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过程工程学报 ›› 2017, Vol. 17 ›› Issue (3): 600-604.DOI: 10.12034/j.issn.1009-606X.217114

• 过程与工艺 • 上一篇    下一篇

多元回归综合优化碳化硅超细粉球团变温干燥工艺

李 军, 许树栋*, 张红超,王 露, 李 朋   

  1. 中国矿业大学(北京)化学与环境工程学院,北京 100083
  • 收稿日期:2017-01-13 修回日期:2017-03-06 出版日期:2017-06-20 发布日期:2017-06-14
  • 通讯作者: 许树栋 870647213@qq.com

Multi-variable Regression Analysis Applied to Process Optimization of Variable Temperature Drying on SiC Ultra-fine Powder Pellets#br#  

Jun LI,  Shudong XU*,  Hongchao ZHANG,  Lu WANG,  Peng LI   

  1. School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
  • Received:2017-01-13 Revised:2017-03-06 Online:2017-06-20 Published:2017-06-14

摘要:

采用先高温后低温的变温干燥工艺降低碳化硅超细粉球团的干燥成本,以干燥时间和单位能耗为指标,以变温干燥的前期温度、前期风速、转换干基含水率、后期风速和后期温度为主要影响因素进行了L16(45)正交实验,研究了变温干燥工艺,并结合多元回归,根据多指标权重进行线性加权和优化. 结果表明,最佳优化变温干燥工艺参数为前温期度200℃、前期风速0.83 m/s、转换干基含水率5%、后期风速1.78 m/s、后期温度140,该条件下干燥时间为203 min,单位能耗为65 MJ/kg.

关键词: 正交设计, 多元回归, 碳化硅超细粉球团, 变温干燥, 工艺优化

Abstract:

To reduce drying cost of the SiC ultra-fine powder pellets, variable temperature drying technology which had higher temperature at first and lower temperature afterwards was put forward. The temperature and wind velocity in the early and later period, together with conversion moisture content, were the main influencing factors during this process. These factors were studied by the L16(45) orthogonal experiment which using drying time and unit energy consumption as drying indexes. Multi-variable regression model combined with the weight value of each indexes were also used to fit the design conditions. The optimal parameters of variable temperature drying process were as follow: the temperature in the early period was 200℃, the wind velocity in the initial stage was 0.83 m/s, the transforming dry basis moisture content was 5%, the wind speed in the later period was 1.78 m/s, and the temperature in the later period was 140℃. Under these conditions, the drying time was 203 min and unit energy consumption was 65 MJ/kg.

Key words: orthogonal design, multiple regress, SiC ultra-fine powder pellets, variable temperature drying, process optimization