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Open AccessArticle

A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

by Ping Yu 1,2,3, Jie Cao 1,2,3, Veeriah Jegatheesan 4,* and Xianjun Du 1,4
1
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2
Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China
3
National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China
4
School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(3), 523; https://doi.org/10.3390/app9030523
Received: 14 January 2019 / Revised: 31 January 2019 / Accepted: 1 February 2019 / Published: 4 February 2019
(This article belongs to the Section Environmental and Sustainable Science and Technology)
It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine and the threshold of the hidden layer are encoded as the cuckoo’s nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy, and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases. View Full-Text
Keywords: Biochemical oxygen demand (BOD); cuckoo search algorithm (CSA); extreme learning machine (ELM); soft sensor; wastewater treatment process Biochemical oxygen demand (BOD); cuckoo search algorithm (CSA); extreme learning machine (ELM); soft sensor; wastewater treatment process
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Yu, P.; Cao, J.; Jegatheesan, V.; Du, X. A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine. Appl. Sci. 2019, 9, 523.

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