中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Preparation for industrial pellet production from blends of eucalyptus sawdust and hydrolysis lignin: the optimal variable combinations of co-pelletization

文献类型:期刊论文

作者Li, Weizhen2,3,4,5; Bu, Wenjing1; Guo, Weiwei2; Jiang, Yang3,4,5; Wang, An2; Yin, Xiuli3,4,5
刊名BIOMASS CONVERSION AND BIOREFINERY
出版日期2020-06-01
卷号10期号:2页码:513-521
关键词Biomass pellet fuel Eucalyptus sawdust Hydrolysis lignin Response surface methodology Relaxed density Optimal variable combination
ISSN号2190-6815
DOI10.1007/s13399-019-00444-y
通讯作者Li, Weizhen(liwz@ms.giec.ac.cn)
英文摘要The technology of co-pelletization was adopted to improve the performance of eucalyptus sawdust pellets, and hydrolysis lignin was treated as an effective binder. The main objective of this work was to obtain the optimal variable combinations of co-pelletization for preparation of industrial production. Response surface methodology using a central composite design with five-factor and five-level was employed to design and obtain the optimal variable combinations. Single pellet-making experiments were carried out using a uniaxial piston-cylinder densification apparatus. The effect of variables on responses was analyzed in detail. The selected optimal models for responses are all modified quadratic expressed in the form of regression equations. By setting the desired values of the responses, the optimized variable combinations are 26% hydrolysis lignin adding amount, 11% moisture content, 107-degrees C temperature, 5500-N pressure, and 3-mm particle size. The validation experiments were performed using the same apparatus according to the optimal variable combinations, and the response values are 24.83 kJ kg(-1) for specific energy consumption, 1048.91 kg m(-3) for relaxed density, and 31.44 N mm(-2) for Meyer hardness. The relative percentage errors between the response predicted values and validation experiment results are all less than 10%.
WOS关键词MOISTURE-CONTENT ; PINE SAWDUST ; MECHANICAL-PROPERTIES ; CHLORELLA-VULGARIS ; BIOMASS ; DURABILITY ; PYROLYSIS ; STRENGTH
资助项目National Natural Science Foundation of China[51661145022] ; Guangdong Provincial Natural Science Foundation Project[2017B030308002] ; Chinese Academy of Sciences Key Laboratory of Renewable Energy[y807j91001] ; Heilongjiang Science and Technology Planning Project[KS2018KH0090]
WOS研究方向Energy & Fuels ; Engineering
语种英语
WOS记录号WOS:000538051200025
出版者SPRINGER HEIDELBERG
资助机构National Natural Science Foundation of China ; Guangdong Provincial Natural Science Foundation Project ; Chinese Academy of Sciences Key Laboratory of Renewable Energy ; Heilongjiang Science and Technology Planning Project
源URL[http://ir.giec.ac.cn/handle/344007/27293]  
专题中国科学院广州能源研究所
通讯作者Li, Weizhen
作者单位1.Henan Univ Anim Husb & Econ, Sch Energy & Power Engn, 16 Beilin Rd, Zhengzhou 450001, Henan, Peoples R China
2.Zhengzhou Univ, Sch Chem Engn & Energy, 100 Sci Ave, Zhengzhou 450001, Henan, Peoples R China
3.Guangdong Prov Key Lab New & Renewable Energy Res, 2 Nengyuan Rd, Guangzhou, Peoples R China
4.Chinese Acad Sci, Key Lab Renewable Energy, 2 Nengyuan Rd, Guangzhou 510640, Peoples R China
5.Chinese Acad Sci, Guangzhou Inst Energy Convers, 2 Nengyuan Rd, Guangzhou 510640, Peoples R China
推荐引用方式
GB/T 7714
Li, Weizhen,Bu, Wenjing,Guo, Weiwei,et al. Preparation for industrial pellet production from blends of eucalyptus sawdust and hydrolysis lignin: the optimal variable combinations of co-pelletization[J]. BIOMASS CONVERSION AND BIOREFINERY,2020,10(2):513-521.
APA Li, Weizhen,Bu, Wenjing,Guo, Weiwei,Jiang, Yang,Wang, An,&Yin, Xiuli.(2020).Preparation for industrial pellet production from blends of eucalyptus sawdust and hydrolysis lignin: the optimal variable combinations of co-pelletization.BIOMASS CONVERSION AND BIOREFINERY,10(2),513-521.
MLA Li, Weizhen,et al."Preparation for industrial pellet production from blends of eucalyptus sawdust and hydrolysis lignin: the optimal variable combinations of co-pelletization".BIOMASS CONVERSION AND BIOREFINERY 10.2(2020):513-521.

入库方式: OAI收割

来源:广州能源研究所

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