Data Driven Modeling For Sustainable Engineering

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.

This book gathers the proceedings of the 1st International Conference on Engineering, Applied Sciences and System Modeling (ICEASSM), a four-day event (18th–21st April 2017) held in Accra, Ghana. It focuses on research work promoting a better understanding of engineering problems through applied sciences and modeling, and on solutions generated in an African setting but with relevance to the world as a whole. The book provides a holistic overview of challenges facing Africa, and addresses various areas from research and development perspectives. Presenting contributions by scientists, engineers and experts hailing from a host of international institutions, the book offers original approaches and technological solutions to help solve real-world problems through research and knowledge sharing. Further, it explores promising opportunities for collaborative research on issues of scientific, economic and social development, making it of interest to researchers, scientists and practitioners looking to conduct research in disciplines such as water supply, control, civil engineering, statistical modeling, renewable energy and sustainable urban development.

This book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.

Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.

This book provides a comprehensive overview of potential opportunities and the business value position related to implementing physics-based real-time simulation to production. The objective of real-time simulation is to provide value for all three dimensions of sustainability: economic, social, and environmental. By reviewing actual industrial cases and presenting relevant academic research, the book examines the topic from four interrelated viewpoints: the industrial need for sustainable production, the development of game-like virtual environments, capturing customer value and enhancing the user experience, and finally, establishing business value. It offers a framework that will enable a rethink and shift in mindset to appreciate how real-time simulation can change the way products are manufactured and services are produced. This book will appeal to researchers and scholars in areas as diverse as strategic management, manufacturing and operations management, marketing, industrial economics, and product lifecycle management.

Sustainable engineering is of great importance for resilient and agile technology and society. This book balances economics, environment, and societal elements of sustainable engineering by integrating process intensification, energy analysis, and artificial intelligence to reduce production costs, improve the use of material and energy, product quality, safety, societal well-being, and water usage. The book provides comprehensive discussion of topics on process intensification, energy analysis, and artificial intelligence that include optimization, energy integration, green engineering, pinch analysis, exergy analysis, feasibility analysis, life cycle assessment, circular economy, bioeconomy, data processing, machine learning, expert systems, digital twins, and self-optimized plants for sustainable engineering.

This book takes a unique interdisciplinary look at the latest developments, advances, and trends in the interrelated areas of sustainable engineering, energy, and the environment, focusing on environmental engineering for renewable and green energy. It looks at new research and studies on a variety of topics in green nanotechnology, green processing and solar energy, sustainable energy policies, biofuels, fuel cells, and much more. The first section of Sustainable Engineering, Energy, and the Environment: Challenges and Opportunities looks at myriad issues in sustainable energy, such as sustainable urbanism through space planning and residential building design, a method to convert vibrations from mechanical work into power, energy grid maintenance, mathematical modeling and time analysis of various mechanical activities, and more. Topics on sustainable energy include voltage systems for stand-alone nanogrids, new sources for biodiesel production, solar energy conversion, protection equipment for windmill towers, etc. The section on sustainable environment explores issues such as industrial water recycling, regeneration of spent-activated carbon in pharmaceutical production, smell mitigation and recovery of fuel from waste, the water footprint of agriculture, etc. Key features Presents advances and developments in the areas of engineering, energy, and environment under sustainable development Examines potential issues of understanding of green buildings and their energy efficiency Presents case studies on sustainable urbanization Presents novel clean technology applications for attaining environmental sustainability Assesses green auditing and natural capital accounting Describes relevant experimental techniques This book features important contributions from scientists, academicians, and professionals on the latest developments and advances in the interrelated fields of sustainable engineering, energy, and environment.