镧负载D201树脂同步脱硝除磷的性能对比

采用La3+、La（OH）₃以及La₂（CO₃）₃对D201树脂进行改性,并从吸附动力学、等温线、共存离子影响以及再生等方面系统地对比3种La负载树脂的同步脱硝除磷性能。扫描电镜及成分分析结果表明,D201表面能够负载不同形态的La,以La₂（CO₃）₃形态负载时La含量最高。吸附数据表明,La₂（CO₃）₃型D201树脂除磷效果优越,吸附容量高且受干扰离子影响小。经Na₂CO₃溶液再生5次后,各树脂均能保持良好的再生吸附效果,同步脱硝除磷性能稳定。树脂中季铵官能团（-R₄N+）对硝酸根选择性较高,使得硝氮吸附受共存离子的影响较弱;磷酸根吸附归因于与-R₄N+静电作用及与负载的各形态La形成沉淀、发生配体交换等,受体系pH值变化较为敏感,弱碱性条件可促进吸附。      

基于智能磨矿介质及CNN和优化SVM模型的球磨机负荷识别方法

当前球磨机负荷检测方法难以准确评估磨机内部变化,给磨机综合运行状态的控制和优化带来较大难度。本文设计了一款内嵌加速度传感器且与钢球介质物理性质相一致的智能磨矿介质用于识别磨机负荷,开展了不同充填率等磨矿条件下的磨矿试验,设计磨矿效果系数划分磨机负荷状态;分别采用了卷积神经网络方法(CNN)和优化的支持向量机(SVM)模型对智能磨矿介质获取的加速度信号进行球磨机负荷识别。基于优化的SVM模型将获取的一维加速度信号进行互补集合经验模态分解算法（CEEMD）去噪、时域特征值和样本熵提取等预处理,将上述磨机负荷的特征向量分别输入GA?SVM、GS?SVM、PSO?SVM分类模型进行训练,研究表明,PSO?SVM模型的识别准确率可达98.33%,但存在训练过程繁琐,耗费时间长的问题。在图像识别领域具有优秀应用能力的CNN模型是把智能磨矿介质检测加速度信号数据转换为二维图片后直接输入基于VGG19网络的CNN模型进行分类识别,磨机负荷分类识别准确率高于优化的SVM模型,可达98.89%,在保证识别准确率的同时有效节约了计算时间。基于CNN的智能磨矿介质球磨机负荷识别方法可为实现球磨机负荷检测与在线评估提供重要解决方案与技术保障。      

固定化芽孢对铽离子的吸附特性

微生物吸附法作为一种高效、廉价、环境友好的生物技术,已在稀土离子分离回收中逐渐被开发利用。实验通过固定化微生物技术,使用海藻酸钠包埋活性炭与枯草芽孢杆菌芽孢制备成固定化颗粒,处理含稀土离子的废水。对比了固定化芽孢加活性炭、固定化芽孢以及固定化活性炭对铽离子的吸附效果,并探究了不同条件对几种固定化吸附剂吸附铽离子的影响。结果表明:固定化芽孢加活性炭兼具包埋法与吸附法的优点,对铽离子的吸附效果更好; 在包炭量为1:100(活性炭质量:溶液总质量)、芽孢悬液浓度为OD₆₀₀(菌液在600 nm波长处的吸光值)=2、铽离子浓度为100 μmol/L、温度为15~25 ℃、pH=4.5~8.5的条件下吸附60 min,铽去除率可达90%以上。固定化芽孢加活性炭对铽离子的吸附更符合准一级动力学、Langmuir吸附等温线模型。      

电化学还原辅助废旧锂离子电池正极材料高效浸出

为了提高废旧锂离子电池正极材料中有价金属的浸出效率,采用电化学还原法实现对正极材料中有价金属的高效浸出。采用单因素试验探索硫酸浓度、浸出时间、温度和电流密度等因素对锂、镍、钴和锰浸出率的影响,借助扫描电镜、X射线衍射仪对电化学浸出前后正极片的表面形貌、元素分布及物相组成进行综合分析。结果表明：在外加电场的作用下实现了有价金属的还原,正极材料浸出后,主要剩余具有多孔结构的黏结剂PVDF;在硫酸浓度0.8 mol/L、浸出时间60 min、温度50 ℃、电流密度30 mA/cm2的条件下,锂、镍、钴和锰的浸出率可分别达到97.2%、95.68%、95.09%、94.61%。     

Ecosystem Service Impacts of Urban Water Supply and Demand Management

Utilities face the challenge of enhancing long-term water security while minimising undesirable economic, social and environmental impacts of supply and demand management options. This paper provides an example of how the ecosystem services concept can be used to enumerate and organise broad impacts of water supply options. A case study of Adelaide, South Australia, is used to examine costs and benefits associated with different sources of water and source-water mix scenarios. Ecosystem service impacts are estimated using estimates from the literature. Seven water supply and demand management options are considered for Adelaide: 1) the River Murray, 2) Mt. Lofty Ranges catchments, 3) wastewater reuse, 4) desalination, 5) stormwater harvesting, 6) groundwater and 7) water conservation. The largest costs are associated with sourcing water from conservation measures such as water restrictions on outdoor watering estimated at $1.87/kL. Salinity damage costs associated with residential uses are estimated at up to $1.54/kL. Salinity damage costs of wastewater reuse were estimated at $1.16/kL. The largest benefit is coastal amenity services associated with stormwater harvesting and treatment estimated at $1.03/kL. Results show that there is a trade-off between financial costs and ecosystem services impacts with source-water mix scenarios with the highest ecosystem services cost having the lowest financial O&M cost and vice versa. This highlights the importance of taking ecosystem services into account when evaluating water supply options.     

Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis

In this paper we introduce a multi-method multiple criteria approach for evaluating the performance of organizations. Performance analysis may include both strategic and operational performance, as well as financial and other less tangible factors. This paper introduces the use of Fuzzy C-Means and TOPSIS for organizational performance evaluation purposes. Using real company data and balanced scorecard accounting and performance dimensions the methodology is applied and evaluated. The predictive abilities of the technique from an organizational performance evaluation approach are evaluated using this data. One of the results from the illustrative application is that economic performance evaluation is not the best predictor of overall viability of some organizations, especially e-commerce based organizations.     

Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

Pressureless sintering and tribological properties of WC–ZrO2 composites

In a recent work [Basu, B., Lee, J. H. and Kim, D. Y., Development of WC-ZrO₂ nanocomposites by spark plasma sintering. J. Am. Ceram. Soc. 2004 87(2), 317–319], the processing of ultrahard WC–ZrO₂ nanocomposites using spark plasma sintering is reported. In the present work, we investigate the processing and properties of WC–6 wt.% ZrO₂ composites, densified by pressureless sintering route. The densification of the WC–ZrO₂ composites was performed in the temperature range of 1500–1700 °C with varying time (1–3 h) in vacuum. The experimental results indicate that significantly high hardness of 22–23 GPa and moderate fracture toughness of ∼5 MPa m^1/2 can be obtained with 2 mol% Y–stabilized ZrO₂ sinter-additive, sintered at 1600 °C for 3 h. Furthermore, the friction and wear behavior of optimized WC–ZrO₂ composite is investigated on a fretting mode I wear tester. The tribological results reveal that a moderate coefficient of friction in the range from 0.15 to 0.5 can be achieved with the optimised composite. A transition in friction and wear with load is noted. The dominant mechanisms of material removal are tribochemical wear and spalling of tribolayer.     

Research of semi-supervised spectral clustering algorithm based on pairwise constraints

Clustering is often considered as an unsupervised data analysis method, but making full use of the prior information in the process of clustering will significantly improve the performance of the clustering algorithm. Spectral clustering algorithm can well use the prior pairwise constraint information to cluster and has become a new hot spot of machine learning research in recent years. In this paper, we propose an effective clustering algorithm, called a semi-supervised spectral clustering algorithm based on pairwise constraints, in which the similarity matrix of data points is adjusted and optimized by pairwise constraints. The experiments on real-world data sets demonstrate the effectiveness of this algorithm.