C3H6选择性催化还原NO催化剂Ag／γ—A12O3的研究

采用低负载量的(0～3％)Ag／γ-A12O3体系作为C3H6选择性催化还原(C3H3-SCR)NO的催化剂，考察了银负载量、反应温度、空速对NO转化率的影响．同时对于原料气中氧的作用进行了研究．结果表明：银负载量为1．5％时，催化剂表现出最高的反应活性；在考察的条件范围内，原料气中含氧对NO的还原有促进作用，适宜的氧浓度为0．27％；反应温度的过高会导致还原气中C3H6大幅度转化为CO2。     

基于霍耳效应的气敏传感器的研制

利用霍耳效应成功地研制了WO3半导体气敏传感器.这种新型传感器不仅可以根据需要输出几毫伏至几十毫伏甚至几百毫伏的直流电压,而且具有其它半导体传感器所没有的优点--无需另外加热.在实验中对NO2气体进行检测,结果表明:传感器的输出霍耳电动势在几毫伏到几百毫伏之间,且随待测气体浓度变化而变化.因此,利用霍耳效应制作气敏传感器是一条完全可行的新思路.     

粉煤和石灰石加入位置对循环流化床燃煤过程NOx与N2O排放的影响

在30kW的循环流化床(CFB)上进行3种煤的燃烧实验,考察了粉煤和脱硫剂加入位置、分级燃烧以及空气过剩系数对NOX和N2O排放的影响。所采用的CFB燃煤系统由提升管和下行床构成,提升管主要用于粉煤燃烧,下行床主要用于固体床料循环和粉煤热解。粉煤或脱硫剂分别自传统的一次空气布风板上方和下行床上部两个位置加入。结果表明,在不加脱硫剂的条件下,降低空气过剩系数和一次空气化学计量比均可有效降低NO排放,但对N2O排放则呈现上升、下降和无明显变化多种趋势。当粉煤加入位置自传统的提升管下部改变到下行床上部时,减少空气过剩系数或减少一次空气化学计量比可明显降低其中两种煤的NO排放,并可少量降低另一种煤N2O的排放;从下行床加入粉煤时,空气分级和低O2燃烧对NO排放的影响程度有所减弱。最后,对一种煤进行了脱硫实验,随Ca/S摩尔比的升高,SO2排放显著降低,NO排放升高,而N2O则先上升后下降;且自下行床加入时,NO排放更低;CaCO3加入位置变化对N2O排放无明显影响。     

天然气再燃降低锅炉NOx的排放

介绍了常规天然气再燃技术的原理、特点及缺点 ,列举了国外主要天然气再燃的应用项目。介绍了我国 NOX污染现状 ,过去我国环保要求较低加上经济上的考虑 ,对 NOX脱除未予应有关注 ,当前控制 NOX的应用方法皆不尽理想。天然气再燃技术降 NOX效果好 ,技术成熟 ,投资较少 ,无二次污染 ,我国的天然气储备丰富 ,随着环保要求的提高 ,该技术将得到广泛的应用。最后介绍了高级天然气再燃 (AGR)、淡燃料天然气再燃和胺增强天然气再燃 (AEFLGR) 5种其他再燃技术     

煤种对超细煤粉再燃还原NO效率的影响

以三种煤质差异较大的超细煤粉作为再燃燃料,用N2、O2、CO2、NO配制模拟烟气,在立式管式携带炉中,研究了煤种对再燃降低NO效率的影响.结果表明,在相同的条件下,挥发分越大的煤种再燃还原NO效率越高;对于煤质差异较大,尤其是挥发分含量差别较大的超细煤粉,其含N量对再燃还原NO效率的影响将被挥发分的影响所掩蔽.     

冬小麦-夏玉米轮作周期内碱解氮、硝态氮时空变化及施氮安全值的研究

利用 3年 6季氮肥试验研究了冬小麦—夏玉米轮作制度下 ,冀东褐土剖面碱解氮、NO-3 -N含量时空迁移变化和土壤氮素安全值指标。结果表明 ,土壤碱解氮自上而下依次递减 ,NO-3 -N呈“V”字型变化 ,二者均与施肥水平及植株生长状况密切相关 ,在 2 0～ 40cm区域出现碱解氮亏缺区 ,N2处理较为明显。N2处理的地下水NO-3 -N含量已超标 ,建议高产条件下土壤环境投氮安全值为 42 0～ 450kghm- 2 较为适宜     

外源NO和ABA对杨树气孔运动和SOD及POD活性的影响

研究了外源NO和脱落酸(ABA)对杨树气孔运动和SOD、POD活性的影响。结果表明：NO和ABA均可诱导杨树叶片气孔关闭，且NO有加强ABA诱导气孔关闭的作用。NO清除剂(C—PTIO)能显抑制NO和ABA对气孔关闭的诱导效应。不同浓度硝普钠(SNP)和ABA处理杨树离体叶片，SOD活性变化不明显，POD活性受到显抑制。粗酶液的体外实验结果表明，不同浓度SNP对POD活性的抑制呈明显的浓度及时间效应；而ABA对POD活性则几乎没有影响。说明在ABA调控气孔运动的过程中需要NO的参与，由此推测ABA对杨树叶片气孔运动的调节与NO对POD的抑制有关。     

Assessing the cyclic-variability of spark-ignition engine running on methane-hydrogen blends with high hydrogen contents of up to 50%

The cyclic variability in a spark-ignition (SI) engine is examined fueled with methane/hydrogen blends with the use of an in-house computational fluid dynamics (CFD) code. A recent methodology is followed, which has been developed with the main aim at providing accurate predictions of the coefficient of variation (COV) of the indicated mean effective pressure (IMEP) in a fraction of time. Instead of simulating several tens of engine cycles, the methodology is based on the numerical results obtained from just 5 cycles, which are then processed for developing suitable fitted correlations of the main parameters as a function of a normalized distance. The latter expresses the distance of the spheres of the initial flame within the computational cell at the spark-plug region with the local turbulent eddy, and provides a smooth transition from the laminar burning regime to the fully turbulent one. This sub-model is included in the ignition numerical approach and is applied here in a SI engine with 3 different hydrogen contents, 10%, 30% and 50%, and three equivalence ratios, 1, 0.8 and 0.7, showing that the COV of IMEP is well predicted compared to the available measured data. Other parameters of engine cycle variations are also examined, such as the distribution of the IMEP. The variability of NO (nitric oxide) emissions is also examined, showing that for the stoichiometric cases it follows a distribution similar to a normal (Gaussian) one, while for lower ratios it is positively skewed. Overall, the methodology seems to provide reliable results for the whole range of the operating conditions examined, while the next steps of this activity will focus on similar cases for engine with variable speed and load, with the final goal to include additional mechanisms that contribute to the engine cycle variations.     

Studies on solvent extraction of La(III) using [A336][NO3–] and modeling by statistical analysis and neural network

Solvent extraction of La(III) from acidic nitrate medium has been studied with [A336][NO₃^–] in kerosene. The factors affecting the extraction of La(III) like equilibration time, nitrate ion, extractant and La(III) concentrations, aqueous acidity, O/A ratio variation, nature of diluent, and temperature have been investigated. McCabe-Thiele diagram has been plotted to find out the actual number of theoretical stages needed for complete extraction of lanthanum. The solvent has been successfully regenerated for further use after stripping of the metal. IR studies of [A336][NO₃^–] and La(III) loaded [A336][NO₃^–] have been carried out. Modeling of extraction data has been done using Multiple linear regression analysis and Artificial Neural Network, and the performances have been compared. Error in each case was evaluated in terms of R² and Root mean squared error (RMSE). Maximum extraction of La(III) was 82% when 0.6 M [A336][NO₃^–] was used for extraction. About 98% of the metal has been recovered using 0.2 M HNO₃ as stripping agent. Extractive separation of La(III) and Sm(III) was maximum (β = 65.2) using 0.1 M [A336][NO₃^–]. IR studies revealed formation of lanthanum complex in the extraction process. Artificial Neural Network proved to be better over Multiple linear regression in data prediction.