Fausto Pedro Garcia Marquez – författare

Approx.200 pagesApprox.200 pages

Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets integrates state-of-the-art information and discusses future developments and their significance to the improvement of the renewable energy industry. Renewable energy assets are complex systems with several critical components that require inspection and adequate maintenance in order to ensure their high availability and uninterrupted operation. This is the first book to apply NDT and condition monitoring to these complex systems.- Covers inspection and condition monitoring for a broad range of renewable energy systems, including wind turbines, wave energy devices, CSP and photovoltaic plants, and biofuel/biomass power plants- Includes a review of common types of NDT techniques- Discusses future developments in NDT and condition monitoring for renewable energy systems

Decision-Making Management: A Tutorial and Applications provides practical guidance for researchers seeking to optimizing business-critical decisions employing Logical Decision Trees thus saving time and money. The book focuses on decision-making and resource allocation across and between the manufacturing, product design and logistical functions. It demonstrates key results for each sector with diverse real-world case studies drawn primarily from EU projects. Theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data and lot of data. Binary Decision Diagrams are presented as the operating approach for evaluating large Logical Decision Trees, helping readers identify Boolean equations for quantitative analysis of multifaceted problem sets. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The final objective is to optimize dynamic decisions with original approaches employing useful tools, including Big Data analysis. Extensive annexes provide useful supplementary information for readers to follow methods contained in the book.

Explores the use of logical decision trees to solve business problems Uses mathematical optimization techniques to resolve 'big data' or other multi-criteria problems Provides annexes showcasing application in manufacturing, product design and logistics Shows case examples in telecommunications, renewable energy and aerospace Supplies introduction by Benjamin Lev, Editor-in-Chief of Omega, the highest-ranked journal in management science (JCR)

Decision-Making Management: A Tutorial and Applications provides practical guidance for researchers seeking to optimizing business-critical decisions employing Logical Decision Trees thus saving time and money. The book focuses on decision-making and resource allocation across and between the manufacturing, product design and logistical functions. It demonstrates key results for each sector with diverse real-world case studies drawn primarily from EU projects. Theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data and lot of data. Binary Decision Diagrams are presented as the operating approach for evaluating large Logical Decision Trees, helping readers identify Boolean equations for quantitative analysis of multifaceted problem sets. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The final objective is to optimize dynamic decisions with original approaches employing useful tools, including Big Data analysis. Extensive annexes provide useful supplementary information for readers to follow methods contained in the book.- Explores the use of logical decision trees to solve business problems- Uses mathematical optimization techniques to resolve ''big data'' or other multi-criteria problems- Provides annexes showcasing application in manufacturing, product design and logistics- Shows case examples in telecommunications, renewable energy and aerospace- Supplies introduction by Benjamin Lev, Editor-in-Chief of Omega, the highest-ranked journal in management science (JCR)

Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy looks at the complex and critical components of energy assets and the importance of inspection and maintenance to ensure their high availability and uninterrupted operation. Presenting the main concepts, state-of-the-art advances and case studies, this book approaches the topic by considering it as an integral part of the overall operation of any wind energy project. Linking the essential NDT subject with its sub disciplines, the book uses computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques to support analysis of prognostic problems with defined constraints and requirements.This book is the first of its kind and will provide useful insights to industrial engineers and scientists, academics and students in the possibilities that NDT and condition monitoring technologies can offer.- Presents advances in Non-Destructive Techniques and Condition Monitoring Systems applied in the energy industry- Provides case studies in Fault Detection and Diagnosis and Prognosis for critical variability- Offers technical maintenance actions for the observation and analyses of inspection, monitoring, testing, diagnosis, prognosis and active maintenance actions in wind

Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy looks at the complex and critical components of energy assets and the importance of inspection and maintenance to ensure their high availability and uninterrupted operation. Presenting the main concepts, state-of-the-art advances and case studies, this book approaches the topic by considering it as an integral part of the overall operation of any wind energy project. Linking the essential NDT subject with its sub disciplines, the book uses computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques to support analysis of prognostic problems with defined constraints and requirements.

This book is the first of its kind and will provide useful insights to industrial engineers and scientists, academics and students in the possibilities that NDT and condition monitoring technologies can offer.

Presents advances in Non-Destructive Techniques and Condition Monitoring Systems applied in the energy industry Provides case studies in Fault Detection and Diagnosis and Prognosis for critical variability Offers technical maintenance actions for the observation and analyses of inspection, monitoring, testing, diagnosis, prognosis and active maintenance actions in wind

Advanced Analytics for Reliability and Resilience of Energy Systems prepares students, researchers, and industry engineers to design and maintain reliable, sustainable energy systems using state-of-the-art AI techniques. This book provides a clear foundation in the fundamentals of power systems, statistics, and reliability, including resilience principles and strategies, practical applications, and real-world solutions. The AI tools presented range across forecasting, the Internet-of-Things, machine learning, digital twin technology, and big data analysis, with a variety of applications to avoid power outages, minimise disruption, and accurately assess system resilience. Including case studies and details methodology for practical techniques, Advanced Analytics for Reliability and Resilience of Energy Systems helps energy systems engineers and researchers to provide a stable and consistent power supply, in the face of climate change challenges and the energy transitionProvides a foundation with the fundamental principles and strategies for reliability and resilienceIntroduces the readers to a toolbox of AI methodologies with a variety of applicationsLeverages clear chapter objectives, worked problems, and detailed case studies to support real-world solution-building

Advanced Analytics for Reliability and Resilience of Energy Systems prepares students, researchers, and industry engineers to design and maintain reliable, sustainable energy systems using state-of-the-art AI techniques. This book provides a clear foundation in the fundamentals of power systems, statistics, and reliability, including resilience principles and strategies, practical applications, and real-world solutions. The AI tools presented range across forecasting, the Internet-of-Things, machine learning, digital twin technology, and big data analysis, with a variety of applications to avoid power outages, minimise disruption, and accurately assess system resilience. Including case studies and details methodology for practical techniques, Advanced Analytics for Reliability and Resilience of Energy Systems helps energy systems engineers and researchers to provide a stable and consistent power supply, in the face of climate change challenges and the energy transition- Provides a foundation with the fundamental principles and strategies for reliability and resilience- Introduces the readers to a toolbox of AI methodologies with a variety of applications- Leverages clear chapter objectives, worked problems, and detailed case studies to support real-world solution-building

Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered. The very first book in Elsevier’s cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.Provides a sorely needed primer on the opportunities of computer vision techniques for renewable energy systemsBuilds knowledge and tools in a systematic manner, from fundamentals to advanced applicationsIncludes dedicated chapters with case studies and applications for each sustainable energy source

Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered. The very first book in Elsevier''s cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.- Provides a sorely needed primer on the opportunities of computer vision techniques for renewable energy systems- Builds knowledge and tools in a systematic manner, from fundamentals to advanced applications- Includes dedicated chapters with case studies and applications for each sustainable energy source

Computational Automation for Water Security: Enhancing Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and outdated practices, presenting innovative solutions to enhance decision-making, optimizing water treatment processes, and ultimately improving environmental outcomes. Through the coverage of advanced computational techniques, such as data analysis, machine learning, and optimization strategies, readers will gain a deep understanding of how computational automation can revolutionize decision-making. This book is an invaluable resource for professionals, researchers, and policymakers seeking to stay at the forefront of water quality management practices, harnessing the power of computational automation for a cleaner, healthier future.Offers a holistic understanding of the application of computational automation in water quality managementContains practical and unique updates to help learners how to apply computational techniques to address water quality challengesProvides a comprehensive and multidisciplinary perspective on water quality management

Computational Automation for Water Security: Enhancing Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and outdated practices, presenting innovative solutions to enhance decision-making, optimizing water treatment processes, and ultimately improving environmental outcomes. Through the coverage of advanced computational techniques, such as data analysis, machine learning, and optimization strategies, readers will gain a deep understanding of how computational automation can revolutionize decision-making. This book is an invaluable resource for professionals, researchers, and policymakers seeking to stay at the forefront of water quality management practices, harnessing the power of computational automation for a cleaner, healthier future.- Offers a holistic understanding of the application of computational automation in water quality management- Contains practical and unique updates to help learners how to apply computational techniques to address water quality challenges- Provides a comprehensive and multidisciplinary perspective on water quality management

Designing reliable and maintainable energy systems is crucial for ensuring efficiency, longevity, and sustainability in energy production, distribution, and consumption. Reliability focuses on minimizing failures and ensuring continuous operation under varying conditions, while maintainability emphasizes ease of inspection, repair, and upgrades. A well-designed energy system integrates robust components, predictive maintenance strategies, and advanced monitoring technologies to reduce downtime and operational costs. By prioritizing both reliability and maintainability, engineers can develop resilient energy infrastructures that support modern demands while minimizing environmental impact and lifecycle costs.Designing Reliability and Maintainability of Energy Systems is a guide to enhancing the resilience and reliability of modern energy infrastructures. Chapters present fundamental principles of reliability and resilience and new artificial intelligence applications, such as predictive maintenance, advanced analytics, and digital twin technologies for improved efficiency and sustainability in energy systems. Clear chapter objectives, worked problems, and case studies are included to demonstrate real-world applications of these technologies.This book is valuable for researchers, industry professionals, engineers, and advanced undergraduate and graduate students focused on energy grids, power system reliability, and AI-driven solutions for sustainable infrastructure.

Provides a foundation with the fundamental principles and strategies for reliability and resilienceIntroduces the readers to a toolbox of AI methodologies with a variety of applicationsLeverages clear chapter objectives, worked problems, and detailed case studies to support real-world solution-building

Designing reliable and maintainable energy systems is crucial for ensuring efficiency, longevity, and sustainability in energy production, distribution, and consumption. Reliability focuses on minimizing failures and ensuring continuous operation under varying conditions, while maintainability emphasizes ease of inspection, repair, and upgrades. A well-designed energy system integrates robust components, predictive maintenance strategies, and advanced monitoring technologies to reduce downtime and operational costs. By prioritizing both reliability and maintainability, engineers can develop resilient energy infrastructures that support modern demands while minimizing environmental impact and lifecycle costs.Designing Reliability and Maintainability of Energy Systems is a guide to enhancing the resilience and reliability of modern energy infrastructures. Chapters present fundamental principles of reliability and resilience and new artificial intelligence applications, such as predictive maintenance, advanced analytics, and digital twin technologies for improved efficiency and sustainability in energy systems. Clear chapter objectives, worked problems, and case studies are included to demonstrate real-world applications of these technologies.This book is valuable for researchers, industry professionals, engineers, and advanced undergraduate and graduate students focused on energy grids, power system reliability, and AI-driven solutions for sustainable infrastructure. - Provides a foundation with the fundamental principles and strategies for reliability and resilience- Introduces the readers to a toolbox of AI methodologies with a variety of applications- Leverages clear chapter objectives, worked problems, and detailed case studies to support real-world solution-building

As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary.Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

This book provides a cross-disciplinary framework for addressing the competing priorities of modern infrastructures, including cost, efficiency, and security, by integrating Operations Research with Machine Intelligence. The book offers a structured exploration of intelligent modeling and predictive analysis aimed at improving system resilience and promoting informed decision-making. Readers will find practical applications of computer vision and machine learning across a range of domains, including intelligent energy systems and digital twins. By combining hybrid methodologies with real-world use cases, Computing, IoT, and Data Analytics engages with current challenges in Internet-of-Things (IoT) security, pattern recognition, and autonomous communication networks. Engineers, researchers, and applied scientists will find in this volume useful frameworks for translating advanced computational methods into efficient systems that can be applied in practice.