纤维素
热解
催化作用
聚乙烯
聚苯乙烯
聚烯烃
碳纤维
化学
有机化学
聚合物
生物量(生态学)
产品分销
碳氢化合物
化学工程
材料科学
复合材料
工程类
复合数
地质学
图层(电子)
海洋学
作者
Christina Dorado,Charles A. Mullen,Akwasi A. Boateng
标识
DOI:10.1016/j.apcatb.2014.07.006
摘要
Abstract Catalytic pyrolysis over HZSM-5 is an effective method for the conversion of biomass to aromatic hydrocarbons, albeit with low yield and short catalyst lifetimes. Addition of co-reactants rich in carbon and hydrogen can enhance yield and possibly increase catalyst lifetimes by reducing coke formation. Particularly, the catalytic co-pyrolysis of plastic and biomass has been shown to enhance conversion to aromatic hydrocarbons, and also offers a method for productive disposal of waste agricultural plastics. In an effort to determine the origin of the carbon (plastic or biomass) in the products from this catalytic co-pyrolysis, mixtures of uniformly labeled 13 C cellulose and non-labeled plastic including polyethylene terephthalate, polypropylene, high density polyethylene, low density polyethylene and polystyrene were subjected to catalytic fast pyrolysis (CFP) at 650 °C in the presence of HZSM-5. A micro pyrolyzer coupled with GC/MS (py-GC/MS) advised product distributions and mass spectral data was used to determine the distribution of biogenic carbon and plastic derived carbon in the products. The results demonstrate that aromatic hydrocarbon products formed from the CFP of mixtures of cellulose and plastic are composed mostly of molecules containing carbon of mixed origin. Data on the distribution of 13 C x 12 C y from the products followed in this study show that polyolefin mixtures with cellulose favor the formation of alkyl benzenes that incorporate carbon from both sources. Utilization of aromatic polymers (polystyrene or polyethylene terephthalate) is more selective for formation of naphthalenes with carbon derived from both products. The distribution of various 13 C x 12 C y products is used to suggest active mechanisms that result in the formation of the observed products.
科研通智能强力驱动
Strongly Powered by AbleSci AI