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Aquesta tesi està dedicada a dissenyar un controlador MPC multicapa que s'aplica a una complexa xarxa regional emprant com a principal idea el fet de què les diferents capes treballen amb diferents escales de temps i objectius de control s'aconseguiran amb el seu propi controlador. Un esquema jeràrquic de coordinació temporal de dues capes s'ha aplicat per a coordinar als controladors MPC per a les xarxes de captació i transport. Un enfocament integrat de simulació-optimizació que contribueix a asegurar que l'efecte de les dinàmiques complexes, millor representades pel model de simulació s'hagin tingut en compte, s'ha propostat per la gestió operacional temps real de les xarxes regionals. La segona part d'aquesta tesi es centra en el disseny d'un esquema de control que utilitza la combinació del control MPC lineal amb una problema de satisfacció de restriccions (CSP) per optimitzar el control operacional no-lineal de les xarxes d'aigua potable. El mètode d'agregació de xarxes (NAM) s'utilitza per simplificar una xarxa d'aigua complexa en una xarxa conceptual bidireccional equivalent abans d'utilitzar el CSP. L'enfocament proposat es simula utilitzant Epanet per representar el comportament hidràulic de la xarxa d'aigua potable. Finalment, el MPC no lineal s'utilitza per a la validació fent ús de l'eina PLIO per a la seva implementació. I també, un esquema de planificació de dues capes per a estacions de bombament en una aigua xarxa de distribució ha estat proposat en la segona part d'aquesta tesi. Els paràmetres d'ajust d'aquest algorisme són el período de mostreig de control de la capa inferior i el número de bombes en paral·lel en la estació de bombament.
In the research Model Predictive Control on Open Water Systems', the relatively new control methodology Model Predictive Control is configured for application of water quantity control on open water systems, especially on irrigation canals and large drainage systems. The methodology applies an internal model of the open water system, by which optimal control actions are calculated over a prediction horizon. As internal model, two simplified models are used, the Integrator Delay model and the Saint Venant model. Kalman filtering is applied to initialize the internal models. The optimization uses an objective function in which conflicting objectives can be weighed. In most of the cases, these conflicting objectives are keeping the water levels at different locations in the water system within a range around setpoint and executing this by using as little control effort or energy as possible.
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ..., new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The water and wastewater industry has undergone many changes in recent years. Of particular importance has been a renewed emphasis on improving resource management with tighter regulatory controls setting new targets on pricing, industry efficiency and loss reduction for both water and wastewater with more stringent environmental discharge conditions for wastewater. Meantime, the demand for water and wastewater services grows as the population increases and wishes for improved living conditions involving, among other items, domestic appliances that use water. Consequently, the installed infrastructure of the industry has to be continuously upgraded and extended, and employed more effectively to accommodate the new demands, both in throughput and in meeting the new regulatory conditions. Investment in fixed infrastructure is capital-intensive and slow to come on-stream. One outcome of these changes and demands is that the industry is examining the potential benefits of, and in many cases using, more advanced control systems.
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.
This book aims to contribute to the conceptual and practical knowledge pools in order to improve the research and practice on the sustainable development of smart cities by bringing an informed understanding of the subject to scholars, policymakers, and practitioners. This book seeks articles offering insights into the sustainable development of smart cities by providing in-depth conceptual analyses and detailed case study descriptions and empirical investigations. This way, the book will form a repository of relevant information, material, and knowledge to support research, policymaking, practice, and transferability of experiences to address aforementioned challenges. The scope of the book includes the following broad areas, with a particular focus on the approaches, advances, and applications in the sustainable development of smart cities: • Theoretical underpinnings and analytical and policy frameworks; • Methodological approaches for the evaluation of smart and sustainable cities; • Technological developments in the techno-enviro nexus; • Global best practice smart city case investigations and reports; • Geo-design and applications concerning desired urban outcomes; • Prospects, implications, and impacts concerning the future of smart and sustainable cities.
Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications. This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.
During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.
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.