Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 14 January 2019 / Approved: 15 January 2019 / Online: 15 January 2019 (09:13:22 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Appl. Sci. 2019, 9, 523
DOI: 10.3390/app9030523

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 (ELM) 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 (ICS) 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 Areas

Biochemical oxygen demand (BOD); Cuckoo search algorithm (CSA); Extreme learning machine (ELM); Soft sensor; Wastewater treatment process

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