[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119258-en":3,"doc-seo-119258-105":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},119258,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Robotics and Control - Machine Learning and Control for Phosphorous Removal","This study optimizes energy-efficient control strategies under legal environmental constraints for the Hias wastewater treatment process in Hamar, Norway. Using biofilm carriers in anaerobic and aerobic basins, it follows a sequential workflow: defining energy-efficiency research questions, collecting and preprocessing data, removing outliers, scaling, and selecting relevant features before building dynamic models. Multiple regressors—linear regression, deep neural networks, LSTM, KNN, gradient boosting, random forest, SVR, and MLP—are compared, and two are advanced for integrated control design. Models are trained and tested to regulate air consumption and validate compliance with environmental standards, with future work proposing reinforcement learning and improved analyzer measurement quality.","ACIT5900  \nMASTER THESIS  \nin  \nApplied Computer and Information Technology (ACIT)  \nMay 2024  \nRobotics and Control  \nMachine Learning and Control for Phosphorous  \nRemoval  \nMalik Baqeri  \nDepartment of Mechanical, Electronic and Chemical Engineering (MEK) Faculty of Technology, Art and Design  \nPreface  \nThis project’s inspiration comes from my growing environmental concerns and the complexities we face. My heartfelt thanks go out to everyone involved, including professors and fellow master students, for their invaluable assistance and support. Special thanks to Professor Tiina Marjatta Komulainen for her guidance and Professor Anis Yazidi for his insightful instructions.  \nHuge thanks to the Hias team specially Katrine Marsteng Jansen for their collaboration. My family and friends have been my backbone, and their support has been crucial. Undertaking this master’s thesis, particularly. Nonetheless, I persevered, working diligently to produce a highquality project. This experience has deepened my admiration for professionals in this domain. Mastering data preprocessing, developing accurate machine learning techniques in python programming language, and implementing effective control strategies simulation for nutrient monitoring proved challenging but intriguing. I am thankful for the chance to learn and grow through this endeavor.  \nMalik Baqeri  \nAbstract  \nThis study addresses the challenge of optimizing control strategies for energy efficiency within legal environmental constraints for the Hias process, Hamar, Norway. The Hias process employs biofilm carriers in both anaerobic and aerobic basins to absorb the nutrients entering the wastewater treatment facility The research is centered around a series of sequential steps beginning with the formulation of research questions that focus on enhancing energy efficiency while adhering to environmental regulations. The methodology encompasses comprehensive data collection, preprocessing, outlier removal, scaling, and the selection of relevant features, followed by the development of dynamic models. Linear regression, deep neural networks, long short-term memory, k-nearest neighbors, gradient boosting regression, random forest, support vector regression, and multilayer perceptron prediction capabilities are examined. Among these, two are selected for further development of control strategies. The core of the research involves selecting, training, and testing machine learning models, designing and testing control strategies integrating with machine learning that can efficiently manage air consumption, thus energy use in an operational setting. Control results are evaluated to ensure they meet the stringent criteria set forth by environmental standards. The paper concludes the discussion, and suggestions for further work, highlighting potential applications of reinforcement learning, and enhancement of analyzer measurement quality to refine the control strategy. This approach not only promises to optimize energy consumption but also ensures compliance with environmental regulations, thereby supporting sustainable operational practices.  \nContents  \nPreface i  \nAbstract ii  \nContents iii  \nList of Figures vi  \nList of Tables viii  \n1 Introduction 1  \n1.1 Process Description ................................ 2  \n2 Literature Review 5  \n2.1 Theoretical Background on Machine Learning .................. 6  \n3 Materials and Methodology 7  \n3.1 Hardware and Software .............................. 7  \n3.2 Data Analysis Pipeline .............................. 7  \n3.3 Data Screening .................................. 9  \n3.3.1 Imputation of Missing values ....................... 9  \n3.3.2 Extreme Values ............................. 9  \n3.3.3 Data scaling ................................ 11  \n3.4 Hydraulic Retention Time ............................ 12  \n3.5 Machine Learning Applied in this work ..................... 12  \n3.5.1 Linear Regression ............................ 12  \n3.5.2 Deep N","cbCaip1w6BVsHjMQ","https://ap.wps.com/l/cbCaip1w6BVsHjMQ","pdf",23862742,1,113,"English","en",105,"# 1 Introduction\n## 1.1 Process Description\n# 2 Literature Review\n## 2.1 Theoretical Background on Machine Learning\n# 3 Materials and Methodology\n## 3.1 Hardware and Software\n## 3.2 Data Analysis Pipeline\n## 3.3 Data Screening\n## 3.4 Hydraulic Retention Time\n## 3.5 Machine Learning Applied in this work\n## 3.6 Evaluation of Model Performance\n## 3.7 Control Strategy Development\n# 4 Results and Analysis\n## 4.1 Data Preprocessing\n## 4.2 Model Construction\n## 4.3 Machine Learning Results\n## 4.4 Machine Learning Performance Evaluation\n## 4.5 Dynamic Models Used in Simulation","[{\"question\":\"What process and problem does the thesis focus on?\",\"answer\":\"The thesis targets the Hias wastewater treatment process in Hamar, Norway, focusing on optimizing control strategies to improve energy efficiency while meeting environmental regulations.\"},{\"question\":\"What does the data workflow include before model training?\",\"answer\":\"The workflow includes comprehensive data collection, preprocessing, outlier removal, scaling, and selecting relevant features prior to developing dynamic models.\"},{\"question\":\"Which machine learning approaches are evaluated for predicting and supporting control strategies?\",\"answer\":\"The study evaluates linear regression, deep neural networks, LSTM, k-nearest neighbors, gradient boosting regression, random forest, support vector regression, and multilayer perceptron, then selects two for further control-strategy development.\"}]","Robotics and Control - Machine Learning and Control for Phosphorous Removal | PDF",1785723370,285,{"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},"robotics-and-control-machine-learning-and-control-for-phosphorous-removal","",{"@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/robotics-and-control-machine-learning-and-control-for-phosphorous-removal/119258/",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 process and problem does the thesis focus on?","Question",{"text":75,"@type":76},"The thesis targets the Hias wastewater treatment process in Hamar, Norway, focusing on optimizing control strategies to improve energy efficiency while meeting environmental regulations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the data workflow include before model training?",{"text":80,"@type":76},"The workflow includes comprehensive data collection, preprocessing, outlier removal, scaling, and selecting relevant features prior to developing dynamic models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are evaluated for predicting and supporting control strategies?",{"text":84,"@type":76},"The study evaluates linear regression, deep neural networks, LSTM, k-nearest neighbors, gradient boosting regression, random forest, support vector regression, and multilayer perceptron, then selects two for further control-strategy development.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]