[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118830-en":3,"doc-seo-118830-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118830,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Prediction of nitrogen purification in wastewater with Machine learning","Wastewater treatment plants are essential to prevent environmental pollution. To comply with newly proposed stricter EU wastewater regulations, many facilities would need costly upgrades that may also increase land use and financial burden on taxpayers. This thesis evaluates whether machine learning can predict nitrate remaining in wastewater after denitrification. Historical data from two denitrification processes at one plant are modeled using SARIMAX and two ML approaches, LSTM and XGBoost, comparing prediction accuracy via MSE, RMSE, and MAE.","Master’s Thesis 2023 30 ECTS  \nFaculty of Science and Technology  \nPrediction of nitrogen purification in wastewater with Machine learning  \nSebastian Tobias Becker  \nMSc Data Science  \nAbstract  \nWastewater treatment plants are necessary for avoiding environmental pollution by humans. Last year the European Commission proposed a new directive with stricter requirements for wastewater treatment plants [1] . To meet the proposed regulatory changes regarding the allowed amount of pollution, many wastewater treatment plants need expensive facility upgrades. These upgrades may increase land use. Additionally, the taxpayers will most likely have to pay for the expenses related to meet the new requirements for the wastewater treatment plants. One possible solution for reducing the cost and land use could be to optimize the processes used today with new technology. This study will investigate if it is possible to use machine learning to predict the amount of nitrate contained in the wastewater after denitrification. For this purpose, historical data from two different denitrification processes from one wastewater treatment plant is utilized. The first process dosed methanol based on measurements of nitrate, oxygen, and flow before denitrification, while the second process dosed methanol based on measurements of nitrate, oxygen, and flow before denitrification and previous nitrate out measurements. The data were collected between 30 .11.2022 and 05 .01.2023. One statistical approach and two machine learning models were tested for predicting the amount of nitrate contained in the wastewater after denitrification. The statistical method is a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) and the machine learning approaches are the long short term memory (LSTM) and extreme gradient boosting (XGBoost) algorithms. For the first process all models showed similar results with SARIMAX as the best model with an MSE, RMSE and MAE of 0 . 15, 0 .39 and 0 .29 respectively. For the second process the SARIMAX model outperformed the LSTM and XGBoost with MSE,RMSE and MAE of 2.09, 1 .45 and 1 .24 respectively. Our research show that it is significantly easier to get good performing models for process one than two. We are presenting some aspects which should be further investigated to obtain a solution that is ready to be put into use.  \nSammendrag  \nRenseanlegg er nødvendig for ˚a unng˚a miljøforurensninger fra mennesker. I fjor foreslo EUkommisjonen et nytt direktiv med strengere krav til renseanlegg [1] . For ˚a møte de foresl˚attereguleringene ang˚aende tillatt mengde forurensning, trengs kostbare oppgraderinger av mange anlegg. Disse oppgraderingene kan føre til økt arealbruk. I tillegg vil mest sannsynlig skattebetalerne m˚atte betale for utgiftene knyttet til ˚a oppfylle de nye kravene. En mulig løsning for ˚aredusere kostnadene og arealbruk kan være˚a optimalisere dagens prosesser med ny teknologi. Idenne oppgaven vil det undersøkes om det er mulig˚a bruke maskinlæring til˚a predikere mengden nitrat som er igjen i avløpsvannet etter denitrifikasjon. For dette form˚alet er historiskedata fra to ulike denitrifikasjonsprosesser fra et renseanlegg benyttet. I den første prosessen er metanoldoseringen basert p˚a m˚alinger av nitrat, oksygen og strømning av avløpsvann førdenitrifikasjon. I den andre prosessen doseres metanol basert p˚a m˚alinger av nitrat, oksygen, strømning av avløpsvann samt tidligere nitrat ut m˚alinger. Dataen ble samlet inn mellom 30.11.2022 og 05 .01.2023. En statistisk tilnærming og to maskinlæringsmodeller ble testet for˚apredikere mengden nitrat som er i avløpsvannet etter denitrifikasjon. Den statistiske metoden som ble brukt er en SARIMAX modell og maskinlæringsmetodene er LSTM og XGBoost. Forden første prosessen viste alle modellene lignende resultater, hvor SARIMAX var den bestemed en MSE, RMSE og MAE p˚a henholdsvis 0 . 15, 0 .39 og 0 .29. For den andre prosessen var SARIMAX den kla","cbCait9hGBOYUgVg","https://ap.wps.com/l/cbCait9hGBOYUgVg","pdf",3349676,1,55,"English","en",105,"# Introduction\n## Context\n## Thesis objective and motivation\n# Theory\n## Description of the wastewater treatment plant\n## Machine Learning\n## Artificial neural network","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses how to predict nitrate levels in wastewater after denitrification, aiming to support cost- and land-saving optimization of treatment processes under stricter regulations.\"},{\"question\":\"Which models are used for prediction?\",\"answer\":\"The study tests one statistical method, SARIMAX, and two machine learning models: LSTM and XGBoost.\"},{\"question\":\"How do the results compare between the two denitrification processes?\",\"answer\":\"For the first process, all models perform similarly, with SARIMAX achieving the best metrics. For the second process, SARIMAX clearly outperforms LSTM and XGBoost, indicating easier model building for process one than two.\"}]","Prediction of nitrogen purification in wastewater with Machine learning | PDF",1785720501,139,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-nitrogen-purification-in-wastewater-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-nitrogen-purification-in-wastewater-with-machine-learning/118830/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address?","Question",{"text":75,"@type":76},"The thesis addresses how to predict nitrate levels in wastewater after denitrification, aiming to support cost- and land-saving optimization of treatment processes under stricter regulations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models are used for prediction?",{"text":80,"@type":76},"The study tests one statistical method, SARIMAX, and two machine learning models: LSTM and XGBoost.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results compare between the two denitrification processes?",{"text":84,"@type":76},"For the first process, all models perform similarly, with SARIMAX achieving the best metrics. 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