{"title":"Artificial neural network modelling to predict the efficiency of aluminium sacrificial anode","authors":"Amir Rezaei","doi":"10.1080/1478422X.2023.2252258","DOIUrl":null,"url":null,"abstract":"ABSTRACT Study explores the potential of a deep learning-based approach for predicting the current efficiency of aluminium sacrificial anodes in marine environments. The model takes into account various input variables, including the chemical composition of the sacrificial anode, pH, dissolved oxygen (DO), temperature, pressure, cathode electrode, current density, and the ratio of the surface area of the cathode to anode, with the anode current efficiency serving as the output variable. Utilising artificial neural networks in this study shows a mean absolute percentage error of 6.4% and 7.8% for the training and validation for predicting the current efficiency. The proposed model shows promising potential to predict the current efficiency of aluminium sacrificial anodes and improve the design of cathodic protection systems based on aluminium sacrificial anodes.","PeriodicalId":10711,"journal":{"name":"Corrosion Engineering, Science and Technology","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Corrosion Engineering, Science and Technology","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1080/1478422X.2023.2252258","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT Study explores the potential of a deep learning-based approach for predicting the current efficiency of aluminium sacrificial anodes in marine environments. The model takes into account various input variables, including the chemical composition of the sacrificial anode, pH, dissolved oxygen (DO), temperature, pressure, cathode electrode, current density, and the ratio of the surface area of the cathode to anode, with the anode current efficiency serving as the output variable. Utilising artificial neural networks in this study shows a mean absolute percentage error of 6.4% and 7.8% for the training and validation for predicting the current efficiency. The proposed model shows promising potential to predict the current efficiency of aluminium sacrificial anodes and improve the design of cathodic protection systems based on aluminium sacrificial anodes.
期刊介绍:
Corrosion Engineering, Science and Technology provides broad international coverage of research and practice in corrosion processes and corrosion control. Peer-reviewed contributions address all aspects of corrosion engineering and corrosion science; there is strong emphasis on effective design and materials selection to combat corrosion and the journal carries failure case studies to further knowledge in these areas.