Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)
AbstractThe 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
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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.
Qiao J, Hu Z, Li W. Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD). Water. 2016; 8(12):581.Chicago/Turabian Style
Qiao, Junfei; Hu, Zhiqiang; Li, Wenjing. 2016. "Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)." Water 8, no. 12: 581.
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