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响应曲面法优化废旧铅酸蓄电池铅膏脱硫工艺研究
引用本文:邢相栋,莫川,李林波,王莎.响应曲面法优化废旧铅酸蓄电池铅膏脱硫工艺研究[J].有色金属工程,2019,9(7).
作者姓名:邢相栋  莫川  李林波  王莎
作者单位:;1.西安建筑科技大学冶金工程学院
摘    要:以碳酸钠为脱硫转化剂,运用响应曲面法(RSM-response surface method)统计分析和研究了转化剂(Na_2CO_3)浓度、转化温度、转化时间、液固比等参数对废旧铅酸蓄电池中铅膏脱硫转化的影响。采用扫描电镜-能谱仪(SEM-EDS)、X射线衍射分析仪(XRD)对铅膏脱硫前后的结构和形貌进行了表征。结果表明,脱硫前铅膏中存在大量硫酸铅和铅的氧化物,脱硫后铅主要以碱式碳酸铅形式存在,铅膏变为疏松多孔的团状聚集物,氧化物含量基本不变。铅膏脱硫最佳条件为:转化剂浓度1.75mol/L、转化温度55℃、转化时间61min、液固比7.99∶1。铅膏脱硫效率最大为98.46%,与模型预测值的相对误差仅为1.12%,表明所选模型具有良好的预测性能。

关 键 词:响应曲面法  铅膏脱硫  团状聚集物  预测性能
收稿时间:2018/8/29 0:00:00
修稿时间:2018/9/28 0:00:00

Desulfurization technology of lead-acid battery lead paste optimized by response surface methodology
xingxiangdong,mochuan,lilinbo and wangsha.Desulfurization technology of lead-acid battery lead paste optimized by response surface methodology[J].Nonferrous Metals Engineering,2019,9(7).
Authors:xingxiangdong  mochuan  lilinbo and wangsha
Abstract:Using sodium carbonate as the desulfurizer, the effect of conversion agent (Na2CO3) concentration, conversion temperature, conversion time, liquid-solid ratio and other parameters on the desulfurization conversion of lead paste in waste lead-acid batteries was analyzed by applying the response surface method (RSM-response surface method), and the structure and morphology of the lead paste before and after desulfurization were characterized by scanning electron microscopy-energy spectroscopy (SEM-EDS) and X-ray diffraction analyzer (XRD). The results show that there are a large amount of lead sulfate and lead oxide in the lead paste before desulfurization. The lead mainly exists in the form of basic lead carbonate. The lead paste becomes loose porous group aggregate, and the oxide content is basically unchanged after desulfurization. The optimum conditions for desulfurization of lead paste are: conversion agent concentration 1.75mol/L, conversion temperature 55°C, conversion time 61min, liquid-solid ratio 7.99:1. The maximum desulfurization efficiency of lead paste is 98.46%, and the relative error with the predicted value of the model is only 1.12%, indicating that the model has good predictive performance.
Keywords:
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