Combination of Anns and Heuristic Algorithms in Modelling and Optimizing of Fenton Processes for Industrial Wastewater Treatment

Combination of Anns and Heuristic Algorithms in Modelling and Optimizing of Fenton Processes for Industrial Wastewater Treatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Combination of Anns and Heuristic Algorithms in Modelling and Optimizing of Fenton Processes for Industrial Wastewater Treatment Hüseyin Cüce, Ozge Cagcag Yolcu, Fulya AYDIN TEMEL This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-382899/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, it is aimed to evaluate COD removal performance of Classical-Fenton and Photo-Fenton Processes from cosmetic wastewater by different prediction models. Besides Response Surface Methodology (RSM), three neural networks were used to more reliably and effectively predict the behavior of dependent variable at different values of relevant parameters. These neural networks; multi-layer perceptron trained by Levenberg-Marquardt (MLP-LM); multi-layer perceptron and single multiplicative neuron model trained by particle swarm optimization algorithm (MLP-PSO; SMN-PSO). H 2 O 2 doses, Fe(II) doses, and H 2 O 2 /Fe(II) rates were independent variables of prediction models to optimize both processes in batch reactors. The generated predictions for whole data set were compared with each other. The prediction performances of models were evaluated by RMSE and MAPE error criteria. Regression analysis was also applied to determine the performance of the best model. The results obtained from all prediction tools showed that the model produces the best predictive results in almost all cases is SMN-PSO model in terms of both criteria. In addition, the genetic algorithm was utilized for SMN-PSO model results to find the optimum values of the study. Thus, without the need to perform many different experiments, the optimum parameter values can be determined to get maximum removal ratios. Environmental Engineering Environmental Policy Fenton process Multilayer Perceptron Single Multiplicative Neuron Model Particle Swarm Optimization Genetic Algorithm RSM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-382899","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":25000502,"identity":"ff31837b-b28e-4908-81f8-b5d5214e9c3a","order_by":0,"name":"Hüseyin Cüce","email":"","orcid":"","institution":"Giresun University: Giresun Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hüseyin","middleName":"","lastName":"Cüce","suffix":""},{"id":25000503,"identity":"5784d5be-b9b6-4d88-bfa8-c880b9e73292","order_by":1,"name":"Ozge Cagcag Yolcu","email":"","orcid":"","institution":"Giresun University: Giresun Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ozge","middleName":"Cagcag","lastName":"Yolcu","suffix":""},{"id":25000504,"identity":"41c1d996-2c64-4795-9157-9efaf92d91bc","order_by":2,"name":"Fulya AYDIN TEMEL","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8042-9998","institution":"Giresun University: Giresun Universitesi","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Fulya","middleName":"AYDIN","lastName":"TEMEL","suffix":""}],"badges":[],"createdAt":"2021-04-01 12:45:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-382899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-382899/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8917658,"identity":"05878182-a8d6-45b3-b01e-e494c59e88ac","added_by":"auto","created_at":"2021-05-07 14:44:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":517349,"visible":true,"origin":"","legend":"A schematic representation of Photo-Fenton Process ","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1/d2dcd22f06926b3e8beb6b44.png"},{"id":8917425,"identity":"a01754a9-c698-4195-a620-25aeb8326708","added_by":"auto","created_at":"2021-05-07 14:41:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230718,"visible":true,"origin":"","legend":"A scheme of the combination of ANNs and heuristic algorithms","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1/e20fb0dd0a03e1e0a6cc3928.png"},{"id":8917387,"identity":"7513437a-9c60-49cc-a0ba-9c2355aa73cc","added_by":"auto","created_at":"2021-05-07 14:38:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59635,"visible":true,"origin":"","legend":"An illustration of MLP structure / 2-k-1 Architecture (a) and SMN structure (b) ","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1/68deee48ec2e2487e5c8d0a6.png"},{"id":8917390,"identity":"d8ad85a0-fbdb-4dbc-9367-ce16cc96eb68","added_by":"auto","created_at":"2021-05-07 14:38:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50536,"visible":true,"origin":"","legend":"The scatter gram of observed and predicted removals (a: Exp.1; b: Exp. 2; c: Exp. 3; d: Exp. 4; e: Exp. 5; f: Exp. 6; g: Exp. 7; h: Exp. 8)","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1/e22c0dcc48d5cb9b94531a6b.png"},{"id":8917427,"identity":"af0703cc-d30a-48c3-8af9-7cb1d1d472b2","added_by":"auto","created_at":"2021-05-07 14:41:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57595,"visible":true,"origin":"","legend":"Comparing NN-based models in terms of RMSE and MAPE ","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1/12ae89ca7a195b2349b026b9.png"},{"id":8917428,"identity":"51168e28-464c-40b6-9c77-7ad188d1052f","added_by":"auto","created_at":"2021-05-07 14:41:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31738,"visible":true,"origin":"","legend":"Comparing all models in terms of RMSE and MAPE ","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1/d4060d6d35532c5ca8075956.png"},{"id":13629415,"identity":"54fadbf6-5e8b-4fde-8262-20da2c8e11a6","added_by":"auto","created_at":"2021-09-17 08:06:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207117,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptESPR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1_covered.pdf"},{"id":10169031,"identity":"e3d5b2ec-7a3e-4b9a-9a20-d124654866c2","added_by":"auto","created_at":"2021-06-09 16:31:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1203549,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptESPR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1_covered.pdf"},{"id":8917875,"identity":"6b211039-7423-4e9f-b2fa-591cadfe1f81","added_by":"auto","created_at":"2021-05-07 14:47:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1733855,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptESPR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-382899/v1_stamped.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCombination of Anns and Heuristic Algorithms in Modelling and Optimizing of Fenton Processes for Industrial Wastewater Treatment\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-382899/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fenton process, Multilayer Perceptron, Single Multiplicative Neuron Model, Particle Swarm Optimization, Genetic Algorithm, RSM","lastPublishedDoi":"10.21203/rs.3.rs-382899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-382899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, it is aimed to evaluate COD removal performance of Classical-Fenton and Photo-Fenton Processes from cosmetic wastewater by different prediction models. Besides Response Surface Methodology (RSM), three neural networks were used to more reliably and effectively predict the behavior of dependent variable at different values of relevant parameters. These neural networks; multi-layer perceptron trained by Levenberg-Marquardt (MLP-LM); multi-layer perceptron and single multiplicative neuron model trained by particle swarm optimization algorithm (MLP-PSO; SMN-PSO). H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e doses, Fe(II) doses, and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e/Fe(II) rates were independent variables of prediction models to optimize both processes in batch reactors. The generated predictions for whole data set were compared with each other. The prediction performances of models were evaluated by RMSE and MAPE error criteria. Regression analysis was also applied to determine the performance of the best model. The results obtained from all prediction tools showed that the model produces the best predictive results in almost all cases is SMN-PSO model in terms of both criteria. In addition, the genetic algorithm was utilized for SMN-PSO model results to find the optimum values of the study. Thus, without the need to perform many different experiments, the optimum parameter values can be determined to get maximum removal ratios.\u003c/p\u003e","manuscriptTitle":"Combination of Anns and Heuristic Algorithms in Modelling and Optimizing of Fenton Processes for Industrial Wastewater Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-07 14:38:43","doi":"10.21203/rs.3.rs-382899/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"baecb148-54a6-4b3b-b993-45742f6030c0","owner":[],"postedDate":"May 7th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4122793,"name":"Environmental Engineering"},{"id":4122794,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2021-06-09T16:29:56+00:00","versionOfRecord":[],"versionCreatedAt":"2021-05-07 14:38:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-382899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-382899","identity":"rs-382899","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}