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

Zhe Liu; Jie Lei; Linshan Cheng; Rushuo Yang; Zhuangzhuang Yang; Bingrui Shi; JiaXuan Wang; Aining Zhang; Yongjun Liu

doi:10.1016/j.biortech.2024.131509

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

Bioresour Technol. 2024 Dec:413:131509. doi: 10.1016/j.biortech.2024.131509. Epub 2024 Sep 24.

Authors

Zhe Liu¹, Jie Lei², Linshan Cheng², Rushuo Yang², Zhuangzhuang Yang², Bingrui Shi², JiaXuan Wang³, Aining Zhang², Yongjun Liu⁴

Affiliations

¹ 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. Electronic address: zheliu@xauat.edu.cn.
² School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an 710055, China.
³ School of Architecture and Civil Engineering, Xi'an University of Science and Technology, Yan Ta Road, No. 58, Xi'an 710054, China.
⁴ 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.

PMID: 39321933
DOI: 10.1016/j.biortech.2024.131509

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.

Keywords: Black-box model; Grey-box model; Intelligent algorithms; Planning model; White-box model.

MeSH terms

Aerobiosis
Algorithms*
Computer Simulation
Machine Learning*
Models, Theoretical
Sewage* / microbiology

Substances

Sewage