Prediction of effluent total phosphorus using PLSR-based adaptive deep belief network

WANG Gongming; LI Wenjing; QIAO Junfei

doi:10.11949/j.issn.0438-1157.20161826

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Prediction of effluent total phosphorus using PLSR-based adaptive deep belief network

更新时间：2025-12-10

- Prediction of effluent total phosphorus using PLSR-based adaptive deep belief network
- CIESC Journal Issue 5, Pages: 1987-1997(2017)
- 作者机构：
  
  1. 北京工业大学信息学部,北京,100124
  2. 计算智能与智能系统北京市重点实验室,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161826
  CLC： P183
- Online First：05 May 2017，
  
  Published：05 May 2017
- 稿件说明：
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WANG Gongming, LI Wenjing, QIAO Junfei. Prediction of effluent total phosphorus using PLSR-based adaptive deep belief network[J]. CIESC Journal, 2017, (5): 1987-1997.
DOI：

WANG Gongming, LI Wenjing, QIAO Junfei. Prediction of effluent total phosphorus using PLSR-based adaptive deep belief network[J]. CIESC Journal, 2017, (5): 1987-1997. DOI： 10.11949/j.issn.0438-1157.20161826.

摘要

针对污水处理过程出水总磷预测问题存在的强非线性、大时变等特征，提出了一种基于偏最小二乘回归自适应深度信念网络（partial least square regression adaptive deep belief network，PLSR-ADBN）的出水总磷预测方法。PLSR-ADBN是基于深度学习模型DBN的一种改进型建模方法。首先，将自适应学习率引入到DBN的无监督预训练（pre-training）阶段，来提高网络收敛速度。其次，利用PLSR方法取代传统DBN中基于梯度的逐层权值精调（fine-tuning）方法，来提高网络预测精度。同时，通过构造李雅普诺夫函数证明了PLSR-ADBN学习过程的收敛性。最后，将PLSR-ADBN用于实际污水处理过程出水总磷预测中。实验结果表明所提出的PLSR-ADBN收敛速度快且预测精度高，能够满足实际污水处理过程对出水总磷监测精度和运行效率的要求。

Abstract

Considered high nonlinearity and large transient variation

a PLSR-adaptive deep belief network (PLSR-ADBN) was proposed for prediction of total phosphorus (TP) in effluent of wastewater treatment process (WWTP). The PLSR-ADBN was an improved DBN

a deep learning model. First

an adaptive learning rate was introduced into the unsupervised pre-training stage of DBN so as to accelerate convergence rate. Secondly

PLSR was used to replace gradient fine-tuning method in conventional DBN for improving prediction accuracy. Meanwhile

a Lyapunov function was constructed to prove convergence of the PLSR-ADBN learning process. Finally

the proposed PLSR-ADBN was applied to an actual TP prediction in WWTP. The experimental results show that the method has a fast convergence rate and a high prediction accuracy

which can meet the demands for TP detection accuracy and WWTP operating efficiency.

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