[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127143-en":3,"doc-seo-127143-105":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},127143,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Transformative Insights into Corrosion Inhibition - A Machine Learning Journey from Prediction to Web-Based Application","This study explores and evaluates machine learning models for analyzing expired pharmaceutical data to assess their potential as corrosion inhibitors. The full modeling workflow is deployed through a Streamlit-assisted, web-based platform, enabling practical use by non-programmers without requiring users to retrain models. Models are trained offline to ensure reliable prediction performance and deliver a ready-to-use interface for customization. Among the implemented approaches, XGB achieves the strongest results with an R2-score of 0.99999999, supporting broader accessibility to ML-enabled inhibitor experimentation.","Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application  \nDzaki Asari Surya Putra1, Nicholaus Verdhy Putranto1, Nibras BahyArdyansyah1, Gustina Alfa  \nTrisnapradika1,2, Muhamad Akrom1,2*  \n1 Study Program in Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro,  \nSemarang, 50131, Indonesia  \n2Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro,  \nSemarang, 50131, Indonesia  \n*[Correspondence: m.akrom@dsn.dinus.ac.id](Correspondence: m.akrom@dsn.dinus.ac.id)  \nAbstract  \nThis study focuses on the exploration and evaluation of machine learning (ML) models to analyze expired pharmaceutical data for their potential use as corrosion inhibitors. Additionally, the entire modeling process is integrated into a user-friendly platform through a Streamlit service-assisted corrosion inhibitor website, facilitating broader accessibility and practical application. The models are trained offline to ensure accurate performance, eliminating the need for users to retrain the models themselves. This approach simplifies the user experience by offering a ready-to-use prediction service directly on the website platform. Among the various ML models implemented, XGB demonstrated the highest performance with an R2-score of 0.99999999. Given that many chemists are not familiar with informatics coding, the researchers developed a Streamlit-based website that includes tools to customize the models. The end product of this work is a corrosion inhibitor experimentation tool that eliminates the need for users to code, making advanced ML techniques accessible to a broader audience within the chemistry community.  \nKeywords: corrosion, inhibitor, machine learning, web, streamlit  \n1. Introduction  \nCorrosion represents the degradation of metal, characterized by chemical or electrochemical processes occurring between a metal and its surrounding environment, leading to a deterioration in metal quality [1], [2], [3] . Its significance in the environmental context is paramount, and corrosion manifestations can vary depending on the environmental conditions to which a metal is exposed, making consequences challenging to discern. The occurrence of corrosion is influenced by factors such as metal reactivity, degradation, air presence, humidity levels, gas coefficients (e.g., sulfur dioxide and carbon dioxide), and the existence of electrolytes [3] . Corrosion results in losses across diverse sectors, impacting industrial assets, buildings, and various infrastructures [4] . Consequently, the utilization of corrosion inhibitors is imperative to address and mitigate existing corrosion challenges effectively [5],[6].  \nCorrosion inhibitors play a pivotal practical role, frequently employed to diminish metal losses in the production process and mitigate the potential for material failure [7],[8]. The occurrence of these two events can result in abrupt disruptions in  \nindustrial operations, ultimately incurring additional costs. Research indicates that inhibitors, including drugs, play a crucial role in diverse environments and at various concentration levels, exhibiting effectiveness against a range of metals such as zinc, copper, mild steel, aluminum, bronze, and carbon steel. These inhibitors demonstrate the capability to create a protective layer on material surfaces, preventing corrosive reactions between the material and the environment and, consequently, mitigating or reducing corrosion levels in specific materials [9].  \nConventional methods for determining the effectiveness of corrosion inhibitors are slow and costly due to the need for chemical experts, materials, and equipment. These methods typically involve experimental techniques such as weight loss assessment, potentiodynamic polarization, and electrochemical impedance spectroscopy [10] . The economic toll of addressing corrosion-related issues amounts to approximately ","cbCaiiHLghYuZiaG","https://ap.wps.com/l/cbCaiiHLghYuZiaG","pdf",1016217,1,9,"English","en",105,"# Introduction\n## Background on corrosion and corrosion inhibitors\n## Limitations of conventional inhibitor evaluation methods\n## Motivation for machine learning approaches\n## Related work on ML models for corrosion inhibition efficiency\n# Method and application goal","[{\"question\":\"What problem does the research address?\",\"answer\":\"The research addresses the challenge of evaluating corrosion inhibitors efficiently by using machine learning on expired pharmaceutical data to predict corrosion inhibition efficiency (CIE).\"},{\"question\":\"How does the proposed solution make the ML workflow easier for users?\",\"answer\":\"The solution integrates trained models into a Streamlit-based website so users can obtain predictions and customize model tools without retraining or coding the models themselves.\"},{\"question\":\"Which machine learning model shows the best performance?\",\"answer\":\"XGB shows the highest performance, achieving an R2-score of 0.99999999 in the implemented experiments.\"}]","Transformative Insights into Corrosion Inhibition - A Machine Learning Journey from Prediction to Web-Based Application | PDF",1785937157,23,{"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},"transformative-insights-into-corrosion-inhibition-a-machine-learning-journey-from-prediction-to-web-based-application","",{"@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/transformative-insights-into-corrosion-inhibition-a-machine-learning-journey-from-prediction-to-web-based-application/127143/",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","2026-08-05",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 research address?","Question",{"text":75,"@type":76},"The research addresses the challenge of evaluating corrosion inhibitors efficiently by using machine learning on expired pharmaceutical data to predict corrosion inhibition efficiency (CIE).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed solution make the ML workflow easier for users?",{"text":80,"@type":76},"The solution integrates trained models into a Streamlit-based website so users can obtain predictions and customize model tools without retraining or coding the models themselves.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model shows the best performance?",{"text":84,"@type":76},"XGB shows the highest performance, achieving an R2-score of 0.99999999 in the implemented experiments.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]