臭氧—焙尘去除ZnSO4溶液中砷的响应曲面法优化研究
Optimization of response surface methodology for removing arsenic from ZnSO4 solution by ozone roasting dust
刘殿传 1张奇 2夏洪应 2张特 1付光 1陆占清3
作者信息
- 1. 云南驰宏锌锗股份有限公司
- 2. 昆明理工大学冶金与能源工程学院
- 3. 云南驰宏锌锗股份有限公司;昆明理工大学冶金与能源工程学院
- 折叠
摘要
文章利用单因素试验,研究O3 流量、焙尘添加量和反应时间对砷去除率的影响.通过响应曲面RSM的Box-Beichen设计,以O3 流量、焙尘添加量和反应时间为影响因素,建立了砷去除率的预测模型.模型拟合结果表明:影响砷去除率的因素顺序依次是O3 流量>焙尘添加量>反应时间.砷去除率模型在99%的置信区间内显著,模型R2 为 0.997 8,说明该模型具有很好的拟合精度和可靠的预测力.模型优化的最优除砷条件为:O3 流量 867 mg/L,焙尘添加量16 g/L,反应时间46 min,在此条件下,预测砷去除率最大值为95.75%,试验值为95.03%,两者仅相差0.72 个百分点.综上,将RSM应用于砷去除率的工艺优化是一种有效的方法.
Abstract
The article uses a single-factor experiment to study the effects of ozone flow rate,roasting dust addition amount,and reaction time on arsenic removal efficiency.By utilizing the Box-Behnken design of response surface methodology(RSM),with ozone flow rate,roasting dust addition amount,and reaction time as influencing factors,a predictive model for arsenic removal efficiency was estab-lished.The fitting results of the model indicate that the factors influencing arsenic removal efficiency are in the following order:ozone flow rate>roasting dust addition amount>reaction time.The arsenic removal model is significant within a 99%confidence interval,with an R-squared value of 0.997 8,demonstrating good fitting accuracy and reliable predictive power of the model.The optimized condi-tions for arsenic removal are determined to be an ozone flow rate of 867 mg/L,roasting dust addition a-mount of 16 g/L,and reaction time of 46 minutes.Under these conditions,the predicted maximum ar-senic removal rate is95.75%,with an experimental value of 95.03%,yielding a difference of only 0.72 percent point.In conclusion,the application of RSM for process optimization of arsenic removal rate is an effective method.
关键词
除砷/ZnSO4/溶液/臭氧/焙尘/响应曲面Key words
arsenic removal/ZnSO4/solution/ozone/roasting dust/response surface methodology引用本文复制引用
基金项目
国家重点研发计划(2021YFC290281)
云南省锌资源技术创新中心项目(202405AK340004)
云南兴滇人才支撑项目产业创新人才项目(2019-1096)()
云南兴滇人才青年人才项目(2018-73)
出版年
2024
NETL
NSTL
万方数据