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The manner in which time is captured forms the foundation for synthesis, design, and optimization in batch chemical plants. However, there are still serious challenges with handling time in batch plants. Most techniques tend to assume either a fixed time dimension or adopt time average models to tame the time dimension, thereby simplifying the resu
Optimization in Renewable Energy Systems: Recent Perspectives covers all major areas where optimization techniques have been applied to reduce uncertainty or improve results in renewable energy systems (RES). Production of power with RES is highly variable and unpredictable, leading to the need for optimization-based planning and operation in order to maximize economies while sustaining performance. This self-contained book begins with an introduction to optimization, then covers a wide range of applications in both large and small scale operations, including optimum operation of electric power systems with large penetration of RES, power forecasting, transmission system planning, and DG sizing and siting for distribution and end-user premises. This book is an excellent choice for energy engineers, researchers, system operators, system regulators, and graduate students. - Provides chapters written by experts in the field - Goes beyond forecasting to apply optimization techniques to a wide variety of renewable energy system issues, from large scale to relatively small scale systems - Provides accompanying computer code for related chapters
Manufacturing systems, regardless of their size, have to work with scarce resources in dynamic environments. Effective Resource Management in Manufacturing Systems aims to provide methods for achieving effective resource allocation and to solve related problems that occur daily and often generate cost overruns. This book will be bought by postgraduate students of business, engineering and computer science as well as researchers in these fields. It will also be of interest to practitioners in manufacturing systems and operations managers in industry.
Interplant Resource Integration: Optimization and Allocation presents an introduction to the planning and implementation methods for interplant resource integration. The analytic tools provided in this book can be used for the tasks of formulating mathematical programming model(s) to maximize the achievable overall savings and also for devising the "fair" distribution scheme(s) to allocate individual financial benefits among the participating plants. Offers tools for gaining economic benefit and environmental friendliness Presents methods for realistically feasible solutions Provides concrete mathematical modeling procedures Familiarizes readers with various network synthesis approaches and shows alternative viewpoints that can be adopted to model the interactions of participating members in an interplant resource integration scheme Aimed at chemical engineers, process engineers, industrial chemists, mechanical engineers in the fields of chemical processing and plant engineering.
The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods. Focusses on machine learning applications in Industry 4.0 ecosystem, such as resource optimization, data analysis, and predictions. Highlights the importance of the explainable machine learning model in the manufacturing processes. Presents the integration of machine learning and big data analytics from an industry 4.0 perspective. Discusses advanced computational techniques for sustainable manufacturing. Examines environmental impacts of operations and supply chain from an industry 4.0 perspective. This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection, cyber-attack prediction, and inventory management. It further discusses resource optimization using machine learning in industry 4.0, and explainable machine learning models for industry 4.0. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including mechanical engineering, manufacturing engineering, production engineering, aerospace engineering, and computer engineering.
Proven methodologies to enhance business value by exploiting the latest global technology trends and best business and IT practices There is no doubt that a tidal wave of change is hitting the area of business technology; new business models are forming around the cloud, new insights on how an enterprise runs is being aided by mining massive transactional and operational data sets. Decision-making is becoming almost prescient through new classes of data visualization, data analytics, and dashboards. Despite the promise of technologies to make a difference, or perhaps because of it, IT organizations face continued challenges in realizing partnerships and trust with their business partners. While many books take on elements of these emerging developments or address the stubborn barriers to "real" partnership, none make the practices involved fit together in a highly effective fashion - until now. Strategic IT Management in Turbulent Times reveals how this framework ensures that organizations make the right strategic decisions to succeed in times of turbulence and change. Draws together authors with global experience including the Americas, Europe, Pacific Rim, and Africa Offers a comprehensive framework for IT and business managers to maximize the value IT brings to business Addresses the effects of turbulence on business and IT Focuses on developing partnerships and trust with business With practical examples and implementation guidance based on proven techniques developed by the authors over the past twenty years, Strategic IT Management in Turbulent Times considers the challenges facing today's enterprise, IT's critical role in value creation, and the practical road map for achieving strategic IT management competencies.
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With the rapid proliferation of information and communications technology, industrial automation has undergone a sweeping transformation toward intelligent manufacturing. Wireless communication is widely considered to be one of the key technologies enabling intelligent manufacturing. On one hand, deterministic communication with high reliability and low latency is typically required in industrial automation applications. On the other hand, wireless communication in industrial settings is hindered by strictly limited communication resources and many other factors which mainly derive from the shared and error-prone nature of the wireless channels used. The limited communication resources and harsh channel conditions pose considerable challenges for reliable, real-time data transmission in industrial wireless networks. Resource optimization methods are vital to ensuring the deterministic performance of industrial wireless networks. Traditional resource optimization methods adopt the isolated resource optimization methods for each protocol layer, which is inherently local-optimal and leads performance uncontrollable. To focus on “Performance Controllable Industrial Wireless Networks”, this book presents thejoint resource optimization methods across multiple protocol layers for industrial wireless networks; reviews recent, major advances; and discusses the practical implementations of the proposed methods. The joint resource optimization methods discussed here will greatly benefit scientists and researchers in the areas of industrial automation and Industrial Internet of Things. To gain the most from this book, readers should have a fundamental grasp of wireless communication, scheduling theory, and convex optimization.