针对废旧磷酸铁锂(LiFePO_(4),LFP)电池回收,开发硫酸铁辅助氧化焙烧-水浸的优先提锂工艺,探究焙烧温度、物料比及焙烧时间对锂浸出率的影响,并结合X射线衍射(X-Ray Diffraction,XRD)、扫描电子显微镜(Scanning Electron Microscope,SEM...针对废旧磷酸铁锂(LiFePO_(4),LFP)电池回收,开发硫酸铁辅助氧化焙烧-水浸的优先提锂工艺,探究焙烧温度、物料比及焙烧时间对锂浸出率的影响,并结合X射线衍射(X-Ray Diffraction,XRD)、扫描电子显微镜(Scanning Electron Microscope,SEM)进行机理分析。结果表明,在焙烧温度为500℃、物料比为0.75、焙烧时间为5 h的最佳工艺条件下,锂浸出率为92.44%。表征分析显示,硫酸铁可通过焙烧反应将LFP中的锂转化为可水浸的Li_(2)SO_(4),但LFP氧化生成的Li_(3)Fe_(2)(PO_(4))_(3)会导致部分锂难以浸出。本研究为废旧LFP电池的高效资源化利用提供了理论与技术参考。展开更多
The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatl...The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.展开更多
文摘针对废旧磷酸铁锂(LiFePO_(4),LFP)电池回收,开发硫酸铁辅助氧化焙烧-水浸的优先提锂工艺,探究焙烧温度、物料比及焙烧时间对锂浸出率的影响,并结合X射线衍射(X-Ray Diffraction,XRD)、扫描电子显微镜(Scanning Electron Microscope,SEM)进行机理分析。结果表明,在焙烧温度为500℃、物料比为0.75、焙烧时间为5 h的最佳工艺条件下,锂浸出率为92.44%。表征分析显示,硫酸铁可通过焙烧反应将LFP中的锂转化为可水浸的Li_(2)SO_(4),但LFP氧化生成的Li_(3)Fe_(2)(PO_(4))_(3)会导致部分锂难以浸出。本研究为废旧LFP电池的高效资源化利用提供了理论与技术参考。
基金supported by the Japanese Government,Grants-in-Aid for Scientific Research 2014 to 2016 under Grant No.26330296
文摘The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.
