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%.     

烟草烟雾对室内空气颗粒物浓度的影响

采用电称冲击低压系统(ELPI)将无烟室和吸烟室内的空气颗粒物(0.03～10.00 μm)分成12级,对其粒子数和质量浓度进行测定.结果表明,吸烟室PM0.03～10.00的日平均粒子数和质量浓度分别是无烟室的1.50、1.13倍.烟草烟雾对室内颗粒物粒子数的影响集中在0.03～1.00 μm粒径段;对室内颗粒物质量浓度的影响表现为双模态结构,峰值分别在0.20～0.70、5.00～8.20 μm粒径段.烟草烟雾颗粒物的粒子数和质量浓度随烟雾消散时间的增加而减少,粒径越小,减小的越明显;烟草烟雾颗粒物在室内消散缓慢,会在长时间内造成影响,应引起足够关注.     

壳聚糖/纳米CdS复合颗粒可见光光催化降解猩红B动力学研究

采用仿生矿化法制备了壳聚糖/纳米CdS复合颗粒光催化剂,并用于可见光光催化降解猩红B染料模拟废水,研究了猩红B初始浓度、pH、催化剂投加量和催化剂重复使用次数等因素对猩红B光催化降解的影响.X射线衍射(XRD)分析表明,壳聚糖能有效负载CdS纳米微晶.采用Langmuir-Hinshelwood模型描述壳聚糖/纳米CdS复合颗粒可见光光催化降解猩红B反应动力学行为,在猩红B初始质量浓度较低(≤20 mg/L)时,光催化降解过程符合假一级动力学方程.降低猩红B初始浓度和溶液pH都可显著增大光催化降解速率常数;催化剂投加量小于0.7 g/L时,光催化降解速率随其增加而增大,但催化剂投加量过大会使光催化降解速率减小;催化剂重复使用第5次时,猩红B光催化降解速率常数仍为第1次使用时的63.4%.     

Cr(Ⅵ)污染土壤的热解还原无害化处理

提出了用热解还原法对含铬土壤进行无害化处理的新技术,研究了热解温度、热解时间及土壤有机质对铬无害化处理的影响,分析了热解前后土壤中铬的元素形态的变化.同时还探讨了热解还原过程中Cr(Ⅵ)的无害化机制.结果表明,土壤中的有机质在热解还原过程中产生的挥发分对Cr(Ⅵ)的无害化起核心作用;在200～600℃范围内,Cr(Ⅵ)的还原量随着热解温度升高而增大,500.0℃最适合于经济有效地实现Cr(Ⅵ)的热解还原处理;Cr(Ⅵ)的热解还原过程较快.铬的形态分析结果表明,热解后可交换态和碳酸盐结合态铬量大大降低,大部分铬转化成了活性低的残渣态,极大地降低了铬的危害.     

氨水土壤混合物脱除CO2温室气体的实验研究

为了脱除CO2温室气体,提出了利用氨水土壤混合物去除CO2的新方法。分别考察了土壤颗粒粒径、CO2初始流量、氨水浓度（质量比）和温度对CO2脱除量和脱除率的影响。实验结果表明,该方法去除CO2的量较土壤物理吸附量和氨水化学吸收量的总和提高了大约15%;随着氨水浓度的增大,CO2的脱除率和脱除量都增大;随着土壤颗粒粒径和CO2初始流量的增大,CO2的脱除率和脱除量都减小;当温度由22℃升高到31℃,CO2的脱除率随着温度的升高而增大,但是继续升高温度到40℃,CO2的脱除率反而下降。     

In situ removal of copper from sediments by a galvanic cell

This study dealt with in situ removal of copper from sediments through an electrokinetic (EK) process driven by a galvanic cell. Iron (Fe) and carbon (C) were placed separately and connected with a conductive wire. Polluted sediments were put between them and water was filled above the sediments. The galvanic cell was thus formed due to the different electrode potentials of Fe and C. The cell could remove the pollutants in the sediments by electromigration and/or electroosmosis. Results showed that a weak voltage less than 1V was formed by the galvanic cell. The voltage decreased with the increase of time. A slight increase of sediment pH from the anode (Fe) to the cathode (C) was observed. The presence of supernatant water inhibited the variation of sediment pH because H(+) and OH(-) could diffuse into the water. The removal of copper was affected by the sediment pH and the distribution of electrolyte in sediment and supernatant water. Lower pH led to higher removal efficiency. More electrolyte in the sediment and/or less electrolyte in the supernatant water favored the removal of copper. The major removal mechanism was proposed on the basis of the desorption of copper from sediment to pore solution and the subsequent electromigration of copper from the anode to the cathode. The diffusion of copper from sediment to supernatant water was negligible.     

Incorporating layer- and local-scale heterogeneities in numerical simulation of unsaturated flow and tracer transport

This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty assessment of unsaturated flow and tracer transport in the unsaturated zone of Yucca Mountain, USA. The layer-scale heterogeneity is specific to hydrogeologic layers with layerwise properties, while the local-scale heterogeneity refers to the spatial variation of hydraulic properties within a layer. A Monte Carlo method is used to estimate mean, variance, and 5th, and 95th percentiles for the quantities of interest (e.g., matrix saturation and normalized cumulative mass arrival). Model simulations of unsaturated flow are evaluated by comparing the simulated and observed matrix saturations. Local-scale heterogeneity is examined by comparing the results of this study with those of the previous study that only considers layer-scale heterogeneity. We find that local-scale heterogeneity significantly increases predictive uncertainty in the percolation fluxes and tracer plumes, whereas the mean predictions are only slightly affected by the local-scale heterogeneity. The mean travel time of the conservative and reactive tracers to the water table in the early stage increases significantly due to the local-scale heterogeneity, while the influence of local-scale heterogeneity on travel time gradually decreases over time. Layer-scale heterogeneity is more important than local-scale heterogeneity for simulating overall tracer travel time, suggesting that it would be more cost-effective to reduce the layer-scale parameter uncertainty in order to reduce predictive uncertainty in tracer transport.     

复合沸石滤床修复北方景观水体的实验研究

以天然矿物质沸石、细砂及煤渣取代传统滤料构建复合基质生态床,表面种植景观植物,采用下向流-上向流运行方式修复北方景观水体。分别进行静态实验及不同循环速率下的动态实验,考察对水体污染物去除过程。结果表明,2种运行方式下对水体NH⁺₄-N去除率都在85%以上,其中以1 h为循环周期的运行方式去除率达97%,较静态提高12.8%;TN去除率最高为84%;TP去除不稳定,过程缓慢。煤渣层对NH⁺₄-N的去除效果差,硝化作用不彻底与反硝化作用的加强使下层出水NH⁺₄-N 、NO^-₂-N及NO^-₃-N浓度均高于上层。提高循环速率有利于对氮的去除。