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Optimization of a coupled dual-membrane process for ammonia nitrogen recovery using machine learning

 
Authors： YOU Weiming, TANG Han, HUANG Feiyun, YAO Jingmei, HAN Le
Units： College of Environment and Ecology, Chongqing University, Chongqing 400045, China
KeyWords： ammonia nitrogen recovery; machine learning; membrane; coupled system
ClassificationCode：TQ028；TP181
year,volume(issue):pagination： 2026, 46(3):162-170

Abstract：

Efficient recovery of ammonia-nitrogen is essential for establishing a sustainable nitrogen cycle. The Donnan dialysis-osmotic distillation (DD-OD) dual-membrane coupling system recovers ammonia-nitrogen via concentration-driven and low-energy mechanisms, but its complex mass transfer processes and significant non-linear characteristics render it difficult to model for optimizing and regulating the process system. This study introduced machine learning (ML) methods to develop a CatBoost-based prediction model for ammonia-nitrogen removal and recovery rates. Following optimization, the model achieved goodness-of-fit (R2) values of 0.97 and 0.95 on the test set, respectively. Through shapley additive explanations (SHAP) and partial dependence plot (PDP) analyses, the non-linear influence of key parameters, such as operating time and feed concentration, was elucidated. Furthermore, a Bayesian optimization framework was constructed to achieve multi-objective collaborative optimization. Under the optimal conditions-a feed chamber NH+4-N concentration of 52 mmol/L, an operating time of 28.0 h, and a driving-to-target ion concentration ratio (ρ) of 1.92-the removal and recovery rates reached 92.28% and 88.03%, respectively. This research demonstrates that ML methods can effectively address the non-linear coupling characteristics of complex environmental processes, providing a robust foundation for the intelligent operation of novel environmental protection systems. 

Funds：

重庆市科技创新与应用发展专项重点项目（CSTB2024TIAD-KPX0084）； 新重庆青年创新人才项目（2024NSCQ-QNCXX0233）

AuthorIntro：
第一作者简介： 游伟明（2001-），男，福建福清人，硕士研究生，研究方向为膜法水处理、机器学习辅助的膜智能设计等.*通讯作者，E-mail：lehan@cqu.edu.cn

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