[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127050-en":3,"doc-seo-127050-105":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},127050,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds","Corrosion significantly increases operational costs and reduces equipment efficiency across many industrial sectors, motivating the search for efficient organic corrosion inhibitors. This study estimates corrosion inhibition efficiency of pyridazine-derived compounds using an ensemble learning framework with a stacking approach. Diverse pyridazine molecular characteristics are used as input features, and multiple boosting models are evaluated to select the best configuration. The stacking model combining XGB, LGBM, and CatBoost with a Random Forest meta-model achieves the lowest RMSE (0.055). Results indicate machine learning can predict inhibitor performance faster and more economically than conventional experiments.","International Journal of Advances in Data and Information Systems  \nVol. 5, No. 2, October 2024, pp. 198~215  \nISSN: 2721-3056, DOI: 10.59395/ijadis.v5i2.1346 r 198  \nEnsemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine  \nCompounds  \nNoval Ariyanto1, Harun Al Azies2, Muhamad Akrom2  \n1Study Program in Informatics Engineering, Faculty of Computer Science, Dian Nuswantoro University, Indonesia 2Research Center for Quantum Computing and Materials Informatics, Faculty of Computer Science, Dian Nuswantoro  \nUniversity, Indonesia  \nArticle history:  \nReceived Oct 10, 2024 Revised Oct 19, 2024 Accepted Oct 30, 2024  \nKeywords:  \nCorrosion Pyridazine Machine Learning Stacking Ensemble Regression  \nCorresponding Author:  \nCorrosion is a major challenge affecting various industrial sectors, leading to increased operational costs and decreased equipment efficiency. The use of organic corrosion inhibitors is one of the promising solutions. This study applies an ensemble algorithm with a stacking method to estimate pyridazine-derived compounds corrosion inhibition efficiency. This study utilized various molecular characteristics of pyridazine compounds as inputs to predict inhibition efficiency values. After evaluating several boosting models, the stacking technique was chosen as it showed the best results. Stacking Model 6, which combines XGB, LGBM, and CatBoost as the base model with Random Forest as the meta-model, produced the most accurate prediction with an RMSE of 0.055. These findings indicate that machine learning approaches can effectively and efficiently predict corrosion inhibitor performance. This method offers a faster and more economical alternative to conventional experimental methods.  \nThis is an open access article under the CC BY-SA license.  \nMuhamad Akrom,  \nResearch Center for Quantum Computing and Materials Informatics  \nFaculty of Computer Science, Dian Nuswantoro University,  \n207 Imam Bonjol Street, Pendrikan Kidul, Semarang City, Central Java 50131, Indonesia. [Email: m.akrom@dsn.dinus.ac.id](Email: m.akrom@dsn.dinus.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCorrosion is a serious problem many industries face, leading to increased production and maintenance costs and decreased equipment efficiency [1] . Factors such as water, moisture, acidic materials, oil, and high-acidity gases are corrosion triggers [2] . Corrosion can lead to decreased metal thickness, potentially serious structural damage, stress corrosion cracking, decreased mechanical strength, and even sudden material failure [3] . The impact of corrosion is significant on economic and operational costs [4] .  \nThe use of inhibitors, especially those based on organic compounds, is becoming an increasingly popular solution due to their effectiveness and environmental friendliness. Heterocyclic derivatives such as benzotriazoles have demonstrated promising protective capabilities against industrial metals, particularly copper and its streams [5] . Nonetheless, largescale implementation of these compounds still faces several obstacles, including limited thermal resistance and economic considerations related to the manufacturing process [6] . Despite these challenges, the development trend of organic inhibitors continues, driven by their advantages in terms of environmental compatibility and corrosion inhibition efficiency (IE) . Organic compounds  \ncan form a protective film on metal surfaces, preventing direct contact with corrosive agents. Pyridazine-derived compounds show great potential as corrosion inhibitors [7] .  \nTo address the complexity of estimating corrosion inhibitor effectiveness, implementing advanced machine learning methods, specifically stacking techniques offers the potential to produce more comprehensive and accurate solutions. This research focuses on stacking boosting machine learning models to provide more accurate prediction results than single models in the context of","cbCaio044NOIPill","https://ap.wps.com/l/cbCaio044NOIPill","pdf",1040191,1,18,"English","en",105,"# INTRODUCTION\n## Background on corrosion and inhibitors\n## Machine learning and stacking ensemble approach\n# Article Info ABSTRACT\n## Modeling strategy and evaluation results","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses corrosion in industrial settings, which raises production and maintenance costs and reduces equipment efficiency.\"},{\"question\":\"How does the proposed method predict corrosion inhibitor performance?\",\"answer\":\"It uses molecular characteristics of pyridazine-derived compounds as features and applies an ensemble stacking technique to estimate inhibition efficiency values.\"},{\"question\":\"Which model configuration performed best and what was its accuracy?\",\"answer\":\"The best configuration is Stacking Model 6, combining XGB, LGBM, and CatBoost as base models with Random Forest as the meta-model, achieving RMSE = 0.055.\"}]","Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds | 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problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses corrosion in industrial settings, which raises production and maintenance costs and reduces equipment efficiency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method predict corrosion inhibitor performance?",{"text":80,"@type":76},"It uses molecular characteristics of pyridazine-derived compounds as features and applies an ensemble stacking technique to estimate inhibition efficiency values.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model configuration performed best and what was its accuracy?",{"text":84,"@type":76},"The best configuration is Stacking Model 6, combining XGB, LGBM, and CatBoost as base models with Random Forest as the meta-model, achieving RMSE = 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