Advanced Machine Learning Techniques for Corrosion Rate Estimation and Prediction in Industrial Cooling Water Pipelines

Author:

Ruiz Desiree1ORCID,Casas Abraham1ORCID,Escobar Cesar Adolfo1ORCID,Perez Alejandro1,Gonzalez Veronica1

Affiliation:

1. Centro Tecnológico de Componentes-CTC, Scientific and Technological Park of Cantabria (PCTCAN), 39011 Santander, Spain

Abstract

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset.

Funder

Ministry of Economy, Industry and Competitiveness, Spain

Publisher

MDPI AG

Reference52 articles.

1. I.(Spain) National Statistics Institute (2024, May 22). Contabilidad Nacional Anual de España: Principales agregados años 2020–2022. Available online: https://www.ine.es/prensa/cna_pa_2022.pdf.

2. Koch, G. (2017). Cost of corrosion. Trends in Oil and Gas Corrosion Research and Technologies, Elsevier.

3. Schmitt, G., Schütze, M., Hays, G.F., and Burns, W. (2009). Global Needs for Knowledge Dissemination, Research, and Development in Materials Deterioration and Corrosion Control, World Corrosion Organization.

4. Sastri, V.S., Ghali, E., and Elboujdaini, M. (2007). Corrosion Prevention and Protection, John Wiley & Sons, Ltd.

5. Chen, L., Yang, J., and Lu, X. (2021, January 29–31). Research on Time Series Prediction Model for the Trend of Corrosion Rate. Proceedings of the 2021 IEEE Asia Conference on Information Engineering (ACIE), Sanya, China.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3