tert-butyl formate, tert-butyl alcohol, acetone and methyl acetate were observed. These intermediate compounds were also degraded by persulfate under the experimental conditions. Additionally, MTBE degradation by persulfate in a groundwater was much slower than in phosphate-buffer solutions, most likely due to the presence of bicarbonate ions (radical scavengers) in the groundwater.     

Chemical kinetic modeling of de novo synthesis of PCDD/F in municipal waste incinerators

A kinetic model is developed for de novo synthesis of PCDD/F from carbon in incinerator fly ash. The main mechanistic steps considered in the model are carbon gasification, PCDD/F formation, desorption and degradation. Rate equations are derived which can relate PCDD/F formation with process variables including carbon concentration of fly ash, partial pressure of oxygen, reaction temperature and time. The kinetic model has been verified using laboratory de novo synthesis data reported in the literature. When the model is applied to industrial incinerator conditions, PCDD/F formation levels of 0.1-0.5 microg/N m3 in the gas phase and 0.1-1.2 microg/g in the solid phase are calculated, and both are in good agreement with incinerator measurements.     

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.