A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment

深信不疑网络 人工神经网络 过程(计算) 可靠性(半导体) 维数之咒 遗传算法 计算机科学 机器学习 人工智能 数据挖掘 功率(物理) 量子力学 物理 操作系统
作者
Guoqiang Niu,Xiaohui Yi,Chen Chen,Xiaoyong Li,Donghui Han,Bo Yan,Mingzhi Huang,Guang‐Guo Ying
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:265: 121787-121787 被引量:92
标识
DOI:10.1016/j.jclepro.2020.121787
摘要

Abstract Recycling wastewater of the pulping and paper-making industry are widely considered for clean production, which heavily rely on the timely and accurate monitoring in paper-making wastewater treatment processes. A novel predicting model based on genetic-deep belief network algorithm was proposed to improve the predictive accuracy and reliability for process monitoring. Considering the deep belief networks (DBN) as a deep learning model is aiming to describe the relationship among variables in a complex process modeling, genetic algorithm (GA) was employed to reduce the input variables dimensionality, simplify the network structure and overcome the dynamic characteristic difficulties of process data in monitoring. Compared with DBN and back propagation neural network (BPNN), the GA-DBN effectively achieved a better predictive accuracy than other tests models in complex wastewater treatment processes. The value of the coefficient of determination of GA-DBN model is increased by 1.71–1.86% and 5.21–9.32%, respectively. The GA-DBN model can be structured with fewer variables or samples to describe the complex paper-making wastewater treatment process, obtaining the better model performance and predictive accuracy.
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