Machine Learning for Metallic Corrosion Modeling PDF Download
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Author: Kiran
Publisher: Tredition Gmbh
ISBN: 9783384280343
Category : Computers
Languages : en
Pages : 0
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Book Description
Metal corrosion, from rusty cars to crumbling bridges, costs billions. Enter machine learning! This powerful tool analyzes vast amounts of data to predict and prevent corrosion. By simulating how metals interact with their environment, scientists can design better materials and protective coatings. It's a computational exploration to outsmart rust and save our infrastructure!
Author: Kiran
Publisher: Tredition Gmbh
ISBN: 9783384280343
Category : Computers
Languages : en
Pages : 0
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Book Description
Metal corrosion, from rusty cars to crumbling bridges, costs billions. Enter machine learning! This powerful tool analyzes vast amounts of data to predict and prevent corrosion. By simulating how metals interact with their environment, scientists can design better materials and protective coatings. It's a computational exploration to outsmart rust and save our infrastructure!
Author: Reza Javaherdashti
Publisher: Springer Nature
ISBN: 3031603583
Category :
Languages : en
Pages : 239
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Book Description
Author: Reza Javaherdashti
Publisher: Springer
ISBN: 9783031603570
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This book is devoted to explaining advanced modeling practices of corrosion management. The eleven expert-authored chapters cover various aspects of corrosion management, from the basics of corrosion and its underlying definitions and concepts to the use of specific methods such as fuzzy logic or TRIZ (Russian: Theory of Inventive Problem Solving) for modeling specific corrosion management practices or assets like pipelines. It features modeling of various corrosion processes and reactions via numerical analysis, machine learning, fuzzy calculus, and fuzzy logic. Each chapter is written by an expert in the field with significant experience, ensuring that the content is up-to-date and of the highest quality. This book is an essential resource for professionals in the industry who seek to enhance their understanding of corrosion and its management through state-of-the-art modeling methods.
Author: El Kacimi, Younes
Publisher: IGI Global
ISBN: 1668476908
Category : Technology & Engineering
Languages : en
Pages : 706
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Book Description
The climate change crisis presents a multi-dimensional challenge to the development of the built environment. With finite global resources and increasingly unpredictable climate patterns, the need to improve our understanding of sustainable practices and materials for construction has never been more pressing. The Handbook of Research on Corrosion Sciences and Engineering aims to shed light on the recent developments in the usage of sustainable materials to protect metallic materials against corrosion and provides emerging research exploring the theoretical and practical aspects of corrosion engineering science and technology. Covering key topics such as machine learning, smart coating, sustainability, and artificial intelligence, this major reference work is ideal for construction workers, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.
Author: M. Z. Naser
Publisher: John Wiley & Sons
ISBN: 1119897602
Category : Technology & Engineering
Languages : en
Pages : 610
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Book Description
Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain. Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality, and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with. Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on: The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Details explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure.
Author: Stefano Trasatti
Publisher: Trans Tech Publications Ltd
ISBN: 3038133930
Category : Technology & Engineering
Languages : en
Pages : 158
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Book Description
Volume is indexed by Thomson Reuters CPCI-S (WoS). The editors of this special volume made every effort to invite all of those corrosion specialists working in the field of lightweight alloys and, specifically, their modeling. Their expertise provided a basis upon which to discuss corrosion problems and solutions for Military and Aerospace Systems and Facilities; thus laying a solid foundation for the tackling of yet-unsolved issues. The use of lightweight metals and composites to replace heavy structural materials for military hardware and weapons systems (ships, aircraft, ground vehicles, etc.) is a new strategic consideration for defence forces; falling under Naval S&T Strategic Plans. The objectives of the workshop were to seek state-of-the-art ideas, from outside of the continental United States, in the field of low-density metallic materials and composites for structural applications, as well as modeling and simulation software tools which are capable of generating and identifying damage evolution data for health monitoring, corrosion control, life prediction and assessment of civil and military hardware systems. The result is an invaluable guide to this increasingly important topic.
Author: Dakeshwar Kumar Verma
Publisher: Elsevier
ISBN: 0323951627
Category : Technology & Engineering
Languages : en
Pages : 566
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Book Description
Computational Modeling and Simulations for Designing of Corrosion Inhibitors: Fundamentals and Realistic Applications offers a collection of major advancements in the field of computational modeling for the design and testing of corrosion inhibition effectiveness of organic corrosion inhibitors. This guide presents the latest developments in molecular modeling of organic compounds using computational software, which has emerged as a powerful approach for theoretical determination of corrosion inhibition potentials of organic compounds. The book covers common techniques involved in theoretical studies of corrosion inhibition potentials, and mechanisms such as density functional theory, molecular dynamics, Monte Carlo simulations, artificial neural networks, and quantitative structure-activity relationship. - Covers basic, fundamental principles, advantages, parameters, and applications of computational and molecular modeling for designing potential corrosion inhibitors for metals and alloys - Describes advancements of computational modeling for the design of organic corrosion inhibitors and applications in electrochemical engineering and materials science - Focuses on the most advanced applications in industry-oriented fields, including current challenges - Includes websites of interest and information about the latest research
Author: Bhajan Lal
Publisher: Springer Nature
ISBN: 3031242319
Category : Technology & Engineering
Languages : en
Pages : 179
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Book Description
This book is useful to flow assurance engineers, students, and industries who wish to be flow assurance authorities in the twenty-first-century oil and gas industry. The use of digital or artificial intelligence methods in flow assurance has increased recently to achieve fast results without any thorough training effectively. Generally, flow assurance covers all risks associated with maintaining the flow of oil and gas during any stage in the petroleum industry. Flow assurance in the oil and gas industry covers the anticipation, limitation, and/or prevention of hydrates, wax, asphaltenes, scale, and corrosion during operation. Flow assurance challenges mostly lead to stoppage of production or plugs, damage to pipelines or production facilities, economic losses, and in severe cases blowouts and loss of human lives. A combination of several chemical and non-chemical techniques is mostly used to prevent flow assurance issues in the industry. However, the use of models to anticipate, limit, and/or prevent flow assurance problems is recommended as the best and most suitable practice. The existing proposed flow assurance models on hydrates, wax, asphaltenes, scale, and corrosion management are challenged with accuracy and precision. They are not also limited by several parametric assumptions. Recently, machine learning methods have gained much attention as best practices for predicting flow assurance issues. Examples of these machine learning models include conventional approaches such as artificial neural network, support vector machine (SVM), least square support vector machine (LSSVM), random forest (RF), and hybrid models. The use of machine learning in flow assurance is growing, and thus, relevant knowledge and guidelines on their application methods and effectiveness are needed for academic, industrial, and research purposes. In this book, the authors focus on the use and abilities of various machine learning methods in flow assurance. Initially, basic definitions and use of machine learning in flow assurance are discussed in a broader scope within the oil and gas industry. The rest of the chapters discuss the use of machine learning in various flow assurance areas such as hydrates, wax, asphaltenes, scale, and corrosion. Also, the use of machine learning in practical field applications is discussed to understand the practical use of machine learning in flow assurance.
Author: Joshua Ighalo
Publisher:
ISBN:
Category :
Languages : en
Pages : 55
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Book Description
This thesis proposes a new machine learning model for predicting the corrosion rate of 3C steel in seawater. The corrosion rate of a material depends not just on the nature of the material but also on the material's environmental conditions. The proposed machine learning model comes with a selection framework based on the hyperparameter optimization method and a performance evaluation metric to determine the models that qualify for further implementation in the proposed models' ensembles architecture. The major aim of the selection framework is to select the least number of models that will fit efficiently (while already hyperparameter-optimized) into the architecture of the proposed model. Subsequently, the proposed predictive model is fitted on some portion of a dataset generated from an experiment on corrosion rate in five different seawater conditions. The remaining portion of this dataset is implemented in estimating the corrosion rate. Furthermore, the performance of the proposed models' predictions was evaluated using three major performance evaluation metrics. These metrics were also used to evaluate the performance of two hyperparameter-optimized models (Smart Firefly Algorithm and Least Squares Support Vector Regression (SFA-LSSVR) and Support Vector Regression integrating Leave Out One Cross-Validation (SVR-LOOCV)) to facilitate their comparison with the proposed predictive model and its constituent models. The test results show that the proposed model performs slightly below the SFA-LSSVR model and above the SVR-LOOCV model by an RMSE score difference of 0.305 and RMSE score of 0.792. Despite its poor performance against the SFA-LSSVR model, the super learner model outperforms both hyperparameter-optimized models in the utilization of memory and computation time (graphically presented in this thesis).
Author: Oluwatobi Adeleke
Publisher: CRC Press
ISBN: 1003803113
Category : Technology & Engineering
Languages : en
Pages : 377
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Book Description
While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.
