Integrated Process Modeling Advanced Control And Data Analytics For Optimizing Polyolefin Manufacturing
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Integrated Process Modeling Advanced Control And Data Analytics For Optimizing Polyolefin Manufacturing
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Author : Y. A. Liu
language : en
Publisher: John Wiley & Sons
Release Date : 2023-07-25
Integrated Process Modeling Advanced Control And Data Analytics For Optimizing Polyolefin Manufacturing written by Y. A. Liu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-25 with Technology & Engineering categories.
Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing Detailed resource on the “Why,” “What,” and “How” of integrated process modeling, advanced control and data analytics explained via hands-on examples and workshops for optimizing polyolefin manufacturing. Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing discusses, as well as demonstrates, the optimization of polyolefin production by covering topics from polymer process modeling and advanced process control to data analytics and machine learning, and sustainable design and industrial practice. The text also covers practical problems, handling of real data streams, developing the right level of detail, and tuning models to the available data, among other topics, to allow for easy translation of concepts into practice. Written by two highly qualified authors, Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing includes information on: Segment-based modeling of polymer processes; selection of thermodynamic methods; estimation of physical properties for polymer process modeling Reactor modeling, convergence tips and data-fit tool; free radical polymerization (LDPE, EVA and PS), Ziegler-Natta polymerization (HDPE, PP, LLPDE, and EPDM) and ionic polymerization (SBS rubber) Improved polymer process operability and control through steady-state and dynamic simulation models Model-predictive control of polyolefin processes and applications of multivariate statistics and machine learning to optimizing polyolefin manufacturing Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing enables readers to make full use of advanced computer models and latest data analytics and machine learning tools for optimizing polyolefin manufacturing, making it an essential resource for undergraduate and graduate students, researchers, and new and experienced engineers involved in the polyolefin industry.
Integrated Process Modeling Advanced Control And Data Analytics For Optimizing Polyolefin Manufacturing
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Author : Yih An Liu
language : en
Publisher:
Release Date : 2023
Integrated Process Modeling Advanced Control And Data Analytics For Optimizing Polyolefin Manufacturing written by Yih An Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Polyolefin industry categories.
Detailed resource on the “Why,” “What,” and “How” of integrated process modeling, advanced control and data analytics explained via hands-on examples and workshops for optimizing polyolefin manufacturing. Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing discusses, as well as demonstrates, the optimization of polyolefin production by covering topics from polymer process modeling and advanced process control to data analytics and machine learning, and sustainable design and industrial practice. The text also covers practical problems, handling of real data streams, developing the right level of detail, and tuning models to the available data, among other topics, to allow for easy translation of concepts into practice. Written by two highly qualified authors, Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing includes information on: Segment-based modeling of polymer processes; selection of thermodynamic methods; estimation of physical properties for polymer process modeling; Reactor modeling, convergence tips and data-fit tool; free radical polymerization (LDPE, EVA and PS), Ziegler-Natta polymerization (HDPE, PP, LLPDE, and EPDM) and ionic polymerization (SBS rubber); Improved polymer process operability and control through steady-state and dynamic simulation models; Model-predictive control of polyolefin processes and applications of multivariate statistics and machine learning to optimizing polyolefin manufacturing. Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing enables readers to make full use of advanced computer models and latest data analytics and machine learning tools for optimizing polyolefin manufacturing, making it an essential resource for undergraduate and graduate students, researchers, and new and experienced engineers involved in the polyolefin industry.
Digitalization And Analytics For Smart Plant Performance
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Author : Frank (Xin X.) Zhu
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-06
Digitalization And Analytics For Smart Plant Performance written by Frank (Xin X.) Zhu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-06 with Technology & Engineering categories.
This book addresses the topic of integrated digitization of plants on an objective basis and in a holistic manner by sharing data, applying analytics tools and integrating workflows via pertinent examples from industry. It begins with an evaluation of current performance management practices and an overview of the need for a "Connected Plant" via digitalization followed by sections on "Connected Assets: Improve Reliability and Utilization," "Connected Processes: Optimize Performance and Economic Margin " and "Connected People: Digitalizing the Workforce and Workflows and Developing Ownership and Digital Culture," then culminating in a final section entitled "Putting All Together Into an Intelligent Digital Twin Platform for Smart Operations and Demonstrated by Application cases."
Refinery Engineering
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Author : Ai-Fu Chang
language : en
Publisher: John Wiley & Sons
Release Date : 2013-03-01
Refinery Engineering written by Ai-Fu Chang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-01 with Technology & Engineering categories.
A pioneering and comprehensive introduction to the complex subject of integrated refinery process simulation, using many of the tools and techniques currently employed in modern refineries. Adopting a systematic and practical approach, the authors include the theory, case studies and hands-on workshops, explaining how to work with real data. As a result, senior-level undergraduate and graduate students, as well as industrial engineers learn how to develop and use the latest computer models for the predictive modeling and optimization of integrated refinery processes. Additional material is available online providing relevant spreadsheets and simulation files for all the models and examples presented in the book.
New Directions In Bioprocess Modeling And Control
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Author : Michael A. Boudreau
language : en
Publisher: ISA
Release Date : 2007
New Directions In Bioprocess Modeling And Control written by Michael A. Boudreau and has been published by ISA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Science categories.
Models offer benefits even before they are put on line. Based on years of experience, the authors reveal in New Directions in Bioprocess Modeling and Control that significant improvements can result from the process knowledge and insight that are gained when building experimental and first-principle models for process monitoring and control. Doing modeling in the process development and early commercialization phases is advantageous because it increases process efficiency and provides ongoing opportunities for improving process control. This technology is important for maximizing benefits from analyzers and control tool investments. If you are a process design, quality control, information systems, or automation engineer in the biopharmaceutical, brewing, or bio-fuel industry, this handy resource will help you define, develop, and apply a virtual plant, model predictive control, first-principle models, neural networks, and multivariate statistical process control. The synergistic knowledge discovery on bench top or pilot plant scale can be ported to industrial scale processes. This learning process is consistent with the intent in the Process Analyzer and Process Control Tools sections of the FDA_s Guidance for Industry PAT _ A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. It states in the Process Analyzer section of the FDA_s guidance: _For certain applications, sensor-based measurements can provide a useful process signature that may be related to the underlying process steps or transformations. Based on the level of process understanding these signatures may also be useful for the process monitoring, control, and end point determination when these patterns or signatures relate to product and process quality._
Introduction To Process Control
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Author : Jose A. Romagnoli
language : en
Publisher: CRC Press
Release Date : 2020-07
Introduction To Process Control written by Jose A. Romagnoli and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07 with Science categories.
Introduction to Process Control, Third Edition continues to provide a bridge between traditional and modern views of process control by blending conventional topics with a broader perspective of integrated process operation, control, and information systems. Updated and expanded throughout, this third edition addresses issues highly relevant to today's teaching of process control: Discusses smart manufacturing, new data preprocessing techniques, and machine learning and artificial intelligence concepts that are part of current smart manufacturing decisions Includes extensive references to guide the reader to the resources needed to solve modeling, classification, and monitoring problems Introduces the link between process optimization and process control (optimizing control), including the effect of disturbances on the optimal plant operation, the concepts of steady-state and dynamic back-off as ways to quantify the economic benefits of control, and how to determine an optimal transition policy during a planned production change Incorporates an introduction to the modern architectures of industrial computer control systems with real case studies and applications to pilot-scale operations Analyzes the expanded role of process control in modern manufacturing, including model-centric technologies and integrated control systems Integrates data processing/reconciliation and intelligent monitoring in the overall control system architecture Drawing on the authors' combined 60 years of teaching experiences, this classroom-tested text is designed for chemical engineering students but is also suitable for industrial practitioners who need to understand key concepts of process control and how to implement them. The text offers a comprehensive pedagogical approach to reinforce learning and presents a concept first followed by an example, allowing students to grasp theoretical concepts in a practical manner and uses the same problem in each chapter, culminating in a complete control design strategy. A vast number of exercises throughout ensure readers are supported in their learning and comprehension. Downloadable MATLAB® toolboxes for process control education as well as the main simulation examples from the book offer a user-friendly software environment for interactively studying the examples in the text. These can be downloaded from the publisher's website. Solutions manual is available for qualifying professors from the publisher.
Petroleum Refinery Process Modeling
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Author : Y. A. Liu
language : en
Publisher: John Wiley & Sons
Release Date : 2018-06-05
Petroleum Refinery Process Modeling written by Y. A. Liu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-05 with Technology & Engineering categories.
A comprehensive review of the theory and practice of the simulation and optimization of the petroleum refining processes Petroleum Refinery Process Modeling offers a thorough review of how to quantitatively model key refinery reaction and fractionation processes. The text introduces the basics of dealing with the thermodynamics and physical property predictions of hydrocarbon components in the context of process modeling. The authors - three experts on the topic - outline the procedures and include the key data required for building reaction and fractionation models with commercial software. The text shows how to filter through the extensive data available at the refinery and using plant data to begin calibrating available models and extend the models to include key fractionation sub-models. It provides a sound and informed basis to understand and exploit plant phenomena to improve yield, consistency, and performance. In addition, the authors offer information on applying models in an overall refinery context through refinery planning based on linear programming. This important resource: -Offers the basic information of thermodynamics and physical property predictions of hydrocarbon components in the context of process modeling -Uses the key concepts of fractionation lumps and physical properties to develop detailed models and workflows for atmospheric (CDU) and vacuum (VDU) distillation units -Discusses modeling FCC, catalytic reforming and hydroprocessing units Written for chemical engineers, process engineers, and engineers for measurement and control, this resource explores the advanced simulation tools and techniques that are available to support experienced and aid new operators and engineers.
Dynamic Modeling Of Complex Industrial Processes Data Driven Methods And Application Research
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Author : Chao Shang
language : en
Publisher: Springer
Release Date : 2019-03-19
Dynamic Modeling Of Complex Industrial Processes Data Driven Methods And Application Research written by Chao Shang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-19 with Technology & Engineering categories.
This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.
Integrated Process Design And Operational Optimization Via Multiparametric Programming
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Author : Baris Burnak
language : en
Publisher:
Release Date : 2020-09-04
Integrated Process Design And Operational Optimization Via Multiparametric Programming written by Baris Burnak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-04 with categories.
This book presents a comprehensive optimization-based theory and framework that exploits the synergistic interactions and tradeoffs between process design and operational decisions that span different time scales. Conventional methods in the process industry often isolate decision making mechanisms with a hierarchical information flow to achieve tractable problems, risking suboptimal, even infeasible operations. In this book, foundations of a systematic model-based strategy for simultaneous process design, scheduling, and control optimization is detailed to achieve reduced cost and improved energy consumption in process systems. The material covered in this book is well suited for the use of industrial practitioners, academics, and researchers. In Chapter 1, a historical perspective on the milestones in model-based design optimization techniques is presented along with an overview of the state-of-the-art mathematical tools to solve the resulting complex problems. Chapters 2 and 3 discuss two fundamental concepts that are essential for the reader. These concepts are (i) mixed integer dynamic optimization problems and two algorithms to solve this class of optimization problems, and (ii) developing a model based multiparametric programming model predictive control. These tools are used to systematically evaluate the tradeoffs between different time-scale decisions based on a single high-fidelity model, as demonstrated on (i) design and control, (ii) scheduling and control, and (iii) design, scheduling, and control problems. We present illustrative examples on chemical processing units, including continuous stirred tank reactors, distillation columns, and combined heat and power regeneration units, along with discussions of other relevant work in the literature for each class of problems.
Model Based Control
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Author : Paul Serban Agachi
language : en
Publisher: Wiley-VCH
Release Date : 2006-11-10
Model Based Control written by Paul Serban Agachi and has been published by Wiley-VCH this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-10 with Science categories.
Filling a gap in the literature for a practical approach to the topic, this book is unique in including a whole section of case studies presenting a wide range of applications from polymerization reactors and bioreactors, to distillation column and complex fluid catalytic cracking units. A section of general tuning guidelines of MPC is also present.These thus aid readers in facilitating the implementation of MPC in process engineering and automation. At the same time many theoretical, computational and implementation aspects of model-based control are explained, with a look at both linear and nonlinear model predictive control. Each chapter presents details related to the modeling of the process as well as the implementation of different model-based control approaches, and there is also a discussion of both the dynamic behaviour and the economics of industrial processes and plants. The book is unique in the broad coverage of different model based control strategies and in the variety of applications presented. A special merit of the book is in the included library of dynamic models of several industrially relevant processes, which can be used by both the industrial and academic community to study and implement advanced control strategies. Zoltán K. Nagy received his PhD from Babes-Bolyai University of Cluj, where he worked as a lecturer until 2005. Before taking up his current appointment as a faculty member at Loughborough University, UK, he was NATO research fellow and visiting lecturer at the University of Illinois at Urbana-Champaign, and research associate at the University of Stuttgart, University of Heidelberg and ETH Zürich. His main research interest is in the model based control and optimization of chemical processes. He worked on industrial implementation of model-based control strategies with companies such as BASF and ABB, and has published over 80 papers in the field. Arpad Imre-Lucaci received his M.S. and Ph.D. degrees in chemical engineering from Babes-Bolyai University of Cluj-Napoca in 1985 and 1999, respectively. Since 1988 he has worked in the Chemical Engineering Department of BBU Cluj-Napoca, Romania, and spent research stays at University of Stuttgart (1994) and ETH Zürich (in 2002 and 2003). His main research fields are mathematical modeling, simulation and optimization in process industries, on which he has published over 20 scientific papers. Cristea Vasile Mircea graduated the Faculty of Electrotechnics, Romania, with specialization on process control and computer science and holds a Ph.D. degree in process control. After 8 years spent in industry he is at present Associate Professor at Babes-Bolyai University, Cluj-Napoca; his interests lie in systems theory, chemical process control, advanced process control, data acquisition and control, linear and nonlinear model based predictive control, and fuzzy control. He was director of CNCSIS Projects and has published 3 books as well as over 55 scientific papers. Professor Paul Serban Agachi graduated in 1970 in Control Engineering at the Politehnica University of Bucharest. Obtained his Ph.D. in Chemical Engineering from the University Petroleum & Gas Ploiesti, Romania. Professional experience: design engineer, system analyst, researcher in fuel cells, process modeling, optimization and control. At present, professor of Process Control at the Department of Chemical Engineering of Babes-Bolyai University, Cluj-Napoca and member of the Academy of Technical Sciences of Romania. He has been visiting associate at California Institute of Technology, invited professor at Eötvös Lorand University, UNESCO Higher Education consultant. He has published 8 books and 96 scientific papers.