{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:57:01Z","timestamp":1777705021908,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>Due to the complexity of the factors influencing membrane fouling in membrane bioreactors (MBR), it is difficult to accurately predict membrane fouling. This paper proposes a multi-strategy of integration aquila optimizer deep belief network (MAO-DBN) based membrane fouling prediction method. The method is developed to improve the accuracy and efficiency of membrane fouling prediction. Firstly, partial least squares (PLS) are used to reduce the dimensionality of many membrane fouling factors to improve the algorithm\u2019s generalization ability. Secondly, considering the drawbacks of deep belief network (DBN) such as long training time and easy overfitting, piecewise mapping is introduced in aquila optimizer (AO) to improve the uniformity of population distribution, while adaptive weighting is used to improve the convergence speed and prevent falling into local optimum. Finally, the prediction of membrane fouling is carried out by utilizing membrane fouling data as the research object. The experimental results show that the method proposed in this paper can achieve accurate prediction of membrane fluxes, with an 88.45% reduction in RMSE and 87.53% reduction in MAE compared with the DBN model before improvement. The experimental results show that the model proposed in this paper achieves a prediction accuracy of 98.61%, both higher than other comparative models, which can provide a theoretical basis for membrane fouling prediction in the practical operation of membrane water treatment.<\/jats:p>","DOI":"10.3233\/jifs-233655","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:48:48Z","timestamp":1710247728000},"page":"10923-10939","source":"Crossref","is-referenced-by-count":0,"title":["MAO-DBN based membrane fouling prediction"],"prefix":"10.1177","volume":"46","author":[{"given":"Zhiwen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China"},{"name":"Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou, China"},{"name":"National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibin","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoke","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guobi","family":"Ling","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"9","key":"10.3233\/JIFS-233655_ref1","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1111\/fwb.13519","article-title":"Release of treated effluent into streams: A global review of ecological impacts with a consideration of its potential use for environmental flows[J]","volume":"65","author":"Hamdhani","year":"2020","journal-title":"Freshwater Biology"},{"key":"10.3233\/JIFS-233655_ref2","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.memsci.2019.03.064","article-title":"Effects of packing carriers and ultrasonication on membrane fouling and sludge properties of anaerobic side-stream reactor coupled membrane reactors for sludge reduction[J]","volume":"581","author":"Anonymous","year":"2019","journal-title":"Journal of Membrane Science"},{"key":"10.3233\/JIFS-233655_ref3","doi-asserted-by":"crossref","first-page":"120670","DOI":"10.1016\/j.memsci.2022.120670","article-title":"Membrane fouling diagnosis of membrane components based on multi-feature information fusion[J]","volume":"657","author":"Shi","year":"2022","journal-title":"Journal of Membrane Science"},{"key":"10.3233\/JIFS-233655_ref4","doi-asserted-by":"crossref","first-page":"118098","DOI":"10.1016\/j.watres.2022.118098","article-title":"Insights on fouling development and characteristics during different fouling stages between a novel vibrating MBR and an air-sparging MBR for domestic wastewater treatment[J]","volume":"212","author":"Wang","year":"2022","journal-title":"Water Research"},{"key":"10.3233\/JIFS-233655_ref5","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.biortech.2017.03.005","article-title":"Membrane fouling control in membrane bioreactors (MBRs) using granular materials[J]","volume":"240","author":"Iorhemen","year":"2017","journal-title":"Bioresource Technology"},{"key":"10.3233\/JIFS-233655_ref6","doi-asserted-by":"crossref","first-page":"151109","DOI":"10.1016\/j.scitotenv.2021.151109","article-title":"Modeling, simulation and control of biological and chemical P-removal processes for membrane bioreactors (MBRs) from lab to full-scale applications: State of the art[J]","volume":"809","author":"Nadeem","year":"2022","journal-title":"Science of The Total Environment"},{"issue":"9","key":"10.3233\/JIFS-233655_ref7","doi-asserted-by":"crossref","first-page":"843","DOI":"10.3390\/membranes12090843","article-title":"Membrane Fouling Diagnosis of Membrane Components Based on MOJS-ADBN[J]","volume":"12","author":"Shi","year":"2022","journal-title":"Membranes"},{"key":"10.3233\/JIFS-233655_ref8","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.desal.2013.06.011","article-title":"Influence of various operating conditions on cleaning efficiency in sequencing batch reactor (SBR) activated sludge process. 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