阳离子型聚合物对低温低浊水的絮凝效果与形态学特性

采用微絮凝-深床直接过滤工艺,以西安市曲江水厂低温低浊水质为原水(水温低于10 ℃,初始浊度低于10NTU),投加阳离子型聚合物(简称CP)作主混凝剂或助凝剂,借助分形数学理论与图像分析技术,对滤料粒径、原水浊度、原水温度、药剂种类、聚合物分子量及投加量、混合强度等因素对处理效果的影响进行了研究,分析探讨了不同药剂处理低温低浊水的作用机理、絮凝形态学特征以及絮体结构的分形特性.结果表明,①当温度低于4 ℃、初始浊度小于4NTU时,不宜单独采用Al2(SO4)3或PAC作为絮凝剂;当温度为4～10 ℃、初始浊度小于10NTU时,如果只投加Al2(SO4)3或PAC作为絮凝剂,宜用细砂滤料过滤;低温低浊条件下,无机混凝剂形成的絮体粒径小、结构松散脆弱、有效质量密度低、沉速慢,但表征絮体分形特性的分维值较高.②CP作絮凝剂能显著改善低温低浊水的絮凝效果与过滤性能,但混合强度需增大,宜用粗砂滤料过滤;③单独用CP作絮凝剂时,宜投加分子量较低的弱阳性聚合物或投加低剂量较高分子量的强阳性聚合物;CP用作助凝剂时,能显著减少主混凝剂用量,但宜投加强阳性聚合物或增加弱阳性聚合物的剂量.④CP兼备电性中和与吸附架桥絮凝作用,能形成粒径较大、吸附性能与过滤性能良好的网状絮体构型,其有效质量密度高,产生的污泥量少,污泥沉降速度快,脱水效果好,但分维值低.⑤各种水处理药剂的处理效果为:Al2(SO4)3(或PAC) CP＞CP＞PAC＞Al2(SO4)3,这种差别由絮体的形态学特性与构型特征各异引起.     

反硝化条件下有机碳低丰度河床沉积层中的苯胺降解

采集渭河河床沉积物,研究了反硝化条件下有机碳低丰度的河床沉积层中苯胺降解.结果表明,反硝化条件下,苯胺在有机碳低丰度的河床沉积层中可生物降解.使该环境中苯胺(约50 mg/L)降解近95%,当硝酸盐为30.69、184.16、245.54 mg/L时,降解时间分别约为20、45、70 d.在上述环境中另加35.98 mg/L乙酸盐后,苯胺降解速度在硝酸盐为184.16 mg/L时最大,硝酸盐为30.69 mg/L时最小.当硝酸盐为30.69 mg/L,不加乙酸盐,27 d苯胺降解约95%;添加35.98 mg/L乙酸盐后,实验进行了47 d还仍有近13 mg/L苯胺残留,说明外加碳源(乙酸盐)对苯胺降解具有抑制作用.但当硝酸盐为184.16、245.54 mg/L时,外加碳源(乙酸盐)则强化苯胺降解.水合金属氧化物对苯胺降解具有促进作用.     

新型树脂对氯酚类物质的吸附研究

合成了乙酰氯修饰树脂NDA-O,与Purolite公司的MN-200相比,合成的树脂比表面积较小,但酸性官能团较多.用两种树脂吸附2-氯酚、4-氯酚和2,4-二氯酚发现,吸附结果可以用Langmuir和Freundlich等温吸附方程很好地模拟,合成的NDA-O对3种氯酚类物质的吸附能力优于MN-200.两种树脂对氯酚类物质的吸附量随着温度的升高而减少,表明吸附是放热过程,同时对氯酚类物质的吸附焓变随着吸附量的增大而减小.对同一种树脂来说,吸附量随着氯代程度的增加而增加,取代氯数相同的时候,吸附量与吸附质的K..成正相关.进一步研究了pH对吸附的影响,在pH大于9时,3种氯酚类物质的吸附量均急剧减少.     

综合资源立法是资源可持续利用的法律保证

资源的有效保护和永续利用在我国可持续发展战略中占有十分重要的地位。本文分析了我国资源法制建设中存在的问题，阐述了综合资源立法在资源法制建设体系中的意义。     

Comparative study on performances of a heat-pipe PV/T system and a heat-pipe solar water heating system

The heat-pipe solar water heating (HP-SWH) system and the heat-pipe photovoltaic/thermal (HP-PV/T) system are two practical solar systems, both of which use heat pipes to transfer heat. By selecting appropriate working fluid of the heat-pipes, these systems can be used in the cold region without being frozen. However, performances of these two solar systems are different because the HP-PV/T system can simultaneously provide electricity and heat, whereas the HP-SWH system provides heat only. In order to understand these two systems, this work presents a mathematical model for each system to study their one-day and annual performances. One-day simulation results showed that the HP-SWH system obtained more thermal energy and total energy than the HP-PV/T system while the HP-PV/T system achieved higher exergy efficiency than the HP-SWH system. Annual simulation results indicated that the HP-SWH system can heat the water to the available temperature (45°C) solely by solar energy for more than 121 days per year in typical climate regions of China, Hong Kong, Lhasa, and Beijing, while the HP-PV/T system can only work for not more than 102 days. The HP-PV/T system, however, can provide an additional electricity output of 73.019 kWh/m², 129.472 kWh/m², and 90.309 kWh/m² per unit collector area in the three regions, respectively.     

The determination and matching analysis of pinch point temperature difference in evaporator and condenser of organic rankine cycle for mixed working fluid

Exergo-economic analysis of the pinch point temperature difference (PPTD) in both evaporator and condenser of sub-critical organic Rankine cycle system (ORCs) are performed based on the first and second laws of thermodynamics. Taking mixture R13I1/R601a as a working fluid and the annual total cost per net output power Z as exergo-economic performance evaluation criterion, the effects of PPTD in evaporator ΔT_e, and the PPTD ratio of condenser to evaporator y, on the exergo-economic performance of ORCs are analyzed. Moreover, how some other parameters influence the optimal PPTD in evaporator ΔT_e,opt and the optimal PPTD ratio of condenser to evaporator y_opt are also discussed. It has been found that the exergo-economic performance of ORCs is remarkably influenced by ΔT_e and y, and there exists ΔT_e,opt and y_opt. In addition, ΔT_e,opt and y_opt are affected by heat transfer coefficient ratio of condenser to evaporator ß, the temperature of working fluid at dew point in condenser T_1a, and composition of R13I1/R601a: larger ß and T_1a lead to lower ΔT_e,opt and y_opt; by contraries, larger mass fraction of R13I1 makes ΔT_e,opt and y_opt increase, and y_opt increases linearly. The effects of the temperature of working fluid at bubble point in evaporator T_3a, mass flow rate of exhaust flue gas m_g, and inlet temperature of exhaust flue gas T_gi on ΔT_e,opt and y_opt are very slight. For comparison, three additional working fluids, namely R601a, R245fa, and 0.32R245fa/0.68R601a, are also taken into account.     

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.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

Effects of sediment burial disturbance on macro and microelement dynamics in decomposing litter of <Emphasis Type="Italic">Phragmites australis</Emphasis> in the coastal marsh of the Yellow River estuary,China

From April 2008 to November 2009, a field decomposition experiment was conducted to investigate the effects of sediment burial on macro (C, N) and microelement (Pb, Cr, Cu, Zn, Ni, and Mn) variations in decomposing litter of Phragmites australis in the coastal marsh of the Yellow River estuary. Three one-off sediment burial treatments [no sediment burial (0 mm year^?1, S₀), current sediment burial (100 mm year^?1, S₁₀), and strong sediment burial (200 mm year^?1, S₂₀)] were laid in different decomposition sites. Results showed that sediment burials showed significant influence on the decomposition rate of P. australis, in the order of S₁₀ (0.001990 day^?1)?≈?S₂₀ (0.001710 day^?1)?>?S₀ (0.000768 day^?1) (p?<?0.05). The macro and microelement in decomposing litters of the three burial depths exhibited different temporal variations except for Cu, Zn, and Ni. No significant differences in C, N, Pb, Cr, Zn, and Mn concentrations were observed among the three burial treatments except for Cu and Ni (p?>?0.05). With increasing burial depth, N, Cr, Cu, Ni, and Mn concentrations generally increased, while C, Pb, and Zn concentrations varied insignificantly. Sediment burial was favorable for C and N release from P. australis, and, with increasing burial depth, the C release from litter significantly increased, and the N in litter shifted from accumulation to release. With a few exceptions, Pb, Cr, Zn, and Mn stocks in P. australis in the three treatments evidenced the export of metals from litter to environment, and, with increasing burial depth, the export amounts increased greatly. Stocks of Cu and Ni in P. australis in the S₁₀ and S₂₀ treatments were generally positive, evidencing incorporation of the two metals in most sampling times. Except for Ni, the variations of C, N, Pb, Cr, Cu, Zn, and Mn stocks in P. australis in the S₁₀ and S₂₀ treatments were approximated, indicating that the strong burial episodes (S₂₀) occurred in P. australis marsh in the future would have little influence on the stocks of these elements. With increasing burial depths, the P. australis was particularly efficient in binding Cu and Ni and releasing C, N, Pb, Cr, Zn, and Mn, implying that the potential eco-toxic risk of Pb, Cr, Zn, and Mn exposure might be very serious. This study emphasized the effects of different burials on nutrient and metal cycling and mass balance in the P. australis marsh of the Yellow River estuary.