The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model
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摘要: 污水处理厂出水总氮(TN)浓度是评价水处理效果的关键指标之一。建立BP神经网络模型对污水处理厂脱氮工艺进行模拟,引入自回归整合移动平均模型(ARIMA模型)对污水处理厂未来短期出水TN浓度进行预测。结果表明:BP神经网络模型在训练集和测试集模拟结果的平均相对误差分别为15.9%和16.5%,模型预测结果的平稳性较差;ARIMA模型对未来7 d出水TN浓度的时序预测平均误差为4.41%,预测精度较高;2个模型相结合有助于实现污水处理厂快捷和高效的在线检测。Abstract: Total nitrogen in effluent is one of the critical indicators for evaluating the performance of wastewater treatment plants. A BP neural network model was developed to simulate the present nitrogen removal system for wastewater treatment, and an autoregressive integrated moving average (ARIMA) model was creatively applied to realize the short-term prediction of future effluent. The results showed that the simulation average relative error of BP model on training set was 15.9%, and that on test set was 16.5%,which revealed that the stability of model prediction was poor. The average error of the ARIMA model for predicting the total nitrogen value in the coming week was around 4.41%, which showed high prediction accuracy. The combination of the two models could help fast and efficient on-line detection of wastewater treatment plants.
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Key words:
- wastewater treatment /
- TN /
- BP neural network /
- ARIMA model
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