A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process
| Autor: | Guoqiang Niu, Hongbin Liu, Guang-Guo Ying, Zehua Huang, Jiannan Cai, Mingzhi Huang, Renren Wu, Xiaohui Yi |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Article Subject Computer science 02 engineering and technology 010501 environmental sciences 01 natural sciences Fuzzy logic lcsh:QA75.5-76.95 Methane chemistry.chemical_compound Biogas Robustness (computer science) Genetic algorithm 0202 electrical engineering electronic engineering information engineering Anaerobic treatment Process engineering Cluster analysis Effluent 0105 earth and related environmental sciences Biogas production Pollutant Multidisciplinary business.industry Hybrid algorithm chemistry 020201 artificial intelligence & image processing Sewage treatment lcsh:Electronic computers. Computer science Anaerobic wastewater treatment business |
| Zdroj: | Complexity, Vol 2019 (2019) |
| ISSN: | 1076-2787 |
| DOI: | 10.1155/2019/2468189 |
| Popis: | The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system. |
| Databáze: | OpenAIRE |
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