Agro-ecosystem modeling can aid in the optimization of biomass feedstock supply

Recent European Directives promoted the development of biofuels, requesting mandatory limits to their emissions ot greenhouse gases (GHG). Second-generation biofuels based on lignocellulosic biomass are prime candidates but their GHG emissions are variable and uncertain. Agro-ecosystem modeling can capture them and the performance of biofuel feedstocks.This study aimed at optimizing feedstock supply for a bioethanol unit in France, from agricultural residues, annual and perennial crops. Their productivity and environmental impacts were modelled on a regional scale using geo-referenced data on soil properties, crop management, land-use and future weather data. Several supply scenarios were tested. Cereal straw was the most efficient feedstock but had a low availability, and only miscanthus could meet the bioethanol plant's demand. Sorghum combined poor yields and high GHG emissions compared by miscanthus and triticale. A mix of three biomass sources used less than 3% of the regional agricultural land while abating GHG emissions by 60%.     

Individual-based modeling of soil organic matter in NetLogo: Transparent,user-friendly,and open

Soil organic matter dynamics are essential for terrestrial ecosystem functions as they affect biogeochemical cycles and, thus, the provision of plant nutrients or the release of greenhouse gases to the atmosphere. Most of the involved processes are driven by microorganisms. To investigate and understand these processes, individual-based models allow analyzing complex microbial systems' behavior based on rules and conditions for individual entities within these systems, taking into account local interactions and individual variations. Here, we present a streamlined, user-friendly and open version of the individual-based model INDISIM-SOM, which describes the mineralization of soil carbon and nitrogen. It was implemented in NetLogo, a widely used and easily accessible software platform especially designed for individual-based simulation models. Including powerful means to observe the model behavior and a standardized documentation, this increases INDISIM-SOM's range of potential uses and users, and facilitates the exchange among soil scientists as well as between different modeling approaches.     

大型抽水蓄能电站首机首次启动试验方式选择研究——水泵工况启动试验方式关注的主要技术问题

对大型抽水蓄能电站首机首次水泵工况启动试验方式关注的主要技术问题,重点研究了9个技术难点和特点问题,提出分析意见和风险性评估。     

成都市PM2.5、PM10变化特征及其与气象因素的关系

为研究气象因素对成都市大气细颗粒物 (PM2.5)、可吸入颗粒物 (PM10) 的影响, 收集了2015―2018 年成都市 PM2.5、PM10的月平均浓度, 采用Pearson 相关分析法, 分析了成都市PM2.5、PM10与气象条件的关系。结果表明： (1) 2015 ―2018 年, 成都市PM2.5、PM10年平均浓度虽然年际间差别较小, 但整体呈现逐年缓慢下降趋势, 2015 年以来成都市的 一系列大气污染控制措施是PM2.5、PM10逐年缓慢下降的原因; 2015―2018 年成都市PM2.5、PM10浓度季节变化特征整体 表现为冬季 > 春季 > 秋季> 夏季。(2) 不同气象因素对成都市PM2.5、PM10月平均浓度的影响程度不同, 降水量与气温 是影响成都市PM2.5、PM10月平均浓度的主要因素, 两者与PM2.5、PM10呈较高的负线性相关, 其中PM2.5、PM10与降水量 的相关系数均为 −0.612, 与月平均气温的相关系数分别为 −0.822、−0.776, 降水会通过捕获大气中的颗粒物来去除 PM2.5、PM10, 而温度的升高会加强PM2.5、PM10等污染物在垂直方向上的对流运动, 从而对成都市污染物浓度的降低起 到重要作用; 日照时数、月平均风速、相对湿度等与PM2.5、PM10月平均浓度整体也呈现负相关, 但与降水量和气温相 比, 日照时数、月平均风速与PM2.5、PM10月平均浓度的相关性较低, 而相对湿度与PM2.5、PM10月平均浓度的相关性则 更加微弱, 表明相对湿度的变化对成都市PM2.5、PM10的积累和扩散影响很小。     

基于G1-CRITIC-云物元法的风电集群有功控制策略综合评价

为全面衡量风电集群有功控制效果、合理筛选控制方法使其综合性能最优,提出基于G1-CRITIC-云物元法的风电集群有功控制策略综合评价方法。首先,从安全稳定性、经济性、利用水平3个方面构建评价指标体系;其次,基于G1-CRITIC法对指标进行权重分配;最后,引入云物元理论实现评价等级边界的模糊化并计算控制策略的综合等级隶属度,有效提升评价结果的合理性与准确性。案例分析验证了所提方法的有效性。     

Energy coupled to matter for magnetic alignment of rare earth–doped alumina

Energy coupled to matter (ECM) concepts such as magnetic field–assisted processing were used to align rare earth–doped alumina ceramics in the presence of applied fields. The addition of gadolinium and ytterbium dopants to alumina increased the magnetic susceptibility anisotropies, and induced magnetic torques that led to significant alignment of ceramic particles under the application of magnetic fields as low as 1.8?T. In comparison, undoped alumina materials showed minimal alignment under applied field strengths as high as 9?T. Density function theory modeling indicated that the specific dopant type dictated changes in the magnetic properties of different rare earth–doped alumina systems by directly affecting the magnetic moment localization and magnetocrystalline anisotropy.     

High‐Temperature Particulate Matter Filtration with Resilient Yttria‐Stabilized ZrO2 Nanofiber Sponge

Particulate matter (PM) is a major air pollutant in many regions, jeopardizing ecosystems and public health. Filtration at pollutant source is one of the most important ways to protect the environment, however, considering the high‐temperature exhaust gas emissions, effective removal of PM and related pollutants from their sources remains a major challenge. In this study, a resilient, heat‐resisting, and high‐efficiency PM filter based on yttria‐stabilized ZrO₂ (YSZ) nanofiber sponge produced with a scalable solution blow spinning process is reported. The porous 3D sponge composed of YSZ nanofibers is lightweight (density of 20 mg cm^?3) and resilient at both room temperature and high temperatures. At room‐temperature conditions, the YSZ nanofiber sponge exhibits 99.4% filtration efficiency for aerosol particles with size in the range of 20–600 nm, associated with a low pressure drop of only 57 Pa under an airflow velocity of 4.8 cm s^?1. At a high temperature of 750 °C, the ceramic sponge maintains a high filtration efficiency of 99.97% for PM_0.3–2.5 under a high airflow velocity of 10 cm s^?1. A practical vehicle exhaust filter to capture particles with filtration efficiency of >98.3% is also assembled. Hence, the YSZ nanofiber sponge has enormous potential to be applied in industry.