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Published on: October 1, 2013
Intelligent optimal control model of selection pressure for rapid culture of aerobic granular sludge based on machine
Zhe Liu1, Jie Lei2, Linshan Cheng2
1School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an 710055, China; Key Lab of Northwest Water Resource, Environment and Ecology, Ministry of Education, Xi'an University of Architecture and Technology, Xi'an 710055, China.
Abstract:
Aerobic Granular Sludge (AGS) has advantages over Activated sludge (AS) but faces challenges with long granulation periods. In this study, a novel grey-box model is devised to optimize the cultivation of AGS to shorten the formation time. This model is based on an existing white-box model. The modeling process starts with the application of four sensitivity analysis methods to assess the 12 model metrics selected. Subsequently, 12 prediction models were constructed by combining the six Machine learning (ML) algorithms and integrated algorithms, with the best performance selected (R2 = 0.98). Finally, an AGS selection pressure planning model was designed in conjunction with a simulated annealing (SA) algorithm to guide AGS training. The results demonstrate that AGS formation could be achieved within four days under the model's optimal control. Therefore, the establishment of this model provides a new technique for the cultivation of AGS.
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