The Relative Importance of Landscape Amenity and Health Impacts in the Wind Farm Debate in Australia

In recent years, Australia has experienced public debate around the growth in wind farms as part of government and community strategies to move from fossil fuels to renewable energy. Many of the arguments put forward by opponents and supporters are similar to those that have been evident in North America and Western Europe, including possible environmental impacts on wildlife, noise levels and, perhaps most prominently adverse changes in ‘landscape amenity’. A concern of increasing prominence is that of adverse human health impacts from proximity to operating turbines. Using analysis of submissions to public inquiries and a small number of detailed interviews, we consider the increased focus in Australia on the health impacts of wind farms. We note that health impacts, as elsewhere in the world, are often part of a suite of objections to wind farm developments but conclude that for some people near such developments, these are the main concern and not a cover or proxy for other concerns, such as changes in landscape amenity and aesthetics.     

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%.     

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.     

甘肃省风力发电现状及发展对策

介绍了甘肃省的风力资源及风力发电的发展现状,重点阐述了白银地区、玉门地区、酒泉瓜州、金昌地区、敦煌地区和武威地区等几个具有代表性地区的风能资源优势及风电建设情况,指出了甘肃省风力发电在技术和政策等方面目前还存在的主要问题,分析了产生这些问题的原因,提出了促进甘肃省风力发电事业发展的对策。     

风电场环境影响评价原则及评价重点研究

针对风电场环境影响评价的评价原则及评价重点进行了探讨性的研究。提出了风电场环境影响评价应以早期介入和避让措施优先为基本原则;对风电场环境影响评价重点进行了识别,并提出风电场选址的环境可行性分析是风电场环评的重中之重;风电项目建设对生态环境的影响及风机噪声、光影影响也是风电场环境影响评价的重点,并创造性的提出风机噪声光影联合防护区的概念,以期对风电场环境影响评价提供理论及技术参考依据。     

安全利用火烧清理法进行脱硅器的清理

针对氧化铝生产中密闭性压力容器脱硅器清理作业环境差、清理难度大、危险性高等特点,结合中国铝业中州分公司脱硅器现场清理检修作业的实践经验,阐述了如何安全利用火烧法进行脱硅器现场清理工作.     

街道峡谷内汽车排放污染物浓度分布的观测与数值模拟

在上海某典型街道峡谷内按一定的空间布点,在一定时段内同时对各布点进行采样并做一氧化碳浓度分析,同时记录车辆种类、车流量、气象条件等,分析街道峡谷内污染物浓度的分布.运用风向频率加权(WDFW)方法,结合大气流动和污染物扩散的CFD模型进行数值模拟计算.结果表明,数值模拟结果和现场观测结果较吻合,建筑物低的一侧污染物浓度远高于建筑物高的一侧污染物浓度,两侧的污染物浓度随着高度的增加而降低.     

开放源对环境空气质量影响的评估技术与实例

开放源排放的颗粒物已经成为城市环境大气颗粒物的主要来源之一,介绍了其对环境空气质量影响的评估技术,采用开放源的动力学经验公式测算S市粉煤灰场、原煤堆场、土堆、沙石料堆4类开放源不同粒径颗粒物的起动风速及扩散距离,并利用箱模型(A值)和源解析(化学质量平衡法,CMB)模型测算开放源的区域年均起尘量及其对环境空气中颗粒物的贡献值.结果表明:开放源排放的颗粒物易升腾而且影响面积很大;S市区开放源类排放的小于100 μm的颗粒物约22.2×104 t/a,小于10 μm的颗粒物约15.8×104 t/a;开放源对环境空气中TSP的贡献值约370 μg/m3,质量浓度分担率达60%,对PM₁₀的贡献值约263 μg/m3,分担率达64%.      

活塞风影响下地铁火灾烟气运动规律的数值模拟研究

地铁车站及地铁列车为人流密集的公众聚集场所,一旦发生火灾事故伤亡损失往往非常惨重,因此深入开展地铁火灾安全的研究有助于地铁安全管理工作。在合理分析地铁站台和列车火灾荷载的基础上,采用数值模拟方法对活塞风情况下地铁区间隧道火灾问题进行了模拟计算和分析,研究了在活塞风影响下的烟气蔓延规律和温度分布情况,其结果有助于提高地铁车站的运营的安全管理和发生火灾后的疏散指挥能力。     

横通道通风对隧道火灾烟气蔓延影响的数值模拟研究

为探究隧道横通道通风对隧道火灾烟气蔓延的影响规律,使用火灾动力学模拟软件FDS,对不同火源位置的横通道临界风速、主隧道温度分布以及烟气层高度进行研究。研究结果表明：在一定火源功率范围内,隧道横通道临界风速与火源功率的1/3次方成正比且火源距横通道越远,临界风速越小;当火源位于交叉口,横通道使用临界风速通风时,隧道内烟气温度明显降低,烟气迅速沉降到2 m以下;当火源距离交叉口10,20 m,横通道通风会加快火源下游烟气沉降,烟气沉降速度随横通道通风速率的增大而增大;当火源位于交叉口时,烟气沉降由横通道通风对烟气的降温作用和涡旋作用共同主导,当火源位于距离交叉口10,20 m时,烟气沉降主要由涡旋作用主导。