The influence of the level of definition of functional specifications on the environmental performances of a complex system. EcoCSP approach

The tendency towards a homogenous mode of development modelled on that of Western countries means that sustainable development has become increasingly urgent. It is necessary to thoroughly redefine products and their expected performances in such a way that the consequences are compatible with sustainable development. In the domain of product design, this means that it is no longer sufficient to use assessment tools “after the fact” to check the impact of products whose functional unit (FU) was defined prior to production; it is now necessary to rethink the definition of the FU itself. This article aims to present an approach based on a combination of life cycle analysis methods and problem-solving by constraint satisfaction. This original approach makes it possible to vary the design of the different dimensions of the FUs of a complex system and thus to make it easier to identify the best architecture along with the best functional definition of the system. In this study, the EcoCSP approach is applied to define the functional performances of an ecological passenger ferry. The complexity of couplings between subsystems and the sheer number of those subsystems mean that the designer has to use “intelligent” tools. These simulate a great number of scenarios and help him/her to fine-tune the system and make the right technological choices with regard to the right functional specifications.     

重庆缙云山四川大头茶常绿阔叶林重金属元素的累积与生物循环

四川大头茶,常绿阔叶林,重金属,循环,净化     

亚磷酸盐降解微生物的筛选、鉴定和降解特性

从太湖底泥中筛选出一株能够利用亚磷酸盐(+3价)的细菌P1.通过生理生化实验及16S rDNA基因序列分析鉴定,P1菌与所有已知菌的同源性都很低,属于未知的新菌株.P1菌的最适培养条件为:pH6.5~7.0、温度30℃.P1菌能以亚磷酸盐为唯一磷源生长,在60~100mg P/L的初始亚磷酸盐培养条件下,100mgP/L的亚磷酸盐培养基中亚磷酸盐减少量最大(11%),培养基中生成正磷酸盐的比例最高1.6%.初始亚磷酸盐浓度越低,碱性磷酸酶(BAP)活性越高,60mgP/L的亚磷酸盐培养基中BAP的最高活性为1.86mol PNP/(L菌液·h).P1菌可能通过BAP将亚磷酸盐转化成生物体内所需的磷源.     

低浓度石油类测定过程中的问题探讨

分析了影响低浓度石油类测定的一些因素,通过合理的方法克服这些影响因素之后,测定结果才能准确可靠。试验测得1000ml水样直接萃取法测定石油类的方法检出限为0.01mg/L,证明用该方法可以定量测定石油类浓度在0.05mg/L附近的低浓度水样。     

我国硒淡水水生生物水质基准值推导

水体中硒含量过高会导致水生生态系统退化,而中国现有的地表水环境质量标准对硒的标准值设定并不是基于我国水生生物相关毒理学研究得出的,难以因地制宜地保护我国水生生物.为保护我国水生生物,本研究利用物种敏感度分布法,推导出基于最大无效应浓度（NOEC）、最低有效应浓度（LOEC）的慢性硒（无机）淡水水质一级基准值和基于半数致死效应浓度（LC₅₀）、半数最大效应浓度（EC₅₀）、半数抑制浓度（IC₅₀）的急性硒（无机）淡水水质二级基准值分别为0.58 μg·L^-1、0.52 mg·L^-1,发现我国现有的水质标准可能会对我国水生生物造成欠保护.进一步推导出为保护我国鱼类的硒（无机）淡水水质一级、二级基准值分别为0.21 μg·L^-1、1.60 mg·L^-1,以及硒淡水鱼类饲料有机硒和无机硒含量一级基准值分别为97 μg·kg^-1和98 μg·kg^-1.本研究基于硒对水生生物的急性和慢性毒性效应,推导出硒的系列基准值,为保护我国淡水水生生物安全的标准制定提供数据支撑和科学依据.     

Domestic or imported? An assessment of carbon footprints and sustainability of seafood consumed in Australia

The distance between where food is produced and consumed is increasing, and is often taken as evidence of an unsustainable global food system. Seafood is a highly traded commodity yet seafood sustainability assessments do not typically consider the impacts of the movement of products beyond the fishery or farm. Here we use life cycle assessment to examine the carbon footprint of the production and distribution of select seafood products that are consumed in Australia and determine differences in the sustainability of imports and their domestically produced counterparts. We found that the distance food is transported is not the main determinant of food sustainability. Despite the increased distance between production and consumption, carbon footprints of meals from imported seafood are similar to meals consisting of domestically produced seafood, and sometimes lower, depending on the seafood consumed. In combining LCA with existing seafood sustainability criteria the trade-offs between sustainability targets become more apparent. Carbon ‘footprinting’ is one metric that can be incorporated in assessments of sustainability, thereby demonstrating a broader perspective of the environmental cost of food production and consumption.     

Simulation-based fault propagation analysis—Application on hydrogen production plant

Recently production of hydrogen from water through the Cu–Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables’ interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS^®. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.     

基于模糊聚类关联分析法的煤与瓦斯突出程度分析

运用模糊聚类分析方法对煤与瓦斯突出的样本集合进行分类,建立了不同突出程度的模糊模式.用关联分析确定待分析样本与模式的关联程度,以此预测预报样本的煤与瓦斯突出危险程度.实例分析表明,与模糊聚类分类后、将模式与待预报样本组成新样本集合进行聚类分析并以此分类结果进行预报的方法相比,这种预报方法不仅可靠程度高,而且能定量描述待报样本与模式的亲和程度.     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.