Journal
APPLIED SOFT COMPUTING
Volume 105, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2021.107227
Keywords
Wastewater treatment process; Soft sensor; Non-Gaussian; Over-complete; Deep Recurrent Neural Network
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Funding
- National Science Foundation of China [61174109]
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The wastewater treatment process is a complex biochemical reaction process with strong nonlinear, non-Gaussian, and time correlation characteristics. An Over-Complete Deep Recurrent Neural Network (ODRNN) method is proposed to address these issues by efficiently extracting non-Gaussian information and temporal correlation features. Simulation results demonstrate that the ODRNN based soft sensor method outperforms other state-of-the-art methods in terms of accuracy and robustness.
The wastewater treatment process (WWTP) is a complex biochemical reaction process in which sensor data has strong nonlinear, non-Gaussian and time correlation characteristics. The traditional methods ignore to consider the aforementioned three characteristics simultaneously, which may have insufficient feature extraction of WWTP. In this work, an Over-Complete Deep Recurrent Neural Network (ODRNN) method is proposed to solve the above issues. The ODRNN combines the over-complete independent component analysis (OICA) and binary particle swarm optimization (BPSO) to efficiently extract the non-Gaussian information, and then the extracted information is fed into DRNN to obtain the time correlation characteristics. In this way, the method can not only capture the nonlinear and non-Gaussian information but also extract temporal correlation of WWTP data. Simulation results on BSM1 showed that the ODRNN based soft sensor method has higher accuracy and robustness than other state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.
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