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BP神经网络和ARIMA模型对污水处理厂出水总氮浓度的模拟预测
Authors
张志强
李宣辑
+5 more
林佳敏
林晶晶
沈亮
陈金良
马聪
Publication date
28 May 2019
Publisher
Abstract
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。福建省自然科学基金项目(2018J01016);;\n厦门大学大学生创新创业训练计划项目(2018X0256
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Last time updated on 20/11/2020