Soft measurement modeling based on chaos theory for biochemical oxygen demand (BOD)
Soft measurement modeling based on chaos theory for biochemical oxygen demand (BOD)
复制标题
基于混沌理论的生化需氧量（BOD）软测量建模
作者：
Qiao Junfei;Hu Zhiqiang;Li Wenjing
由于污水处理厂中各种因素的影响，制约了生化需氧量软测量的精度。针对这一问题，提出了一种基于混沌理论的软测量建模方法，并将其应用于BOD测量中。利用基于Takens嵌入定理的相空间重构方法，从有限的混沌数据集中提取更多的混沌信息。通过BOD等水质参数时间序列的关联维数（D）、最大李雅普诺夫指数（λ1）、柯尔莫哥洛夫熵（K），证明污水处理厂是一个混沌系统。然后采用主成分分析（PCA）和人工神经网络（ANN）相结合的多元混沌时间序列建模方法对出水BOD值进行估计。仿真结果表明，该方法比非混沌建模方法具有更高的精度和更好的预测能力。
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.