{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:46:07Z","timestamp":1743075967452,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":14,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819972395"},{"type":"electronic","value":"9789819972401"}],"license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-7240-1_20","type":"book-chapter","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T11:03:23Z","timestamp":1697108603000},"page":"254-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NARXNN Modeling of Ultrafiltration Process for Drinking Water Treatment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3123-2704","authenticated-orcid":false,"given":"Mashitah Che","family":"Razali","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8522-2507","authenticated-orcid":false,"given":"Norhaliza Abdul","family":"Wahab","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4940-0991","authenticated-orcid":false,"given":"Noorhazirah","family":"Sunar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6126-3969","authenticated-orcid":false,"given":"Nur Hazahsha","family":"Shamsudin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7194-3645","authenticated-orcid":false,"given":"Muhammad Sani","family":"Gaya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7160-6948","authenticated-orcid":false,"given":"Azavitra","family":"Zainal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","first-page":"120102","DOI":"10.1016\/j.seppur.2021.120102","volume":"282","author":"W Liu","year":"2022","unstructured":"Liu, W., Yang, K., Qu, F., Liu, B.: A moderate activated sulfite pre-oxidation on ultrafiltration treatment of algae-laden water: fouling mitigation, organic rejection, cell integrity and cake layer property. Sep. Purif. Technol. 282, 120102 (2022)","journal-title":"Sep. Purif. Technol."},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.jes.2018.01.020","volume":"73","author":"X Wang","year":"2018","unstructured":"Wang, X., Ma, B., Bai, Y., Lan, H., Liu, H., Qu, J.: The effects of hydrogen peroxide pre-oxidation on ultrafiltration membrane biofouling alleviation in drinking water treatment. J. Environ. Sci. 73, 117\u2013126 (2018)","journal-title":"J. Environ. Sci."},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"116122","DOI":"10.1016\/j.desal.2022.116122","volume":"543","author":"H Chang","year":"2022","unstructured":"Chang, H., et al.: Long-term operation of ultrafiltration membrane in full-scale drinking water treatment plants in China: characteristics of membrane performance. Desalination 543, 116122 (2022)","journal-title":"Desalination"},{"key":"20_CR4","doi-asserted-by":"publisher","first-page":"112629","DOI":"10.1016\/j.envres.2021.112629","volume":"206","author":"L Zhang","year":"2022","unstructured":"Zhang, L., et al.: The performance of electrode ultrafiltration membrane bioreactor in treating cosmetics wastewater and its anti-fouling properties. Environ. Res. 206, 112629 (2022)","journal-title":"Environ. Res."},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.psep.2015.03.015","volume":"96","author":"SA Mirbagheri","year":"2015","unstructured":"Mirbagheri, S.A., Bagheri, M., Bagheri, Z., Kamarkhani, A.M.: Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm. Process Saf. Environ. Prot. 96, 111\u2013124 (2015)","journal-title":"Process Saf. Environ. Prot."},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"114888","DOI":"10.1016\/j.watres.2019.114888","volume":"164","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., et al.: Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Res. 164, 114888 (2019)","journal-title":"Water Res."},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.bej.2018.02.001","volume":"133","author":"F Schmitt","year":"2018","unstructured":"Schmitt, F., Banu, R., Yeom, I.-T., Do, K.-U.: Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater. Biochem. Eng. J. 133, 47\u201358 (2018)","journal-title":"Biochem. Eng. J."},{"issue":"1384","key":"20_CR8","first-page":"1","volume":"14","author":"M Lowe","year":"2022","unstructured":"Lowe, M., Qin, R., Mao, X.: A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring. Water 14(1384), 1\u201328 (2022)","journal-title":"Water"},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"107308","DOI":"10.1016\/j.compchemeng.2021.107308","volume":"149","author":"AN Matheri","year":"2021","unstructured":"Matheri, A.N., Ntuli, F., Ngila, J.C., Seodigeng, T., Zvinowanda, C.: Performance prediction of trace metals and cod in wastewater treatment using artificial neural network. Comput. Chem. Eng. 149, 107308 (2021)","journal-title":"Comput. Chem. Eng."},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"129268","DOI":"10.1016\/j.chemosphere.2020.129268","volume":"267","author":"SA Naghibi","year":"2021","unstructured":"Naghibi, S.A., Salehi, E., Khajavian, M., Vatanpour, V., Sillanp\u00e4\u00e4, M.: Multivariate data-based optimization of membrane adsorption process for wastewater treatment: multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system. Chemosphere 267, 129268 (2021)","journal-title":"Chemosphere"},{"doi-asserted-by":"crossref","unstructured":"Abdel daiem, M.M., Hatata, A., Said, N.: Modeling and optimization of semi-continuous anaerobic co-digestion of activated sludge and wheat straw using nonlinear autoregressive exogenous neural network and seagull algorithm. Energy 241, 122939 (2022)","key":"20_CR11","DOI":"10.1016\/j.energy.2021.122939"},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"103935","DOI":"10.1016\/j.jwpe.2023.103935","volume":"54","author":"N-B Mih\u00e1ly","year":"2023","unstructured":"Mih\u00e1ly, N.-B., Luca, A.-V., Simon-V\u00e1rhelyi, M., Cristea, V.M.: Improvement of air flowrate distribution in the nitrification reactor of the waste water treatment plant by effluent quality, energy and greenhouse gas emissions optimization via artificial neural networks models. J. Water Process Eng. 54, 103935 (2023)","journal-title":"J. Water Process Eng."},{"key":"20_CR13","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.psep.2021.12.034","volume":"158","author":"Y Yang","year":"2022","unstructured":"Yang, Y., et al.: Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling. Process Saf. Environ. Prot. 158, 515\u2013524 (2022)","journal-title":"Process Saf. Environ. Prot."},{"key":"20_CR14","doi-asserted-by":"publisher","first-page":"107432","DOI":"10.1016\/j.jobe.2023.107432","volume":"77","author":"Y Meng","year":"2023","unstructured":"Meng, Y., Yun, S., Zhao, Z., Guo, J., Li, X., Ye, D.: Short-term electricity load forecasting based on a novel data preprocessing system and data reconstruction strategy. J. Build. Eng. 77, 107432 (2023)","journal-title":"J. Build. Eng."}],"container-title":["Communications in Computer and Information Science","Methods and Applications for Modeling and Simulation of Complex Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7240-1_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T12:09:11Z","timestamp":1697112551000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7240-1_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,13]]},"ISBN":["9789819972395","9789819972401"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7240-1_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,10,13]]},"assertion":[{"value":"13 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AsiaSim","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia Simulation Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Langkawi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asiasim2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.asiasim2023.cmedutm.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}