Abayomi O. Bankole , Rodrigo Moruzzi , Rogério G. Negri , Cassio M. Oishi , Afolashade R. Bankole , Abraham O. James
{"title":"MI-NiDIA：水处理中絮凝动力学和絮凝体演变建模的可扩展框架","authors":"Abayomi O. Bankole , Rodrigo Moruzzi , Rogério G. Negri , Cassio M. Oishi , Afolashade R. Bankole , Abraham O. James","doi":"10.1016/j.simpa.2024.100662","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents a scalable framework for modeling floc evolution and flocculation kinetics in water treatment. Unlike the existing methods that subjects Non-intrusive Dynamic Image Analysis (NiDIA) data to complex mathematical concepts, the proposed software devised a scaling concept for NiDIA data and designed an effective algorithm with the capability to predict varying floc lengths and the underlying kinetics under a broad flocculation conditions (<span><math><mrow><mtext>G</mtext><mi>f</mi></mrow></math></span> and <span><math><mrow><mtext>T</mtext><mi>f</mi></mrow></math></span>). Technically, the designed machine-intelligence framework (MI-NiDIA) involves data preprocessing, automatic parameter selection, validation and prediction of floc length evolution with metrics. For instance, MI-NiDIA-MLP recorded <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.95–1.0 for varying floc length at <span><math><mrow><mtext>G</mtext><mi>f</mi><mspace></mspace><mn>60</mn><mspace></mspace><msup><mrow><mi>s</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000502/pdfft?md5=6a51bd0a25608cc2c5543ea48ccd7c45&pid=1-s2.0-S2665963824000502-main.pdf","citationCount":"0","resultStr":"{\"title\":\"MI-NiDIA: A scalable framework for modeling flocculation kinetics and floc evolution in water treatment\",\"authors\":\"Abayomi O. Bankole , Rodrigo Moruzzi , Rogério G. Negri , Cassio M. Oishi , Afolashade R. Bankole , Abraham O. James\",\"doi\":\"10.1016/j.simpa.2024.100662\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper presents a scalable framework for modeling floc evolution and flocculation kinetics in water treatment. Unlike the existing methods that subjects Non-intrusive Dynamic Image Analysis (NiDIA) data to complex mathematical concepts, the proposed software devised a scaling concept for NiDIA data and designed an effective algorithm with the capability to predict varying floc lengths and the underlying kinetics under a broad flocculation conditions (<span><math><mrow><mtext>G</mtext><mi>f</mi></mrow></math></span> and <span><math><mrow><mtext>T</mtext><mi>f</mi></mrow></math></span>). Technically, the designed machine-intelligence framework (MI-NiDIA) involves data preprocessing, automatic parameter selection, validation and prediction of floc length evolution with metrics. For instance, MI-NiDIA-MLP recorded <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.95–1.0 for varying floc length at <span><math><mrow><mtext>G</mtext><mi>f</mi><mspace></mspace><mn>60</mn><mspace></mspace><msup><mrow><mi>s</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>.</p></div>\",\"PeriodicalId\":29771,\"journal\":{\"name\":\"Software Impacts\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2665963824000502/pdfft?md5=6a51bd0a25608cc2c5543ea48ccd7c45&pid=1-s2.0-S2665963824000502-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Impacts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665963824000502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665963824000502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
MI-NiDIA: A scalable framework for modeling flocculation kinetics and floc evolution in water treatment
This paper presents a scalable framework for modeling floc evolution and flocculation kinetics in water treatment. Unlike the existing methods that subjects Non-intrusive Dynamic Image Analysis (NiDIA) data to complex mathematical concepts, the proposed software devised a scaling concept for NiDIA data and designed an effective algorithm with the capability to predict varying floc lengths and the underlying kinetics under a broad flocculation conditions ( and ). Technically, the designed machine-intelligence framework (MI-NiDIA) involves data preprocessing, automatic parameter selection, validation and prediction of floc length evolution with metrics. For instance, MI-NiDIA-MLP recorded of 0.95–1.0 for varying floc length at .
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