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。
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 WWTE 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.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2017年第5期1987-1997,共11页
CIESC Journal
基金
国家杰出青年科学基金项目(61225016)
国家自然科学基金项目(61533002
61603009)
中国博士后科学基金项目(2015M570910)
朝阳区博士后科研基金项目(2015ZZ-6)
北京工业大学基础研究基金项目(002000514315501)~~
