Intelligent optimal control model of selection pressure for rapid culture of aerobic granular sludge based on machine learning and simulated annealing algorithm.

Journal: Bioresource technology
PMID: 39321933

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 (R = 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.

Authors

  • Zhe Liu
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
  • Jie Lei
    State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China. Electronic address: jielei@mail.xidian.edu.cn.
  • Linshan Cheng
    School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an 710055, China.
  • Rushuo Yang
    School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an 710055, China.
  • Zhuangzhuang Yang
    School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an 710055, China.
  • Bingrui Shi
    School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an 710055, China.
  • Jiaxuan Wang
    IVSLab, The University of Auckland, Auckland, New Zealand.
  • Aining Zhang
    School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an 710055, China.
  • Yongjun Liu
    School 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.

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