床温及SNCR脱硝对CFB锅炉NOx和N2O排放影响的试验研究

为了研究循环流化床（CFB）锅炉燃用无烟煤时床温及选择性非催化还原（SNCR）脱硝对于NO和N2O排放的影响，在1 MW CFB试验装置上开展了试验研究。结果表明:床温由880 ℃提高到970 ℃，NO排放质量浓度由119.5 mg/m3上升到226.0 mg/m3，N2O排放质量浓度由216.0 mg/m3降低到102.2 mg/m3；在氨氮摩尔比（NSR）为0~3.7之间，随着NSR的提高，脱硝效率从0上升到50.72%；进一步提高NSR到5.2，脱硝效率升至53.61%，增加较为缓慢；随着NSR从0提高到1.7，N2O排放质量浓度由84.3 mg/m3上升至118.3 mg/m3，增长较为缓慢；进一步提高NSR至2.0，N2O排放质量浓度上升至187.7 mg/m3，增长速度提高；继续提高NSR至5.2，N2O排放质量浓度上升至381.4 mg/m3；CFB锅炉采用以尿素为还原剂的SNCR脱硝工艺时，单纯通过加大NSR来提高脱硝效率不仅效果有限，过量喷入的还原剂会造成N2O排放量的显著提高。     

干巴菌多糖提取工艺优化及抗氧化活性的研究

以产于云南曲靖的干巴菌为原料,以多糖提取率为评价指标,采用单因素实验及正交实验优化干巴菌多糖的提取工艺,通过测定干巴菌多糖对DPPH自由基及羟基自由基的清除率来评价其抗氧化活性。结果表明,干巴菌多糖的最佳提取工艺为:提取温度70℃、提取时间2.5 h、料液比1∶15(g∶mL),在此条件下,多糖提取率达到10.08%;当干巴菌多糖浓度为5 mg·mL~(-1)时,其对DPPH自由基的清除率为86.99%,对羟基自由基的清除率为87.46%,有较好的抗氧化作用。     

新型钢丝圈清洁器的使用效果

为了解决钢丝圈挂花问题,阐述了LH001新型钢丝圈清洁器的特点和应用效果;通过试验说明使用新型钢丝圈清洁器断头率低,基本无挂花,纺纱效果好。指出:新型钢丝圈清洁器是普通钢丝圈清洁器的更新换代产品,值得推广。     

一种基于FPGA的可变速率帧结构设计与实现

在简述V.35接口的基础上针对V.35接口速率可变的应用需求提出了一种速率可变的帧结构,该帧结构可支持N×64kb/s(3≤N≤32)速率,从而在V.35接口上实现了多种速率的低速业务传输.     

前混合磨料射流锚杆拆除装置设计

介绍锚杆应用及常用的拆锚方法,针对现有前混合磨料射流退锚装置存在锚杆拆卸时间长、工人作业环境差、装置部件锈蚀维护性差等问题,提出了一种前混合磨料射流退锚改进装置,并进行现场试验,结果表明,所改进设计装置具有退锚时间短、对工人伤害小、操作简便、部件维护周期长和无需专门配备泵站等特点,对进一步提高锚杆拆除效率具有重要参考价值。     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.