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.
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