Download or read book A Novel Back Off Algorithm for the Integration Between Dynamic Optimization and Scheduling of Batch Processes Under Uncertainty written by Yael Izamal Valdez Navarro and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a decomposition algorithm for obtaining robust scheduling and control decisions. It iteratively solves scheduling and dynamic optimization problems while approximating stochastic uncertainty through back-off terms, calculated through dynamic simulations of the process. This algorithm is compared, both in solution quality and performance, against a fully-integrated MINLP.
Download or read book Robust Dynamic Optimization and Control of Semi batch Processes Under Parametric Uncertainties written by Jennifer Puschke and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Planning and Scheduling of Batch Processes Under Uncertainty written by Peter Michael Verderame and published by . This book was released on 2011 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Processing Batch Design and Scheduling Under Uncertainty written by Tarun Bhatia and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Optimization of Batch Process Operations with Imperfect Modeling written by Peter Terwiesch and published by . This book was released on 1994 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Optimization of Batch Process Operations with Imperfekt Modeling written by Peter Terwiesch and published by . This book was released on 1994 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Batch Process Design written by P.N. Sharratt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Batch processes are used to manufacture many fine organic chemicals, and as such they can be considered to underpin much of the modern chemical industry. Despite widespread use and a consequent huge contribution to wealth creation, batch processes have attracted limited attention outside the user industries. Batch chemicals processing uses a number of core techniques and technologies, such as scheduling and sequence control, agitation and batch filtration. The combination of these technologies with often complex chemistry, the multi-purpose nature of much of this type of plant, the distinctive safety and environmental issues, and a fast moving commercial environment makes the development of a successful batch process a considerable challenge for the chemist or engineer. The literature on the topics covered in this book is fragmented and often not easily accessible, so this handbook has been written to address this problem and to bring together design and process analysis methods in the core areas of batch process design. By combining the science and pragmatism required in the development of successful batch processes this new book provides answers to real problems in an accessible and concise way. Written by an international team of authors drawn from industry, consulting and academe, this book is an essential part of the library of any chemist, technologist or engineer working on the development of new or existing batch processes.
Download or read book Integration of Batch Processes Subject to Uncertainty written by R. Gonzalez and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Chemical Production Scheduling written by Christos T. Maravelias and published by Cambridge University Press. This book was released on 2021-05-06 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.
Download or read book Dynamics and Nonlinear Control of Integrated Process Systems written by Michael Baldea and published by Cambridge University Press. This book was released on 2012-08-02 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: A theoretical and practical guide to reducing model complexity and achieving tight control of modern integrated plants.
Download or read book Ant Colony Optimization written by Marco Dorigo and published by MIT Press. This book was released on 2004-06-04 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
Download or read book Real Time Optimization written by Dominique Bonvin and published by MDPI. This book was released on 2018-07-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Real-Time Optimization" that was published in Processes
Download or read book Integration of Planning and Scheduling for Batch and Continuous Process Systems written by Muge Erdirik Dogan and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Algorithms for Optimization written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2019-03-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Download or read book Introduction to Applied Optimization written by Urmila Diwekar and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents a multi-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessive theoretical detail. This second edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter. Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.
