电石渣浆处理与资源化

针对电石渣浆处理的工艺流程和资源化途径进行了效益分析,工程实践表明:电石渣浆处理后,浆水回用作为生产用水,降低了乙炔生产成本.同时彻底解决电石渣含水率高的问题,利于企业开展资源化,值得在乙炔行业推广.     

大力发展循环经济促进宁阳县可持续发展

宁阳县发展循环经济过程中所取得的初步成果,同时对当前存在的突出问题,提出了相应的对策和改进措施:制定促进循环经济发展的相关政策;构筑循环经济基本模式,推动建立生态工业园;建立循环经济发展的技术支撑体系;制定发展循环经济的长远规划;提高全民发展循环经济意识和公众参与水平.     

Information Network Topologies for Enhanced Local Adaptive Management

We examined the principal effects of different information network topologies for local adaptive management of natural resources. We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states: one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers lose their capacity to reorganize back to the desired state. An erratum to this article can be found at .     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Dynamic Bayesian networks for reliability evaluation of subsea wellhead connector during service life based on Monte Carlo method

The subsea wellhead connector is a critical connection component between subsea Christmas tree and subsea wellhead for preventing the leakage of oil and gas in the subsea production system. Excited by cyclical loadings due to environmental forces and the other support forces, the subsea wellhead connector is prone to the failure, which could lead to the loss of subsea tree or wellhead integrity and even catastrophic accidents. With the Monte Carlo simulation method, this paper presents a reliability analysis approach based on dynamic Bayesian Networks, aiming to assess the failure probability of the subsea wellhead connector during service life. Take the driving ring component of the subsea wellhead connector as an example to demonstrate the reasonability of the proposed model. The generation data is processed by the transform between the numerical value and the state variable. Based on the stress-strength interference theory, the structure reliability of the driving ring with 96.26% is achieved by the proposed model with the consideration the aging of the material strength and the most influential factors are figured out. Meanwhile, the corresponding control measures are proposed effectively reduce the failure risk of the subsea wellhead connector during service life.     

Performance evaluation of reverse logistics enterprise – an agent-based simulation approach

Reverse logistics (RL) has been applied in many industries and sectors since its conception. Unlike forward logistics, retracing consumer goods from the point of consumption to the point of inception is not a well-studied process. It involves many uncertainties such as time, quality and quantity of returns. The returned products can be remanufactured, have parts harvested, or be disposed safely. It is important to implement these activities in a cost-effective manner. The aim of this research is to measure the performance of the RL enterprise with the help of an agent-based simulation model. The major entities in the RL network are considered as Agents that can act independently. There are several different agents: collector agent, sorting-cum-reuse agent, remanufacturing agent, recycler agent, supplier agent and distributor agent. The individual performances of the agents are measured and recommendations are given to improve their performance, leading to the enhancement of the total performance of the RL enterprise. The approach is applied to a case study involving cell phone remanufacturing.     

Recycling-oriented characterization of plastic frames and printed circuit boards from mobile phones by electronic and chemical imaging

This study characterizes the composition of plastic frames and printed circuit boards from end-of-life mobile phones. This knowledge may help define an optimal processing strategy for using these items as potential raw materials. Correct handling of such a waste is essential for its further “sustainable” recovery, especially to maximize the extraction of base, rare and precious metals, minimizing the environmental impact of the entire process chain. A combination of electronic and chemical imaging techniques was thus examined, applied and critically evaluated in order to optimize the processing, through the identification and the topological assessment of the materials of interest and their quantitative distribution. To reach this goal, end-of-life mobile phone derived wastes have been systematically characterized adopting both “traditional” (e.g. scanning electronic microscopy combined with microanalysis and Raman spectroscopy) and innovative (e.g. hyperspectral imaging in short wave infrared field) techniques, with reference to frames and printed circuit boards. Results showed as the combination of both the approaches (i.e. traditional and classical) could dramatically improve recycling strategies set up, as well as final products recovery.     

Using a Bayesian network to clarify areas requiring research in a host–pathogen system

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease‐driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host–pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious‐disease systems.     

西安市建筑垃圾渣土资源再利用研究

建筑垃圾渣土作为"建筑副产品",已造成严重的城市环境问题。通过对西安市建筑垃圾渣土的现场调查及工程再利用,借鉴发达国家及国内省市建筑垃圾渣土的成功管理经验和资源再利用的成功范例,提出了西安建筑垃圾资源再利用的对策建议,对"十三五"期间减少已有建筑垃圾的存量并遏制增量和建设西安森林城市具有参考意义。     

构建中国快递行业包装废弃物产业链对环境影响重要性研究

互联网和电子商务的快速发展带动了快递行业的飞速发展,使社会对快递包装物的需求增加.庞大的快递废弃物给环境造成了严重的破坏.然而由于相关法律不健全、回收体系不完整、企业不积极以及回收渠道少等原因导致了中国的快递包装废弃物回收率非常低.构建快递包装废弃物回收产业链,并降低快递包装废弃物对环境造成的影响就成了社会经济发展的一个迫切需要.为此推广环保材料和环保产品以及提高消费者环保意识入手,构建快递包装废弃物回收产业链势在必行.