微气泡臭氧氧化+生化中试装置深度处理化工废水运行性能

Operation performance of a pilot plant of microbubble ozonation and biological treatment for advanced treatment of chemical wastewater

采用微气泡臭氧氧化+生化中试装置深度处理化工废水,对其长期连续运行性能进行评估并构建神经网络预测模型。结果表明,该中试装置能够稳定有效深度处理化工废水,微气泡臭氧氧化对COD和UV_(254)平均去除率分别为35.1%和52.5%,臭氧投加量与进水COD的质量比m是影响运行性能的重要参数,控制在0.5较为适宜。微气泡臭氧氧化处理可有效改善废水可生化性,BOD_(5)/COD由0.10提高至0.33,BOD_(5)增加量与COD去除量的比值可达到37.23%,对臭氧氧化后的废水进行生化处理,BOD_(5)去除量占COD去除量的80%以上。采用误差反向传播神经网络(BPNN)建立微气泡臭氧氧化处理中m对COD去除性能的预测模型,预测模型具有稳定的、较为精准的预测能力。

A pilot plant of microbubble ozonation and biological treatment was used to treat chemical wastewater in depth,and its long-term operation performance was evaluated and predicted by a neural network model.The results showed that the chemical wastewater could be treated stably and effectively in the pilot plant,and the average re-moval rates of COD and UV_(254) in the phase of microbubble ozontion were 35.1%and 52.5%,respectively.The mass ratio of ozone dosage to influent COD amount was an important parameter influencing the operation performance,which should be controlled as 0.5 for better performance.The wastewater biodegradability could be improved effec-tively by microbubble ozonation,BOD_(5)/COD could increase from 0.10 to 0.33 and the ratio of BOD_(5) generated amount to COD removed amount could reach to 37.23%.Then biological treatment was applied for the improved wa-ter,and the contribution of BOD_(5) removal to COD removal was more than 80%in biological treatment.Back propa-gation neural network(BPNN)was used to establish a prediction model of the effect of m on COD removal perfor-mance in microbubble ozonation,which showed a stable prediction ability with acceptable accuracy.