[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123575-en":3,"doc-seo-123575-105":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},123575,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Assessment of Fouling in Plate Heat Exchangers with Machine Learning Algorithms - Master’s Thesis","Fouling is treated as the undesired particle accumulation on heat transfer surfaces that reduces exchanger performance by forming an insulation-like fouling layer. The thesis evaluates machine learning approaches to classify and predict the fouling state of plate heat exchangers (PHEs) used in combi-boilers, and develops a 1-D model to quantify fouling impacts on heat transfer and energy consumption. Experimental data generated via an artificial fouling method supports both model calibration and ML training, while validation indicates the numerical model can reproduce experimental outputs with under 2% error. Model-based power penalties are computed and ML results show k-nearest neighbors provides the best class prediction using overall heat transfer coefficient values.","Assessment of Fouling in Plate Heat Exchangers with Machine Learning  \nAlgorithms  \nSubmitted to the Graduate School of Natural and Applied Sciences in partial fulfillment of the requirements for the degree of  \nMaster of Science  \nin Mechanical Engineering  \nby  \nCeren Vatansever  \nORCID 0000-0002-2287-9699  \nOctober 2022  \nThis is to certify that we have read the thesis Assessment of Fouling in Plate Heat Exchangers with Machine Learning Algorithms submitted by Ceren Vatansever, and it has been judged to be successful, in scope and in quality, at the defense exam and accepted by our jury as a MASTER’S / DOCTORAL THESIS.  \nAPPROVED BY:  \nAdvisor: Assoc. Prof. Dr. Ziya Haktan Karadeniz  \nİzmir Kâtip Çelebi University  \nCommittee Members:  \nAssoc. Prof. Dr. Sercan Acarer  \nİzmir Kâtip Çelebi University  \nAssoc. Prof. Dr. Alpaslan Turgut  \nDokuz Eylül University  \nDate of Defense: October 6, 2022  \nDeclaration of Authorship  \nI, Ceren Vatansever, declare that this thesis titled Assessment of Fouling in Plate Heat Exchangers with Machine Learning Algorithms and the work presented in it are my own. I confirm that:  \n• This work was done wholly or mainly while in candidature for the Master’s / Doctoral degree at this university.  \n• Where any part of this thesis has previously been submitted for a degree or anyother qualification at this university or any other institution, this has been clearly stated.  \n• Where I have consulted the published work of others, this is always clearly attributed.  \n• Where I have quoted from the work of others, the source is always given. This thesis is entirely my own work, with the exception of such quotations.  \n• I have acknowledged all major sources of assistance.  \n• Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.  \nDate: 1.10.2022  \nAssessment of Fouling in Plate Heat Exchangers with Machine Learning Algorithms  \nAbstract  \nFouling is the accumulation of undesired particles on heat transfer surfaces which affects the heat transfer performance of a heat exchanger negatively. The accumulation of these particles prevents heat from being transformed through the heat exchangers by generating a fouling layer-like insulation. The main aim of the thesis is to investigate the machine learning algorithms to classify and predict the fouling status of PHE used in combi-boilers, to generate the background of the predictive maintenance, besides investigating the fouling effect on PHEs in terms of heat transfer and energy consumption by using a 1-D model.  \nThe required data to train the machine learning algorithms is acquired experimentally by using an artificially generated method for evaluating the fouling behavior. The effect of fouling on PHE performance is assumed as similar to the performance loss that would be occurred if the PHE that is already used in the combi-boiler, would be replaced with a PHE that has fewer plate numbers. The experiment results show that the expected trends of output temperatures and pressure drop values of both channels are seen.  \nThe overall heat transfer coefficient and fouling resistance coefficient are calculated as the performance values of the tested PHEs. As expected, the overall heat transfer coefficients are resulted in decreasing while the fouling resistance coefficient is increasing.  \nThe 1-D numerical model is generated by using Runge Kutta 4th order ordinary differential equation solving method. The differential equations are created based thermal resistance method for both channels to evaluate the temperature distributions  \nby using the experimental data. The results show that with less than 2% error the model is concluded to receive the correct outputs with the experimental outputs.  \nThe additional required power to reach the setpoint of DHW defined by the customer is calculated at maximum fouling by using the model results. The results show that combi","cbCaiuanq76CCFa0","https://ap.wps.com/l/cbCaiuanq76CCFa0","pdf",5378328,1,111,"English","en",105,"# Declaration of Authorship\n# Abstract\n## Fouling definition and thesis objectives\n## Experimental data generation and performance loss assumption\n## 1-D numerical modeling and validation\n## Energy impact calculation under maximum fouling\n## Machine learning pipeline and model comparison","[{\"question\":\"What problem does the thesis address in plate heat exchangers?\",\"answer\":\"The thesis focuses on fouling, the accumulation of undesired particles on heat transfer surfaces, which lowers heat transfer performance by adding an insulation-like layer.\"},{\"question\":\"How is the machine learning task defined in the thesis?\",\"answer\":\"Machine learning algorithms are used to classify and predict the fouling status of PHEs used in combi-boilers, supporting predictive maintenance.\"},{\"question\":\"What is the accuracy of the 1-D numerical model compared with experiments?\",\"answer\":\"The 1-D numerical model reproduces experimental outputs with less than 2% error, indicating correct prediction of key temperature and pressure-drop trends.\"}]","Assessment of Fouling in Plate Heat Exchangers with Machine Learning Algorithms - 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