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李伟振,姜洋,饶曙,阴秀丽,蒋恩臣.玉米秸秆和木屑及木钠混配成型工艺参数优化[J].农业工程学报,2018,34(1):198-203.DOI:10.11975/j.issn.1002-6819.2018.01.27
玉米秸秆和木屑及木钠混配成型工艺参数优化
投稿时间:2017-07-24  修订日期:2017-11-29
中文关键词:  秸秆  优化  燃料  成型颗粒  松弛密度  木屑
基金项目:国家重点研发项目(2016YFE0203300);国家自然科学基金项目(51661145022);广东省科技计划项目(2017A010104009)
作者单位
李伟振 1.中国科学院广州能源研究所广州 510640; 2. 中国科学院可再生能源重点实验室广州 510640; 3. 广东省新能源和可再生能源研究开发与应用重点实验室广州 510640; 
姜洋 1.中国科学院广州能源研究所广州 510640; 2. 中国科学院可再生能源重点实验室广州 510640; 3. 广东省新能源和可再生能源研究开发与应用重点实验室广州 510640; 
饶曙 4. 华南农业大学材料与能源学院广州 510642; 
阴秀丽 1.中国科学院广州能源研究所广州 510640; 2. 中国科学院可再生能源重点实验室广州 510640; 3. 广东省新能源和可再生能源研究开发与应用重点实验室广州 510640; 
蒋恩臣 4. 华南农业大学材料与能源学院广州 510642; 
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中文摘要:为了研究玉米秸秆与木屑及木钠的混配成型特性,该文采用5因素的响应面中心组合设计,在WD-100KE型电子压力机上进行单颗粒压缩试验,研究了成型参数木屑质量分数(0~40)、木钠质量分数(2%~10%)、温度(40~160 ℃)、水分(4%~20%)、压力(1~9 kN)对成型指标松弛密度、比能耗、径向最大抗压力的影响。通过对试验数据的回归,建立了响应面模型,分析了参数间的交互作用,获得了较佳的成型参数,并进行了试验验证。结果表明:温度和水分、水分和压力对比能耗起显著交互作用;木钠质量分数和温度、温度和水分、水分和压力对径向最大抗压力起显著交互作用。较佳的参数为:木屑质量分数10.15%,木钠质量分数8%,温度95.83 ℃,水分15.83%,压力3 kN,验证试验参数修正为:木屑质量分数10%,木钠质量分数8%,温度96 ℃,水分16%,压力3 kN,响应指标结果为:松弛密度1 054 kg/m3、比能耗8.02 kJ/kg、径向最大抗压力685N,与预测值误差值在8%以内,可为相关生产提供理论参考。
Li Weizhen,Jiang Yang,Rao Shu,Yin Xiuli,Jiang Enchen.Parameter optimization of corn stover blended with sawdust and sodium lignosulphonate compression experiments[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(1):198-203.DOI:10.11975/j.issn.1002-6819.2018.01.27
Parameter optimization of corn stover blended with sawdust and sodium lignosulphonate compression experiments
Author NameAffiliation
Li Weizhen 1. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China; 2.CAS Key Laboratory of Renewable Energy, Guangzhou 510640, China; 3. Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China; 
Jiang Yang 1. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China; 2.CAS Key Laboratory of Renewable Energy, Guangzhou 510640, China; 3. Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China; 
Rao Shu 4. School of Materials and Energy, South China Agricultural University, Guangzhou 510642, China; 
Yin Xiuli 1. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China; 2.CAS Key Laboratory of Renewable Energy, Guangzhou 510640, China; 3. Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China; 
Jiang Enchen 4. School of Materials and Energy, South China Agricultural University, Guangzhou 510642, China; 
Key words:straw  optimization  fuels  pellet  relaxed density  sawdust
Abstract: Transforming to fuels is the reasonable way of utilization for large amounts of corn stover in China. Blended material compression is one of the main directions for the further study of biomass compression technology. Because sawdust and sodium lignosulphonate have higher lignin content and a variety of active groups, corn stover blended with sawdust and sodium lignosulphonate is used as raw material in order to improve the quality of fuel. In this paper the compression characteristics of corn stover blended with sawdust and sodium lignosulphonate are studied, using central composite design of response surface method. The single-pellet compression experiments are conducted on the WD-100 type electronic pressure machine. The effects of parameters including sawdust content (0-40%), sodium lignosulphonate content (2%-10%), temperature (40-160 ℃), moisture content (4%-20%), and pressure (1-9 kN) on relaxed density, specific energy consumption, and radial maximum stress are studied in detail. In the range of parameters, the variation ranges of 3 technical indicators respectively are 883-1 201 kg/m3, 5.25-23.18 kJ/kg, and 390-156 6 N. Through the regression analysis of experimental data, the response surface model is established. All the selected models of 3 technical indicators are adjusted quadratic models, and the models' adjusted R2 values respectively are 0.868, 0.879 9, and 0.934 3, and predicted R2 values respectively are 0.802 6, 0.783 5, and 0.895 6. Interaction analysis shows that temperature and moisture content, and moisture content and pressure have significant interaction effect on relaxed density, sodium lignosulphonate content and temperature, as well as radial maximum stress. After reaching a certain temperature, the increase of water content has little effect on the relaxed density. For example, when the temperature reaches 140 ℃ and the water content is more than 8%, if increasing the water content continually, the relaxed density has no obvious change and is about 1 150 kg/m3. When the water content reaches a certain level, the increase of pressure has little effect on the relaxed density. For example, when the water content reaches 14% and the pressure is greater than 6 kN, if increasing the pressure continually, the relaxed density has no obvious change and is about 1 100 kg/m3. When the temperature is less than 100 ℃, the effect of sodium lignosulphonate content on radial maximum stress is small. Otherwise, the sodium lignosulphonate content has great effect on radial maximum stress. The simultaneous increase of temperature and moisture can keep radial maximum stress constant. When the pressure keeps constant and water content increases, the radial maximum stress decreases. While water content keeps constant and pressure increases, the radial maximum stress reaches the maximum and then slightly decreases. According to the biomass fuel standards, the technical indicators are set as follows: Relaxed density is not less than 1000 kg/m3, and specific energy consumption reaches the minimum in the optimization process. The calculated optimal parameters are sawdust content of 10.15%, sodium lignosulphonate content of 8%, temperature of 95.83 ℃, moisture content of 15.83%, and pressure of 3 kN, and the prediction values of 3 technical indicators respectively are 1012 kg/m3, 7.81 kJ/kg, and 737.4 N. The verification values respectively are 1 054 kg/m3, 8.02 kJ/kg, and 685 N through validation experiment, and the prediction error is within 8%. This shows the regression model has good reference value to guide the production of biomass pellets.
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