锰钛系复合锂离子筛的制备及其吸附性能研究

以LiOH·H2O为锂源、锰钛共沉物作为锰源和钛源,采用共沉淀-水热合成法合成掺钛复合锂离子筛。研究了合成条件对复合锂离子筛的结构和性能的影响,使用X射线衍射(XRD)对合成样品进行表征,通过酸浸和吸附实验研究了合成样品吸附锂的性能和溶损率。研究结果表明:当Ti、Mn物质的量比为0.03,水热反应母液中锂浓度为3.2mol/L,反应温度为230℃,反应时间为12h时合成的离子筛具有较好的吸附性能和稳定性,对Li+的最大饱和吸附容量达到19.71mg·g-1,同时,Mn和Ti保持了较低的溶损率。     

增大蒸馏表面积对改善海绵钛蒸馏效率的影响研究

蒸馏工序是分离海绵钛和氯化镁的关键工序,蒸馏效率会影响产品罐中残留氯化镁以及过剩镁的去除,直接决定产品质量。另一方面蒸馏效率低会延长占炉周期,增加蒸馏电耗,影响生产效率。传统蒸馏工艺改善研究主要集中在控制蒸馏温度、延长蒸馏时间等方面,但对于提升蒸馏表面积的研究较少。通过还原前在反应器内插入钛棒增大海绵钛中部的蒸馏表面积,可在同等蒸馏物浓度和蒸馏温度的条件下达到提高海绵钛蒸馏效率的目的。研究表明,在保证氯化镁完全蒸馏的前提下,增大蒸馏表面积可明显提高海绵钛蒸馏效率,蒸馏时间由120 h降至105 h,降低蒸馏电耗,节约生产成本。     

Gravel coverage rate measurement in synchronous chip seal based on deep convolutional neural network

Synchronous chip seal is an advanced road constructing technology, and the gravel coverage rate is an important indicator of the construction quality. In this paper, a novel approach for gravel coverage rate measurement is proposed based on deep learning. Convolutional neural network (CNN) is used to segment the image of ground covered with gravels, and the gravel coverage rate is computed by the percentage of gravel pixels in the segmented image. The gravel coverage rate dataset for model training and testing is built. The performance of fully convolutional neural network (FCN) and U-Net model in the dataset is tested. A better model named GravelNet is constructed based on U-Net. The scaled exponential linear unit (SELU) is employed in the GravelNet to replace the popular combination of rectified linear unit (ReLU) and batch normalization (BN). Data augmentation and alpha dropout are performed to reduce overfitting. The experimental results demonstrate the effectiveness and accuracy of our proposed method. Our trained GravelNet achieves the mean gravel coverage rate error of 0.35% on test dataset.     

Traffic sign recognition based on deep convolutional neural network

Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named “dropout”. The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceed-ing the state-of-the-art results.     

MnO_2·0.5H_2O锂离子筛吸附剂制备条件对其性能的影响

采用正交试验法研究了锂离子筛MnO2·0.5H2O的制备条件对离子筛吸附容量和锰溶损率的影响,考察了水热温度、锂锰物质的量比、水热反应时间、煅烧温度、煅烧时间等对离子筛性能的影响。结果表明:这些因素对离子筛的吸附容量都有显著影响,但是对离子筛中锰溶损率的影响不显著;在水热温度230℃、锂锰物质的量比5∶1、水热时间13h、煅烧温度450℃、煅烧时间6h优化条件下制备锂离子筛并进行10次循环吸附、解吸试验,锂离子筛的第1次吸附容量为37.182mg/g,连续10次循环吸附-解吸后,锂吸附容量保持在36.11mg/g左右,锰溶损率很低。采用XRD对吸附-解吸前后的样品进行表征,离子筛均保持Li1.6Mn1.6O4尖晶石结构。     

面向新型电力系统的5G网络切片资源分配策略

随着以能源为主体的新型电力系统建设步伐的不断推进，分布式能源、源网荷储等新型电力业务应运而生，如何合理分配资源、适应多类业务差异化需求是目前电网公司关注的焦点。构建混合整数线性规划的5G网络切片模型，基于VIKOR方法对拓扑特性、资源利用率和延迟时间等切片节点因子的重要性进行排序，并在切片链路配置阶段，使用最短路径算法获得切片链路的物理路径。仿真结果表明，本文提出的方案和算法能够在保证业务端到端时延要求的同时，保证较高的切片配置成功率，满足5G网络切片的安全要求。     

增大蒸馏表面积对改善海绵钛蒸馏效率的影响研究

蒸馏工序是分离海绵钛和氯化镁的关键工序，蒸馏效率会影响产品罐中残留氯化镁以及过剩镁的去除，直接决定产品质量。另一方面蒸馏效率低会延长占炉周期，增加蒸馏电耗，影响生产效率。传统蒸馏工艺改善研究主要集中在控制蒸馏温度、延长蒸馏时间等方面，但对于提升蒸馏表面积的研究较少。通过还原前在反应器内插入钛棒增大海绵钛中部的蒸馏表面积，可在同等蒸馏物浓度和蒸馏温度的条件下达到提高海绵钛蒸馏效率的目的。研究表明，在保证氯化镁完全蒸馏的前提下，增大蒸馏表面积可明显提高海绵钛蒸馏效率，蒸馏时间由120 h降至105 h,降低蒸馏电耗，节约生产成本。