Integrated soft sensor with wavelet neural network and adaptive weighted fusion for water quality estimation in wastewater treatment process. (August 2018)

• Record Type: Journal Article
• Authors: Cong, Qiumei; Yu, Wen
• Is Part Of: Measurement. Volume 124(2018)
• Page Start: 436
• Page End: 446
• Publication Date: 2018-08
• Subjects: Wastewater treatment process -- Soft sensor -- Chemical Oxygen Demand -- Stable learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.01.001
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An online estimation method of COD under various operating regimes is proposed. Stable learning of sub models can make mean modeling error within bounded scope. Adaptive weighted fusion reflects the variation characteristic of operating regimes. Abstract: It is difficult to estimate the water quality of the wastewater treatment process, because the operating conditions are frequently changed. This paper gives an effective adaptive estimation method, which uses Hammerstein with wavelet neural networks, adaptive weighted fusion, and approximate linear dependence (ALD) analysis. Adaptive stable learning algorithm for the local Hammerstein with wavelet neural networks is proposed. A novel synchronous learning of fusion weighs is discussed. On-line calibration of operating centers with ALD improves the estimation accuracy. The experimental results show that the proposed estimation method for the water quality COD (Chemical Oxygen Demand) is satisfied compared with the laboratory results even when the operating conditions are changed frequently.