Thermodynamic Analysis of Carbon Capture and Pumped Heat Electricity Storage

This work can be divided into two parts: the first part is focused on carbon capture; the second part is devoted to the study of pumped heat electricity storage processes. Thermodynamic analysis of energy requirement for adsorption and chemical looping processes is investigated. It enables us to compare various technology platforms under the same separation target. Sorption-enhanced reaction is a novel intensified process by combining catalyst and adsorbent in a single fixed bed reactor. Experimental studies of sorption-enhanced water gas shift and steam methane reforming have been done by previous members of our group. Here numerical studies on the interactions between reaction and sorption in a sorption-enhanced reactor are carried out. Water-gas shift reaction, hydrogen sulfide decomposition and propene metathesis reaction are studied. Our results suggest that the produce purity depend on factors such a reaction kinetics, stoichiometry, equilibrium and adsorption isotherm. Mass transfer resistance can also play an important role in product purity. Experimental studies on high temperature carbon dioxide capture by pressure swing adsorption using Na-promoted alumina are undertaken for the first time. The effects of steam during regeneration are discussed. Pumped heat electricity storage processes are a novel thermal energy storage technique recently proposed. It does not require specific geological structure sites and is environmentally friendly. When combined with renewable energy resources, e.g. solar, wind and tidal, it can supply stable power throughout the day. During the charging and delivery cycle a cyclic steady state temperature distribution is formed inside the storage tank. In order to reduce the computing time to simulate this process, a novel matrix exponential solution is provided. Dimensionless analysis on the process performance is discussed

Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels

Computer vision algorithms with pixel-wise labeling tasks, such as semantic segmentation and salient object detection, have gone through a significant accuracy increase with the incorporation of deep learning. Deep segmentation methods slightly modify and fine-tune pre-trained networks that have hundreds of millions of parameters. In this work, we question the need to have such memory demanding networks for the specific task of salient object segmentation. To this end, we propose a way to learn a memory-efficient network from scratch by training it only on salient object detection datasets. Our method encodes images to gridized superpixels that preserve both the object boundaries and the connectivity rules of regular pixels. This representation allows us to use convolutional neural networks that operate on regular grids. By using these encoded images, we train a memory-efficient network using only 0.048\% of the number of parameters that other deep salient object detection networks have. Our method shows comparable accuracy with the state-of-the-art deep salient object detection methods and provides a faster and a much more memory-efficient alternative to them. Due to its easy deployment, such a network is preferable for applications in memory limited devices such as mobile phones and IoT devices.Comment: 6 pages, submitted to MMSP 201

Double carbon decorated lithium titanate as anode material with high rate performance for lithium-ion batteries

AbstractSpinel lithium titanate (Li4Ti5O12) has the advantages of structural stability, however it suffers the disadvantages of low lithium-ion diffusion coefficient as well as low conductivity. In order to solve issues, we reported a simple method to prepare carbon-coated Li4Ti5O12/CNTs (C@Li4Ti5O12/CNTs) using stearic acid as surfactant and carbon source to prepare carbon coated nanosized particles. The obtained Li4Ti5O12 particles of 100nm in size are coated with the carbon layers pyrolyzed from stearic acid and dispersed in CNTs matrix homogeneously. These results show that the synthesized C@Li4Ti5O12/CNTs material used as anode materials for lithium ion batteries, presenting a better high-rate performance (147mAhg−1 at 20C). The key factors affecting the high-rate properties of the C@Li4Ti5O12/CNTs composite may be related to the synergistic effects of the CNTs matrix and the carbon- coating layers with conductivity enhancement. Additionally, the amorphous carbon coating is an effective route to ameliorate the rate capability of Li4Ti5O12/CNTs

Numerical study of instability of nanofluids: the coagulation effect and sedimentation effect

This study is a numerical study on the coagulation as well as the sedimentation effect of nanofluids using the Brownian dynamics method. Three cases are simulated, focusing on the effects of the sizes, volume fraction, and ζ potentials of nano-particles on the formation of coagulation and sedimentation of nanofluids. The rms fluctuation of the particle number concentration, as well as the flatness factor of it, is employed to study the formation and variation of the coagulation process. The results indicate a superposition of coagulation and sedimentation effect of small nano-particles. Moreover, it is stable of nanofluids with the volume fraction of particles below the limit of "resolution" of the fluids. In addition, the effect of ζ potentials is against the formation of coagulation and positive to the stability of nanofluids