污水处理出水BOD区间预测建模

Interval predictor models for effluent BOD of wastewater treatment

摘要:

生化需氧量(BOD)是评价水质的重要指标，也是污水处理过程中直接控制的参数.为了提高污水处理质量，需要寻找BOD的有效测量方法.本文给出一种新的BOD软测量方法，可以实现其保证估计.采用主元分析方法选取BOD预测的主要辅助变量.利用径向基函数神经网络的逼近能力，将其用于污水处理出水BOD软测量建模.径向基函数神经网络的中心被确定之后，考虑到建模误差有界，使用参数线性集员辨识算法得到网络输出权值的集合描述.在污水处理系统运行过程中，所建立好的软测量模型可以预测出水BOD的上下界.此外，建立多个软测量模型，并将多模型测量结果进行融合以降低单一模型所给结果的保守性.实验结果表明本文方法的有效性.

Abstract:

Biochemical oxygen demand (BOD) is an important index for evaluating water quality, and a variable directly controlled in the wastewater treatment process. To improve the performance of wastewater treatment, it is necessary to find out an effective method for measuring BOD. This paper presents a new soft measurement which can provide guaranteed estimation of the effluent BOD. The principal component analysis is utilized to select the secondary variables for the soft sensor. In virtue of its simple topological structure and universal approximation ability, the radial basic function neural network (RBFNN) is utilized in the soft sensor modeling. Considering the bounded modeling error, linear-in-parameters set membership identification algorithm is used to obtain a description of the uncertain set of the output weights after the determination of centers of the RBFNN. The RBFNN model with uncertain output weights can predict the upper and lower bounds of the effluent BOD during the wastewater treatment. Besides, a bundle of soft sensors is constructed and the intersection of the results given by the soft sensors is used to lower the conservatism by using a single sensor. Experiment results show the satisfying performance of the proposed method.