膜污染
结垢
决策树
膜生物反应器
支持向量机
废水
生物反应器
人工智能
机器学习
计算机科学
过滤(数学)
污水处理
生化工程
膜
工艺工程
工程类
微滤
非线性系统
环境科学
环境工程
非线性模型
膜技术
树(集合论)
制浆造纸工业
预测建模
钥匙(锁)
作者
Nguyễn Văn Thành,Khac‐Uan Do,Thuy Phuong Nhat Tran,Tuyen Van Nguyen,Xuan-Quang Chu
摘要
Membrane fouling is widely recognized as a significant drawback of membrane technology, as it reduces filtration flux and impairs the overall efficiency of wastewater treatment systems. Accurate prediction of membrane fouling, therefore, offers a crucial pathway to optimizing system operation and developing proactive mitigation strategies. This study developed machine learning models-including linear regression, support vector regression, and decision tree regression-to predict transmembrane pressure, a key indicator of fouling severity. Input descriptors such as pH, ammonium, nitrate, and alkalinity, measured at multiple stages of the anoxic-aerobic membrane bioreactor system, were used to train and evaluate the models. Among the tested approaches, nonlinear models-particularly decision tree regression-demonstrated superior performance, achieving high prediction accuracy (R2 = 0.99). Moreover, machine learning helped identify the most influential input descriptors and uncover hidden patterns within the collected data. This study presents a promising alternative approach for predicting membrane fouling in wastewater treatment systems.
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