摘要
膜生物反应器(MBR)因其出水水质优良、占地面积小等优势,在污水处理领域应用广泛,但膜污染问题严重制约其发展。近年来,基于人工神经网络(ANN)等智能模型的膜污染预测与控制研究取得显著进展。综述了MBR膜污染的关键影响因素,对比了传统数学模型与ANN模型的预测性能差异,重点阐述了简单ANN、优化算法ANN及深度学习ANN在膜污染预测中的应用现状与效果。同时,指出当前ANN模型存在的解释性差、数据依赖性强等局限,并对其未来发展方向提出展望,为智能模型在MBR膜污染控制中的深化应用提供参考。
Membrane bioreactor(MBR)is widely used in the field of wastewater treatment because of its excellent effluent quality and small footprint.However,membrane fouling seriously restricts its development.In recent years,significant progress has been made in the prediction and control of membrane fouling based on intelligent models such as artificial neural network(ANN).The key influencing factors of MBR membrane fouling were reviewed,and the prediction performance differences between traditional mathematical models and ANN models were compared.The application status and effects of simple ANN,optimization algorithm ANN and deep learning ANN in membrane fouling prediction were emphasized.At the same time,it pointed out the limitations of the current ANN model,such as poor interpretation and strong data dependence,and put forward the prospect of its future development direction,providing a reference for the deepening application of intelligent model in MBR membrane fouling control.
作者
李焕伍
温贵田
杨娟
Li Huanwu;Wen Guitian;Yang Juan(Yunnan Provincial Key Laboratory of Plateau Wetland Protection and Restoration and Ecological Service,College of Ecology and Environment(College of Wetlands),Southwest Forestry University,Kunming 650224;National Plateau Wetland Research Center,College of Ecology and Environment(College of Wetlands),Southwest Forestry University,Kunming 650224;Weifang Vocational College of Environmental Engineering,Weifang 261300)
出处
《化工新型材料》
北大核心
2025年第S2期59-63,共5页
New Chemical Materials
基金
国家自然科学基金(22166033)
云南省“兴滇英才”支持计划项目(2023年)(990124083)。
关键词
膜生物反应器
膜污染
人工神经网络
预测模型
控制策略
membrane bioreactor
membrane fouling
artificial neural network
prediction model
control strategy
