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ABSTRACT
This paper presents the development and evaluation of three fuzzy neural network (FNN) models for a full-scale anaerobic digestion system treating paper-mill wastewater. The aim was the investigation of feasibility of the approach-based control system for the prediction of effluent quality and biogas production from an internal circulation (IC) anaerobic reactor system. To improve FNN performance, fuzzy subtractive clustering was used to identify model's architecture and optimize fuzzy rule, and a total of 5 rules were extracted in the IF-THEN format. Findings of this study clearly indicated that, compared to NN models, FNN models had smaller RMSE and MAPE as well as bigger R for the testing datasets than NN models. The proposed FNN model produced smaller deviations and exhibited a superior predictive performance on forecasting of both effluent quality and biogas (methane) production rates with satisfactory determination coefficients greater than 0.90. From the results, it was concluded that FNN modeling could be applied in IC anaerobic reactor for predicting the biodegradation and biogas production using paper-mill wastewater.
Acknowledgments
The authors are thankful to the anonymous reviewers for their insightful comments and suggestions.
Funding
This research was supported by National Natural Science Foundation of China (No. 51208206 and 51210013), Guangdong Provincial Natural Science Foundation (No. 2016A030306033), Guangdong Provincial Science and Technology Plan Project Foundation (No. 2014A020216007), Technological Innovation Young Talents of Guangdong Special Support Plan (No. 2014TQ01Z530), Pearl River Nova Program of Guangzhou (No. 201506010058), and the Fundamental Research Funds for the Central Universities (No. 15lgpy11).