文献类型：期刊文献

英文题名：Modeling UF fouling and backwash in seawater RO feedwater treatment using neural networks with evolutionary algorithm and Bayesian binary classification

作者：Zhou, Yang[1];Khan, Bilal[2,5];Gu, Han[3];Christofides, Panagiotis D.[4];Cohen, Yoram[4,5]

机构：[1]East China Univ Sci & Technol, Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Calif State Univ San Bernardino, Dept Comp Sci & Engn, San Bernardino, CA 92407 USA;[3]Res & Dev Dept, Orange Cty Water Dist, Fountain Valley, CA USA;[4]Univ Calif Los Angeles, Water Technol Res Ctr, Henry Samueli Sch Engn & Appl Sci, Chem & Biomol Engn Dept, Los Angeles, CA 90095 USA;[5]Univ Calif Los Angeles, Inst Environm & Sustainabil, Los Angeles, CA 90095 USA

年份：2021

卷号：513

外文期刊名：DESALINATION

收录：;EI(收录号：20212210443190);WOS:【SCI-EXPANDED(收录号:WOS:000663711300002)】；

基金：This work was funded, in part, by the United States Office of Naval Research (N00014-11-1-0950 ONR and ONR N000140911132) , California Department of Water Resources (46-4120 and RD200609) , U.S. Bureau of Reclamation (R13AC80025) , Naval Facilities Engineering Command (N6258311C0630) , the Grundfos Pumps Coporation, DuPontInge GmbH, and UCLA Water Technology Research (WaTeR) Center. The authors also acknowledge the technical assistance during the field study by personnel of the Naval Facilities Engineering and Expeditionary Warfare Center (NAVFAC EXWC) at Port Hueneme, CA, William Varnava, Mark Miller, Paul Giuffrida, Theresa Hoffard, Joseph Saenz, and Micah Ing.

语种：英文

外文关键词：Ultrafiltration; Seawater RO feed pre-treatment; Backwash efficiency; Hydraulic membrane resistance; Back propagation neural network (BPNN); Ensemble BPNN-AEA UF model

摘要：A machine learning approach to describing the dynamics of ultrafiltration performance in pretreatment of seawater reverse osmosis (RO) feedwater was explored via ensemble back propagation neural network (BPNN) model. The BPNN model was developed with Alopex evolutionary algorithm (AEA) optimization and AdaBoost strategy. The progression of ultrafiltration (UF) membrane resistance during both filtration and backwash, along with backwash efficiency were modeled via the ensemble BPNN-AEA approach relying on 422 days of operational data for an integrated SWRO UF-RO system. Model performance, for UF membrane resistance and backwash efficiency, evaluated over a wide range of operating conditions and coagulant dosing strategies, revealed excellent performance with a forecasting capability even for cases of temporally variable water quality. The performance level attained with the current machine learning modeling approach, which is particularly suited for handling the dynamics of UF operation, should prove useful for (a) determining UF performance deviation from intended baseline performance, (b) forecasting expected UF performance due to anticipated changes in water quality, and (c) providing a basis for model-based control of UF operation.