摘要
晶硅太阳能电池中铝和银的回收是废弃光伏组件资源化的重要内容,氧化-硫酸同步浸出是一种浸出铝和银的有效方式,浸出效率高,经济效益好。本文采用全因子实验设计的方法,以铝和银的浸出率为响应值,以浸出温度、浸出时间、液固比、过氧化氢浓度4个工艺条件为参数因子,通过建立回归模型,分析各参数因子之间的相互关系,得出优化方案,并对其进行验证。实验得到的最佳浸出条件如下:浸出温度为90℃,浸出时间为2 h,液固比为10∶1 (mL∶g),过氧化氢浓度为75 g/L,铝和银浸出率均在99%以上。结果表明:铝和银浸出率的拟合回归模型可信,精确度高,回归模型得出的优化方案经过平行实验验证,误差小,可信度高,全因子实验设计方案合理可靠。
The recovery of aluminum and silver in crystalline silicon solar cells is an important part of the recycling of waste photovoltaic modules.Simultaneous oxidation-sulfuric acid leaching is an effective method for leaching aluminum and silver with high leaching efficiency and good economic benefits.This paper adopts the method of full factorial experimental design,takes the leaching rate of aluminum and silver as the response value,and uses the four process conditions of leaching temperature,leaching time,liquid-to-solid ratio and hydrogen peroxide concentration as parameter factors.A regression model is established to analyze each The correlation between the parameter factors,the optimization plan is obtained,and it is verified.The optimal leaching conditions obtained in the experiment are as follows:leaching temperature is 90℃,leaching time is 2 h,liquid-to-solid ratio is 10∶1 mL/g,hydrogen peroxide concentration is 75 g/L,and aluminum and silver leaching rates are both above 99%.The research results show that the fitting regression model of aluminum and silver leaching rate is credible and has high accuracy.The optimization scheme obtained by the regression model has been verified by parallel experiments,with small errors and high credibility,and the full factorial experimental design scheme is reasonable and reliable.
作者
李炜垚
焦芬
陈琛
覃文庆
刘维
LI Wei-yao;JIAO Fen;CHEN Chen;QIN Wen-qing;LIU Wei(School of Minerals Processing and Bioengineering,Central South University,Changsha 410083,China)
出处
《中国有色金属学报》
EI
CAS
CSCD
北大核心
2023年第3期898-911,共14页
The Chinese Journal of Nonferrous Metals
基金
国家重点研发计划资助项目(2020YFC1909203)
国家自然科学基金资助项目(51874356)
湖南省重点实验室资助项目(2018TP1002)。
关键词
晶硅电池片
浸出
实验设计
优化工艺
crystalline silicon cell
leaching
experimental design
optimization process
