Download Free Robust Model Predictive Control Of Water Quality In Drinking Water Distribution Systems Book in PDF and EPUB Free Download. You can read online Robust Model Predictive Control Of Water Quality In Drinking Water Distribution Systems and write the review.

This book is a printed edition of the Special Issue "Feature Papers for Celebrating the Fifth Anniversary of the Founding of Processes" that was published in Processes
This book contains 38 papers authored by both scientists and practitioners focused on an interdisciplinary approach to the development of cyber-physical systems. Recently our civilization has been facing one of the most severe challenges in modern history. The COVID-19 pandemic devastated the global economy and significantly disrupted numerous areas of economic activity. Only radical increase of efficiency and versatility of industrial production, with further limitation of human involvement, paralleled by the decrease of environmental burden, will enable us to cope with such challenges. We hope that the presented book provides input to the solution of at least some problems brought about by this challenge. This approach relies on the development of measuring techniques, robotic and mechatronic systems, industrial automation, numerical modeling and simulation as well as application of artificial intelligence techniques required by the transformation leading to Industry 4.0.
This book describes the challenges that critical infrastructure systems face, and presents state of the art solutions to address them. How can we design intelligent systems or intelligent agents that can make appropriate real-time decisions in the management of such large-scale, complex systems? What are the primary challenges for critical infrastructure systems? The book also provides readers with the relevant information to recognize how important infrastructures are, and their role in connection with a society’s economy, security and prosperity. It goes on to describe state-of-the-art solutions to address these points, including new methodologies and instrumentation tools (e.g. embedded software and intelligent algorithms) for transforming and optimizing target infrastructures. The book is the most comprehensive resource to date for professionals in both the private and public sectors, while also offering an essential guide for students and researchers in the areas of modeling and analysis of critical infrastructure systems, monitoring, control, risk/impact evaluation, fault diagnosis, fault-tolerant control, and infrastructure dependencies/interdependencies. The importance of the research presented in the book is reflected in the fact that currently, for the first time in human history, more people live in cities than in rural areas, and that, by 2050, roughly 70% of the world’s total population is expected to live in cities.
Future technology information technology stands for all of continuously evolving and converging information technologies, including digital convergence, multimedia convergence, intelligent applications, embedded systems, mobile and wireless communications, bio-inspired computing, grid and cloud computing, semantic web, user experience and HCI, security and trust computing and so on, for satisfying our ever-changing needs. In past twenty five years or so, Information Technology (IT) influenced and changed every aspect of our lives and our cultures. These proceedings foster the dissemination of state-of-the-art research in all future IT areas, including their models, services, and novel applications associated with their utilization.
This two volume set LNCS 5768 and LNCS 5769 constitutes the refereed proceedings of the 19th International Conference on Artificial Neural Networks, ICANN 2009, held in Limassol, Cyprus, in September 2009. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The first volume is divided in topical sections on learning algorithms; computational neuroscience; hardware implementations and embedded systems; self organization; intelligent control and adaptive systems; neural and hybrid architectures; support vector machine; and recurrent neural network.
The book presents basic structures, concepts and algorithms in the area of multilayer optimizing control of industrial systems, as well as the results of the research that was carried out by the authors over the last two decades. The methodologies and control algorithms are thoroughly illustrated by numerous simulation examples. Also, the applications to several case study examples are presented. These include ethylene distillation column, vaporizer pilot scale plant, styrene distillation line consisting of three columns and industrial furnace pilot scale plant. A temporal decomposition is applied to the Integrated Wastewater System case study to derive multilayer dynamic optimizing controller with repetitive robust model predictive control mechanism distributed over the layers operating in different time scales.
This book presents a set of approaches for the real-time monitoring and control of drinking-water networks based on advanced information and communication technologies. It shows the reader how to achieve significant improvements in efficiency in terms of water use, energy consumption, water loss minimization, and water quality guarantees. The methods and approaches presented are illustrated and have been applied using real-life pilot demonstrations based on the drinking-water network in Barcelona, Spain. The proposed approaches and tools cover: • decision-making support for real-time optimal control of water transport networks, explaining how stochastic model predictive control algorithms that take explicit account of uncertainties associated with energy prices and real demand allow the main flow and pressure actuators—pumping stations and pressure regulation valves— and intermediate storage tanks to be operated to meet demand using the most sustainable types of source and with minimum electricity costs;• decision-making support for monitoring water balance and distribution network quality in real time, implementing fault detection and diagnosis techniques and using information from hundreds of flow, pressure, and water-quality sensors together with hydraulic and quality-parameter-evolution models to detect and locate leaks in the network, possible breaches in water quality, and failures in sensors and/or actuators;• consumer-demand prediction, based on smart metering techniques, producing detailed analyses and forecasts of consumption patterns, providing a customer communications service, and suggesting economic measures intended to promote more efficient use of water at the household level. Researchers and engineers working with drinking-water networks will find this a vital support in overcoming the problems associated with increased population, environmental sensitivities and regulation, aging infrastructures, energy requirements, and limited water sources.
Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.