自适应网络模糊推理系统和人工神经网络在渗透膜生物反应器水通量建模中的应用。
Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network.
机构信息
Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
出版信息
Bioresour Technol. 2020 Aug;310:123391. doi: 10.1016/j.biortech.2020.123391. Epub 2020 Apr 18.
Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R of 0.9755 and 0.9861, and ANN models with R of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.
渗透膜生物反应器（OMBR）是一种新兴的废水处理技术，膜污染是主要挑战。本研究旨在开发自适应网络模糊推理系统（ANFIS）和人工神经网络（ANN）模型，以模拟和预测 OMBR 中的水通量。混合液悬浮固体（MLSS）、电导率（EC）和溶解氧（DO）被用作模型输入。对于 TFC 和 CTA 膜，ANFIS 模型的 R 值分别为 0.9755 和 0.9861，ANN 模型的 R 值分别为 0.9404 和 0.9817，均表现出良好的预测能力。对于 TFC（0.2527）和 CTA（0.1230），ANFIS 模型的均方根误差低于 ANN 模型（分别为 0.4049 和 0.1449）。敏感性分析表明，对于 ANN 模型中的 TFC 和 CTA 膜，EC 是最重要的因素，而对于 ANFIS 模型，EC（TFC）和 MLSS（CTA）是关键参数。
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