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Open AccessArticle

Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

by Junfei Qiao 1,2,*, Zhiqiang Hu 1,2 and Wenjing Li 1,2
1
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas N. Angelakis
Water 2016, 8(12), 581; https://doi.org/10.3390/w8120581
Received: 1 August 2016 / Revised: 29 November 2016 / Accepted: 30 November 2016 / Published: 19 December 2016
The precision of soft measurement for biochemical oxygen demand (BOD) is always restricted due to various factors in the wastewater treatment plant (WWTP). To solve this problem, a new soft measurement modeling method based on chaos theory is proposed and is applied to BOD measurement in this paper. Phase space reconstruction (PSR) based on Takens embedding theorem is used to extract more information from the limited datasets of the chaotic system. The WWTP is first testified as a chaotic system by the correlation dimension (D), the largest Lyapunov exponents (λ1), the Kolmogorov entropy (K) of the BOD and other water quality parameters time series. Multivariate chaotic time series modeling method with principal component analysis (PCA) and artificial neural network (ANN) is then adopted to estimate the value of the effluent BOD. Simulation results show that the proposed approach has higher accuracy and better prediction ability than the corresponding modeling approaches not based on chaos theory. View Full-Text
Keywords: soft measurement; wastewater treatment plant; biochemical oxygen demand; phase space reconstruction; multivariate chaotic time series soft measurement; wastewater treatment plant; biochemical oxygen demand; phase space reconstruction; multivariate chaotic time series
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Qiao, J.; Hu, Z.; Li, W. Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD). Water 2016, 8, 581.

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