Souvik Ganguli - Böcker

Intelligent Green Technologies for Sustainable Smart Cities Presenting the concepts and fundamentals of smart cities and developing “green” technologies, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple disciplines and industries, for both the engineer and the student. Smart cities and green technologies are quickly becoming two of the most important areas of development facing today’s engineers, scientists, students, and other professionals. Written by a team of experts in these fields, this outstanding new volume tackles the problem of detailing advances in smart city development, green technologies, and where the two areas intersect to create innovation and revolutionary solutions. This group of hand-selected and vetted papers deals with the fundamental concepts of adapting artificial intelligence, machine learning techniques with green technologies, and many other advances in concepts related to these key areas. Including the most recent research and developments available, this book is an extraordinary source of knowledge for students, engineers seeking the latest research, and facilities and other professionals working in the area of green technologies and challenges and solutions in urban planning and smart city development.

INDUSTRIAL CONTROL SYSTEMS This volume serves as a comprehensive guide in the journey of industrial control systems with a multidisciplinary approach to the key engineering problems in the 21st century. The journey of the control system may be viewed from the control of steam engines to spacecraft, aeroplane missile control systems to networked control systems and cybersecurity controls. In terms of industrial control and application, the journey starts from the design of P-I-D controllers to fuzzy controllers, neuro-fuzzy controllers, backstepping controllers, sliding mode controllers, and event-triggered controls for networked control systems. Recently, control theory has spread its golden feathers in different fields of engineering by use of the splendid tool of the control system. In this era, the boom of the Internet of Things is at its maximum pace. Different biomedical applications also come under this umbrella and provide the easiest way to continuous monitoring. One of the prominent research areas of green energy and sustainable development in which control plays a vital role is load frequency controllers, control of solar thermal plants, an event-driven building energy management system, speed-sensorless voltage and frequency control in autonomous DFIG-based wind energy, Hazardous Energy Control Programs, and many more. This exciting new volume: Offers a complete journey through industrial control systems Is written for multidisciplinary students and veteran engineers alike Benefits researchers from diverse disciplines with real-world applications

Written and edited by a team of experts in the field, this exciting new volume presents the cutting-edge techniques, latest trends, and state-of-the-art practical applications in linear and nonlinear system modeling. Mathematical modeling of control systems is, essentially, extracting the essence of practical problems into systematic mathematical language. In system modeling, mathematical expression deals with modeling and its applications. It is characterized that how a modeling competency can be categorized and its activity can contribute to building up these competencies. Mathematical modeling of a practical system is an attractive field of research and an advanced subject with a variety of applications. The main objective of mathematical modeling is to predict the behavior of the system under different operating conditions and to design and implement efficient control strategies to achieve the desired performance. A considerable effort has been directed to the development of models, which must be understandable and easy to analyze. It is a very difficult task to develop mathematical modeling of complicated practical systems considering all its possible high-level non-linearity and cross couple dynamics. Although mathematical modeling of nonlinear systems sounds quite interesting, it is difficult to formulate the general solution to analyze and synthesize nonlinear dynamical systems. Most of the natural processes are nonlinear, having very high computational complexity of several numerical issues. It is impossible to create any general solution or individual procedure to develop exact modeling of a non-linear system, which is often improper and too complex for engineering practices. Therefore, some series of approximation procedures are used, in order to get some necessary knowledge about the nonlinear system dynamics. There are several complicated mathematical approaches for solving these types of problems, such as functional analysis, differential geometry or the theory of nonlinear differential equations.

Controller Design for Industrial Applications is essential for anyone looking to master the advanced techniques of intelligent controller design, enabling you to effectively tackle the complexities of modern industrial processes and optimize performance in an ever-evolving landscape. Industrial processes are often complex and dynamic, making it challenging to design controllers that can maintain stable and optimal operation. Traditional controllers, such as PID controllers, have been widely used in industrial applications but have limitations in handling non-linear and uncertain systems. Intelligent controllers offer an alternative solution that can adapt to changing system dynamics and disturbances. The use of intelligent controllers in industrial applications has gained increasing attention in recent years, with numerous successful implementations in various fields, such as process control, robotics control, HVAC control, power systems control, and autonomous vehicle control. However, the design and implementation of intelligent controllers require careful consideration of hardware and software requirements, as well as simulation and testing procedures to ensure reliable and safe operation. In the rapidly evolving industrial landscape, it is essential to develop advanced control techniques to enhance productivity, minimize costs, and ensure safety. Traditional control methods often struggle to handle complex systems and unpredictable environments. However, with the emergence of intelligent control techniques, there is a great opportunity to improve industrial automation and control systems. Controller Design for Industrial Applications aims to provide a comprehensive understanding of intelligent controller design for industrial applications, from theoretical concepts to practical implementation. It will cover the fundamental concepts of intelligent control theory and techniques, their application in various industrial fields, and practical implementation and design considerations.

Equip yourself with the essentials of informed decision-making with this practical guide to mastering data-driven modeling and extracting actionable, meaningful patterns from the vast sea of modern data. In an era defined by data, the ability to transform raw information into actionable insights is a skill set that transcends industries and disciplines. This book is a comprehensive guide designed to unravel the intricacies of extracting meaningful patterns from the vast sea of data that surrounds us. It explores the significance of data-driven modelling, comparing it to traditional approaches and setting the stage for understanding the transformative power and diverse applications of data-driven techniques. This comprehensive resource empowers readers to leverage data for informed decision-making. Whether you are a novice looking to grasp the fundamentals or an experienced professional seeking advanced techniques, this book serves as a practical guide through the dynamic landscape of data-driven modelling. Through clear explanations, hands-on examples, and real-world applications, readers will gain the skills needed to navigate the complexities of modern data analysis.