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

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ID: 69774
2016
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Abstract
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.
Reference Key
qiao2016softwater Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Qiao, Junfei;Hu, Zhiqiang;Li, Wenjing;
Journal water
Year 2016
DOI
DOI not found
URL
http://www.mdpi.com/2073-4441/8/12/581
Keywords
Engineering (General). Civil engineering (General) Technology Mechanical engineering and machinery Science physics water supply for domestic and industrial purposes telecommunication electronics mathematics electronic computers. computer science hydraulic engineering geophysics. cosmic physics wastewater treatment plant phase space reconstruction biochemical oxygen demand multivariate chaotic time serieshydraulic engineeringwater supply for domestic and industrial purposes

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