Stability analysis of gas solids separation in scaling-up fluidized bed reactors

In large industrial fluidized bed reactors with high gas solids flow rates, small cyclones working in parallel are often preferred to achieve higher efficiency in the case of uniform distribution of gas-solid two-phase flow across each inlet. However, there is mounting evidence1-5 that gas-solid suspensions pass through identical paths in parallel can be significantly non-uniform, resulting in a dramatically drop in overall efficiency. In this study we used the direct Liapunov method by considering the interaction between gas and solids to detect the instability of uniformity. Owing to the special symmetry in this system, the criterion can be simplified into identifying the concavity (concave or convex) of pressure drop across a single cyclone with respect to operational parameter CT. Then, based on the stability analysis of uniformity, a novel design principle is provided to prevent non-uniform distribution at high dust loading. The effect of geometrical factor, i.e. dimensionless vortex finder diameter dr, on the stability of uniformity has been further investigated. The phase diagram of stability is calculated to give a clue of designing robust parallel cyclones system. Please click Additional Files below to see the full abstract

Putting Structure into Fluidized Bed – From Concept to Industrial Applications

Structures of particles, particle agglomerates, distributors, and internals have significantly influence on hydrodynamics and transfer behaviors of the dense gas-solid fluidized bed. For nanomaterial production, the particle surface and their agglomerated structures directly influence the fluidization behaviors; while for coal to chemical process, the distributors, internals play an important role in regime transient, and hydrodynamics. Carbon nanotubes mass production, coal to chemicals process. and fuel production were employed as examples to describe the concept of putting structures into fluidized bed, and then to put these structures into industrial applications

Enhancement of bioconversion of coal to methane by graphene

The research of enhancing biomethanation of coal has been paid much attention, which is an effective measure for increasing coalbed methane production. Adding conductive material to the digestive system can effectively accelerate direct interspecific electron transfer and increase methane production, which has great potential in enhancing the anaerobic digestion of organic matter. In this study, long-flame coal was used as the substrate to construct an anaerobic digestion system. The effect of the addition of graphene on biomethane production was discussed from the aspects of cumulative methane yield, the changes of key intermediates in the liquid phase, the microbial community structure, the methane metabolic pathway, and the changes of surface functional groups in residual coal after anaerobic digestion. The results showed that adding 0.4 g/L of graphene to the anaerobic digestion system based on coal effectively enhanced the entire anaerobic digestion process, not only enhanced methane production, but also brought forward the peak of methane production. At the early stage of digestion, the activities of hydrolytic bacteria (Paraclostridium) and hydrogen-production and aceogenic microflora (Alcaligenes and Sphaerochaeta) were enhanced, and sufficient nutrients were accumulated in the early stage. At the peak of methane production, the abundance of Methanoculleus decreased while the abundance of Methanosarcina significantly increased after the addition of graphene. The β subunit and γδ subunit of acetyl-coa decarbonyase/synthase, as key enzymes in the acetic acid synthesis pathway, increased by 233.54% and 3.32%, respectively. This significantly increased the abundance of Methanosarcina and mainly produced methane in the form of acetic acid nutrition. The abundance of Geobacter and Anaerovorax bacteria that can use ethyl acetate increased, and the Geobacter with high abundance were likely to DIET with Methanosarcina by bioelectric connection assisted by graphene. This electron transport mode accelerated the formation of biomethane to some extent. The carbonyl carbon (C=O) and carboxyl carbon (COO—) on the surface of residual coal decreased by 42.8% and 49.5%, respectively, after the addition of graphene, indicating that graphene effectively promoted the degradation of coal by microflora. The addition of graphene improves the activity and degradation efficiency of microflora, speeds up the process of anaerobic digestion, provides abundant substrate for methanogenic microflora, and improves methane production

novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model

To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/

The epidemiology of Plasmodium vivax and Plasmodium falciparum malaria in China, 2004–2012: from intensified control to elimination

BACKGROUND In China, the national malaria elimination programme has been operating since 2010. This study aimed to explore the epidemiological changes in patterns of malaria in China from intensified control to elimination stages. METHODS Data on nationwide malaria cases from 2004 to 2012 were extracted from the Chinese national malaria surveillance system. The secular trend, gender and age features, seasonality, and spatial distribution by Plasmodium species were analysed. RESULTS In total, 238,443 malaria cases were reported, and the proportion of Plasmodium falciparum increased drastically from <10% before 2010 to 55.2% in 2012. From 2004 to 2006, malaria showed a significantly increasing trend and with the highest incidence peak in 2006 (4.6/100,000), while from 2007 onwards, malaria decreased sharply to only 0.18/100,000 in 2012. Males and young age groups became the predominantly affected population. The areas affected by Plasmodium vivax malaria shrunk, while areas affected by P. falciparum malaria expanded from 294 counties in 2004 to 600 counties in 2012. CONCLUSIONS This study demonstrated that malaria has decreased dramatically in the last five years, especially since the Chinese government launched a malaria elimination programme in 2010, and areas with reported falciparum malaria cases have expanded over recent years. These findings suggest that elimination efforts should be improved to meet these changes, so as to achieve the nationwide malaria elimination goal in China in 2020.This study was supported by grants from the Ministry of Science and Technology of China (2012ZX10004-201, 2012ZX10004-220) and the Ministry of Health of China (No. 201202006), and China UK Global Health Support Programme (grant no. GHSP-CS-OP1). S.I.H. is funded by a Senior Research Fellowship from the Wellcome Trust (#095066). S.I.H. also acknowledges funding support from the RAPIDD programme of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health

Identification of membrane protein types via deep residual hypergraph neural network

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network