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The increased complexity of the economy in recent years has led to the advancement of energy generation systems. Engineers in this industrial sector have been compelled to seek contemporary methods to keep pace with the rapid development of these systems. Computational intelligence has risen as a capable method that can effectively resolve complex scenarios within the power generation sector. In-depth research on the various applications of this technology is lacking, as engineering professionals need up-to-date information on how to successfully utilize computational intelligence in industrial systems. Multi-Objective Optimization of Industrial Power Generation Systems: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of the application of intelligent optimization techniques within industrial energy systems. Featuring coverage on a broad range of topics such as swarm intelligence, renewable energy, and predictive modeling, this book is ideally designed for industrialists, engineers, industry professionals, researchers, students, and academics seeking current research on computational intelligence frameworks within the power generation sector.
Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern. Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy. Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry. - Looks at new techniques in artificial intelligence (AI) reducing the dimension of data coverage worldwide - Chapters include AI methodologies using enhanced hybrid swarm-based optimization algorithms - Includes flowchart diagrams for exampling optimizing techniques
Artificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT). Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks. Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor. Provides guidance on implementing metaheuristics in different applications and hybrid technological systems. Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.
To plan, build, monitor, maintain, and dispose of products and assets properly, maintenance and safety requirements must be implemented and followed. A lack of maintenance and safety protocols leads to accidents and environmental disasters as well as unexpected downtime that costs businesses money and time. With the arrival of the Fourth Industrial Revolution and evolving technological tools, it is imperative that safety and maintenance practices be reexamined. Applications and Challenges of Maintenance and Safety Engineering in Industry 4.0 is a collection of innovative research that addresses safety and design for maintenance and reducing the factors that influence and degrade human performance and that provides technological advancements and emergent technologies that reduce the dependence on operator capabilities. Highlighting a wide range of topics including management analytics, internet of things (IoT), and maintenance, this book is ideally designed for engineers, software designers, technology developers, managers, safety officials, researchers, academicians, and students.
New technologies have the power to augment many aspects of society, including public spaces and art. The impact of smart technology on urban design is vast and filled with opportunity and has profound implications on the everyday urban environment. Only by starting new conversations can we develop further contemporary insights that will affect how we move through the world. Reconstructing Urban Ambiance in Smart Public Places is a pivotal reference source that provides contemporary insights into a comprehensive interpretation of urban ambiances in smart places as it relates to the development of cities or to various levels of intervention in extant urban environments. The book also examines the impact of architectural design on the creation of urban ambience in artworks and how to reflect this technique in the fields of professional architectural practice. While covering a wide range of topics including wellbeing, quality-related artistry, and atmosphere, this publication combines smart technological innovation with creative design principles. This book is ideally designed for civil engineers, urban designers, architects, entrepreneurs, policymakers, researchers, academicians, and students.
Additive manufacturing (AM) of metals and composites using laser energy, direct energy deposition, electron beam methods, and wire arc melting have recently gained importance due to their advantages in fabricating the complex structure. Today, it has become possible to reliably manufacture dense parts with certain AM processes for many materials, including steels, aluminum and titanium alloys, superalloys, metal-based composites, and ceramic matrix composites. In the near future, the AM material variety will most likely grow further, with high-performance materials such as intermetallic compounds and high entropy alloys already under investigation. Additive Manufacturing Applications for Metals and Composites is a pivotal reference source that provides vital research on advancing methods and technological developments within additive manufacturing practices. Special attention is paid to the material design of additive manufacturing of parts, the choice of feedstock materials, the metallurgical behavior and synthesis principle during the manufacturing process, and the resulted microstructures and properties, as well as the relationship between these factors. While highlighting topics such as numerical modeling, intermetallic compounds, and statistical techniques, this publication is ideally designed for students, engineers, researchers, manufacturers, technologists, academicians, practitioners, scholars, and educators.
In recent years, intelligent cities, also known as smart cities or cognitive cities, have become a perceived solution for improving the quality of life of citizens while boosting the efficiency of city services and processes. This new vision involves the integration of various sectors of society through the use of the internet of things. By continuing to enhance research for the better development of the smart environments needed to sustain intelligent cities, citizens will be empowered to provision the e-services provided by the city, city officials will have the ability to interact directly with the community as well as monitor digital environments, and smart communities will be developed where citizens can enjoy improved quality of life. Developing and Monitoring Smart Environments for Intelligent Cities compiles the latest research on the development, management, and monitoring of digital cities and intelligent environments into one complete reference source. The book contains chapters that examine current technologies and the future use of internet of things frameworks as well as device connectivity approaches, communication protocols, security challenges, and their inherent issues and limitations. Including unique coverage on topics such as connected vehicles for smart transportation, security issues for smart homes, and building smart cities for the blind, this reference is ideal for practitioners, urban developers, urban planners, academicians, researchers, and students.
All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.
The advent of connected, smart technologies for the built environment may promise a significant value that has to be reached to develop digital city models. At the international level, the role of digital twin is strictly related to massive amounts of data that need to be processed, which proposes several challenges in terms of digital technologies capability, computing, interoperability, simulation, calibration, and representation. In these terms, the development of 3D parametric models as digital twins to evaluate energy assessment of private and public buildings is considered one of the main challenges of the last years. The ability to gather, manage, and communicate contents related to energy saving in buildings for the development of smart cities must be considered a specificity in the age of connection to increase citizen awareness of these fields. The Handbook of Research on Developing Smart Cities Based on Digital Twins contains in-depth research focused on the description of methods, processes, and tools that can be adopted to achieve smart city goals. The book presents a valid medium for disseminating innovative data management methods related to smart city topics. While highlighting topics such as data visualization, a web-based ICT platform, and data-sharing methods, this book is ideally intended for researchers in the building industry, energy, and computer science fields; public administrators; building managers; and energy professionals along with practitioners, stakeholders, researchers, academicians, and students interested in the implementation of smart technologies for the built environment.
For reasons both financial and environmental, there is a perpetual need to optimize the design and operating conditions of industrial process systems in order to improve their performance, energy efficiency, profitability, safety and reliability. However, with most chemical engineering application problems having many variables with complex inter-relationships, meeting these optimization objectives can be challenging. This is where Multi-Objective Optimization (MOO) is useful to find the optimal trade-offs among two or more conflicting objectives. This book provides an overview of the recent developments and applications of MOO for modeling, design and operation of chemical, petrochemical, pharmaceutical, energy and related processes. It then covers important theoretical and computational developments as well as specific applications such as metabolic reaction networks, chromatographic systems, CO2 emissions targeting for petroleum refining units, ecodesign of chemical processes, ethanol purification and cumene process design. Multi-Objective Optimization in Chemical Engineering: Developments and Applications is an invaluable resource for researchers and graduate students in chemical engineering as well as industrial practitioners and engineers involved in process design, modeling and optimization.