Low-Temperature Pyrolysis–Catalysis Coupled System for Efficient Tetrachlorobenzene Removal: Condition Optimization and Decomposition Mechanism

2018 ◽  
Vol 32 (4) ◽  
pp. 5509-5517
Author(s):  
Pingping Liu ◽  
Xiaosheng Yuan ◽  
Huarui Ren ◽  
Yanke Yu ◽  
Ning Xu ◽  
...  
2009 ◽  
Author(s):  
Qirong Fu ◽  
Dimitris Argyropolous ◽  
Lucian Lucia ◽  
David Tilotta ◽  
Stan Lebow

2014 ◽  
Vol 29 (2) ◽  
pp. 137-142
Author(s):  
Jiao-Zhu YU ◽  
Lin LI ◽  
Xin JIN ◽  
Ling-Hua DING ◽  
Tong-Hua WANG

2018 ◽  
Author(s):  
Kanako Sekimoto ◽  
Abigail R. Koss ◽  
Jessica B. Gilman ◽  
Vanessa Selimovic ◽  
Matthew M. Coggon ◽  
...  

Abstract. Biomass burning is a large source of volatile organic compounds (VOCs) and many other trace species to the atmosphere, which can act as precursors to the formation of secondary pollutants such as ozone and fine particles. Measurements collected with a proton-transfer-reaction time-of-flight mass spectrometer during the FIREX 2016 laboratory intensive were analyzed with Positive Matrix Factorization (PMF), in order to understand the instantaneous variability in VOC emissions from biomass burning, and to simplify the description of these types of emissions. Despite the complexity and variability of emissions, we found that a solution including just two emission profiles, which are mass spectral representations of the relative abundances of emitted VOCs, explained on average 85 % of the VOC emissions across various fuels representative of the western US (including various coniferous and chaparral fuels). In addition, the profiles were remarkably similar across almost all of the fuel types tested. For example, the correlation coefficient r of each profile between Ponderosa pine (coniferous tree) and Manzanita (chaparral) is higher than 0.9. We identified the two VOC profiles as resulting from high-temperature and low-temperature pyrolysis processes known to form VOCs in biomass burning. High-temperature and low-temperature pyrolysis processes do not correspond exactly to the commonly used flaming and smoldering categories as described by modified combustion efficiency (MCE). The average atmospheric properties (e.g. OH reactivity, volatility, etc.) of the high- and low-temperature profiles are significantly different. We also found that the two VOC profiles can describe previously reported VOC data for laboratory and field burns. This indicates that the high- and low-temperature pyrolysis profiles could be widely useful to model VOC emissions from many types of biomass burning in the western US, with a few exceptions such as burns of duff and rotten wood.


1982 ◽  
Vol 36 (1) ◽  
pp. 52-57 ◽  
Author(s):  
L. S. Singer ◽  
I. C. Lewis

The applications of electron spin resonance (ESR) to carbonaceous materials are reviewed. The stable paramagnetic species observed in the products of low-temperature pyrolysis are odd-alternate neutral free radicals, whereas the unpaired spins of higher temperature carbons and graphites are primarily conduction electrons. The variety of ESR properties and phenomena requires special attention to techniques of measurement and interpretations of results. The relevance to the carbonization process of the free radicals observed by ESR is also discussed.


Author(s):  
Ming Liu ◽  
Rongtang Liu ◽  
Junjie Yan

Lignite, a kind of low rank coal, has the characteristics of high moisture, high volatile, high ash and low heat value. The low-temperature pyrolysis technology is potential to improve the utilization efficiency of lignite. Therefore, a lignite-based energy system integrated with pre-drying and low-temperature pyrolysis was proposed in this paper. To assess the influence of pre-drying process, theoretical models were developed based on thermodynamics, and a case analysis was then performed to get the quantitative effect of pre-drying on efficiency of energy utilization. Results show that pre-drying on PPPS theoretical model can significantly improve the utilization of lignite by 1.46%.Keywords: Lignite; Pre-drying; Low-temperature pyrolysis; Energy efficiency; Case analysis.   


2011 ◽  
Vol 21 (3) ◽  
pp. 401-405 ◽  
Author(s):  
Xiaomei Wei ◽  
Min Zhou ◽  
Zhang Chun ◽  
Lei Jiali ◽  
Song Liqiang

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