Modeling of CH4-assisted SOEC for H2O/CO2 co-electrolysis

This research was supported by a grant of SFC/RGC Joint Research Scheme (X-PolyU/501/14) from Research Grant Council, University Grants Committee, Hong Kong SAR.Co-electrolysis of H2O and CO2 in a solid oxide electrolysis cell (SOEC) is promising for simultaneous energy storage and CO2 utilization. Fuel-assisted H2O electrolysis by SOEC (SOFEC) has been demonstrated to be effective in reducing power consumption. In this paper, the effects of fuel (i.e. CH4) assisting on CO2/H2O co-electrolysis are numerically studied using a 2D model. The model is validated with the experimental data for CO2/H2O co-electrolysis. One important finding is that the CH4 assisting is effective in lowering the equilibrium potential of SOEC thus greatly reduces the electrical power consumption for H2O/CO2 co-electrolysis. The performance of CH4-assisted SOFEC increases substantially with increasing temperature, due to increased reaction kinetics of electrochemical reactions and CH4 reforming reaction. The CH4-assisted SOFEC can generate electrical power and syngas simultaneously at a low current density of less than 600 Am−2 and at 1123 K. In addition, different from conventional SOEC whose performance weakly depends on the anode gas flow rate, the CH4-assisted SOFEC performance is sensitive to the anode gas flow rate (i.g. peak current density is achieved at an anode flow rate of 70 SCCM at 1073 K). The model can be used for subsequent design optimization of SOFEC to achieve high performance energy storage.PostprintPeer reviewe

Modeling of a combined CH₄-assisted solid oxide co-electrolysis and Fischer-Tropsch synthesis system for low-carbon fuel production

10th International Conference on Applied Energy, ICAE 2018, Hong Kong, 22-25 August 2018201906 bcmaVersion of RecordPublishe

Pore-in-Pore Engineering in a Covalent Organic Framework Membrane for Gas Separation

Covalent organic framework (COF) membranes have emerged as a promising candidate for energy-efficient separations, but the angstrom-precision control of the channel size in the subnanometer region remains a challenge that has so far restricted their potential for gas separation. Herein, we report an ultramicropore-in-nanopore concept of engineering matreshka-like pore-channels inside a COF membrane. In this concept, α-cyclodextrin (α-CD) is in situ encapsulated during the interfacial polymerization which presumably results in a linear assembly (LA) of α-CDs in the 1D nanochannels of COF. The LA-α-CD-in-TpPa-1 membrane shows a high H2 permeance (∼3000 GPU) together with an enhanced selectivity (>30) of H2 over CO2 and CH4 due to the formation of fast and selective H2-transport pathways. The overall performance for H2/CO2 and H2/CH4 separation transcends the Robeson upper bounds and ranks among the most powerful H2-selective membranes. The versatility of this strategy is demonstrated by synthesizing different types of LA-α-CD-in-COF membranes

Modelling of a hybrid system for on-site power generation from solar fuels

201906 bcmaVersion of RecordPublishe

Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning

Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-preserved regions. In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Firstly, we employ the gamma correction iteratively to simulate artificial multiple shots under different exposure conditions, whose haze degrees are different and enrich the underlying scene prior. Secondly, beyond utilizing the local neighboring relationship, we build a bidimensional graph reasoning module to conduct non-local filtering in the spatial and channel dimensions of feature maps, which models their long-range dependency and propagates the natural scene prior between the well-preserved nodes and the nodes contaminated by haze. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks

Holistic resource allocation for multicore real-time systems

This paper presents CaM, a holistic cache and memory bandwidth resource allocation strategy for multicore real-time systems. CaM is designed for partitioned scheduling, where tasks are mapped onto cores, and the shared cache and memory bandwidth resources are partitioned among cores to reduce resource interferences due to concurrent accesses. Based on our extension of LITMUSRT with Intel’s Cache Allocation Technology and MemGuard, we present an experimental evaluation of the relationship between the allocation of cache and memory bandwidth resources and a task’s WCET. Our resource allocation strategy exploits this relationship to map tasks onto cores, and to compute the resource allocation for each core. By grouping tasks with similar characteristics (in terms of resource demands) to the same core, it enables tasks on each core to fully utilize the assigned resources. In addition, based on the tasks’ execution time behaviors with respect to their assigned resources, we can determine a desirable allocation that maximizes schedulability under resource constraints. Extensive evaluations using real-world benchmarks show that CaM offers near optimal schedulability performance while being highly efficient, and that it substantially outperforms existing solutions