A real-time BOD estimation method in wastewater treatment process based on an optimized extreme learning machine

Yu, P, Cao, J, Jegatheesan, J and Du, X 2019, 'A real-time BOD estimation method in wastewater treatment process based on an optimized extreme learning machine', Applied Sciences, vol. 9, no. 3, pp. 1-12.


Document type: Journal Article
Collection: Journal Articles

Title A real-time BOD estimation method in wastewater treatment process based on an optimized extreme learning machine
Author(s) Yu, P
Cao, J
Jegatheesan, J
Du, X
Year 2019
Journal name Applied Sciences
Volume number 9
Issue number 3
Start page 1
End page 12
Total pages 12
Publisher MDPIAG
Abstract 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.
Subject Wastewater Treatment Processes
Environmental Chemistry (incl. Atmospheric Chemistry)
Keyword(s) Biochemical oxygen demand (BOD)
Cuckoo search algorithm (CSA)
Extreme learning machine (ELM)
Soft sensor
Wastewater treatment process
DOI - identifier 10.3390/app9030523
Copyright notice © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
ISSN 2076-3417
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