Dynamic Modeling Of Complex Industrial Processes Data Driven Methods And Application Research

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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research

Chao Shang

Author : Chao Shang
Publisher : Springer
Page : 143 pages
File Size : 42,5 Mb
Release : 2018-02-22
Category : Technology & Engineering
ISBN : 9789811066771

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Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research by Chao Shang Pdf

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.

Data-Driven Fault Detection for Industrial Processes

Zhiwen Chen

Author : Zhiwen Chen
Publisher : Springer
Page : 112 pages
File Size : 40,5 Mb
Release : 2017-01-02
Category : Technology & Engineering
ISBN : 9783658167561

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Data-Driven Fault Detection for Industrial Processes by Zhiwen Chen Pdf

Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.

Data-Driven Prediction for Industrial Processes and Their Applications

Jun Zhao,Wei Wang,Chunyang Sheng

Author : Jun Zhao,Wei Wang,Chunyang Sheng
Publisher : Springer
Page : 443 pages
File Size : 51,5 Mb
Release : 2018-08-20
Category : Computers
ISBN : 9783319940519

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Data-Driven Prediction for Industrial Processes and Their Applications by Jun Zhao,Wei Wang,Chunyang Sheng Pdf

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

Dynamic Mode Decomposition

J. Nathan Kutz,Steven L. Brunton,Bingni W. Brunton,Joshua L. Proctor

Author : J. Nathan Kutz,Steven L. Brunton,Bingni W. Brunton,Joshua L. Proctor
Publisher : SIAM
Page : 241 pages
File Size : 48,9 Mb
Release : 2016-11-23
Category : Science
ISBN : 9781611974492

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Dynamic Mode Decomposition by J. Nathan Kutz,Steven L. Brunton,Bingni W. Brunton,Joshua L. Proctor Pdf

Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.

Dynamic Process Modeling

Anonim

Author : Anonim
Publisher : John Wiley & Sons
Page : 628 pages
File Size : 48,7 Mb
Release : 2013-10-02
Category : Technology & Engineering
ISBN : 9783527631346

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Dynamic Process Modeling by Anonim Pdf

Inspired by the leading authority in the field, the Centre for Process Systems Engineering at Imperial College London, this book includes theoretical developments, algorithms, methodologies and tools in process systems engineering and applications from the chemical, energy, molecular, biomedical and other areas. It spans a whole range of length scales seen in manufacturing industries, from molecular and nanoscale phenomena to enterprise-wide optimization and control. As such, this will appeal to a broad readership, since the topic applies not only to all technical processes but also due to the interdisciplinary expertise required to solve the challenge. The ultimate reference work for years to come.

Modeling and Control of Batch Processes

Prashant Mhaskar,Abhinav Garg,Brandon Corbett

Author : Prashant Mhaskar,Abhinav Garg,Brandon Corbett
Publisher : Springer
Page : 335 pages
File Size : 45,7 Mb
Release : 2018-11-28
Category : Technology & Engineering
ISBN : 9783030041403

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Modeling and Control of Batch Processes by Prashant Mhaskar,Abhinav Garg,Brandon Corbett Pdf

Modeling and Control of Batch Processes presents state-of-the-art techniques ranging from mechanistic to data-driven models. These methods are specifically tailored to handle issues pertinent to batch processes, such as nonlinear dynamics and lack of online quality measurements. In particular, the book proposes: a novel batch control design with well characterized feasibility properties; a modeling approach that unites multi-model and partial least squares techniques; a generalization of the subspace identification approach for batch processes; and applications to several detailed case studies, ranging from a complex simulation test bed to industrial data. The book’s proposed methodology employs statistical tools, such as partial least squares and subspace identification, and couples them with notions from state-space-based models to provide solutions to the quality control problem for batch processes. Practical implementation issues are discussed to help readers understand the application of the methods in greater depth. The book includes numerous comments and remarks providing insight and fundamental understanding into the modeling and control of batch processes. Modeling and Control of Batch Processes includes many detailed examples of industrial relevance that can be tailored by process control engineers or researchers to a specific application. The book is also of interest to graduate students studying control systems, as it contains new research topics and references to significant recent work. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Computational Science — ICCS 2004

Marian Bubak,Geert Dick van Albada,Peter M.A. Sloot,Jack Dongarra

Author : Marian Bubak,Geert Dick van Albada,Peter M.A. Sloot,Jack Dongarra
Publisher : Springer Science & Business Media
Page : 1376 pages
File Size : 50,5 Mb
Release : 2004-05-26
Category : Computers
ISBN : 9783540221166

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Computational Science — ICCS 2004 by Marian Bubak,Geert Dick van Albada,Peter M.A. Sloot,Jack Dongarra Pdf

The International Conference on Computational Science (ICCS 2004) held in Krak ́ ow, Poland, June 6–9, 2004, was a follow-up to the highly successful ICCS 2003 held at two locations, in Melbourne, Australia and St. Petersburg, Russia; ICCS 2002 in Amsterdam, The Netherlands; and ICCS 2001 in San Francisco, USA. As computational science is still evolving in its quest for subjects of inves- gation and e?cient methods, ICCS 2004 was devised as a forum for scientists from mathematics and computer science, as the basic computing disciplines and application areas, interested in advanced computational methods for physics, chemistry, life sciences, engineering, arts and humanities, as well as computer system vendors and software developers. The main objective of this conference was to discuss problems and solutions in all areas, to identify new issues, to shape future directions of research, and to help users apply various advanced computational techniques. The event harvested recent developments in com- tationalgridsandnextgenerationcomputingsystems,tools,advancednumerical methods, data-driven systems, and novel application ?elds, such as complex - stems, ?nance, econo-physics and population evolution.

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Jing Wang,Jinglin Zhou,Xiaolu Chen

Author : Jing Wang,Jinglin Zhou,Xiaolu Chen
Publisher : Springer Nature
Page : 277 pages
File Size : 40,7 Mb
Release : 2022-01-03
Category : Technology & Engineering
ISBN : 9789811680441

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Data-Driven Fault Detection and Reasoning for Industrial Monitoring by Jing Wang,Jinglin Zhou,Xiaolu Chen Pdf

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.

Data-Driven Design of Fault Diagnosis Systems

Adel Haghani Abandan Sari

Author : Adel Haghani Abandan Sari
Publisher : Springer Science & Business
Page : 136 pages
File Size : 42,5 Mb
Release : 2014-04-22
Category : Technology & Engineering
ISBN : 9783658058074

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Data-Driven Design of Fault Diagnosis Systems by Adel Haghani Abandan Sari Pdf

In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements.

Model Based Control

Paul Serban Agachi,Zoltán K. Nagy,Mircea Vasile Cristea,Árpád Imre-Lucaci

Author : Paul Serban Agachi,Zoltán K. Nagy,Mircea Vasile Cristea,Árpád Imre-Lucaci
Publisher : John Wiley & Sons
Page : 290 pages
File Size : 40,9 Mb
Release : 2007-09-24
Category : Technology & Engineering
ISBN : 9783527609222

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Model Based Control by Paul Serban Agachi,Zoltán K. Nagy,Mircea Vasile Cristea,Árpád Imre-Lucaci Pdf

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.

Modelling and Control of Dynamic Systems Using Gaussian Process Models

Juš Kocijan

Author : Juš Kocijan
Publisher : Springer
Page : 267 pages
File Size : 45,9 Mb
Release : 2015-11-21
Category : Technology & Engineering
ISBN : 9783319210216

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Modelling and Control of Dynamic Systems Using Gaussian Process Models by Juš Kocijan Pdf

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Process Modelling and Model Analysis

Ian T. Cameron,Katalin Hangos

Author : Ian T. Cameron,Katalin Hangos
Publisher : Elsevier
Page : 543 pages
File Size : 50,9 Mb
Release : 2001-05-23
Category : Technology & Engineering
ISBN : 9780080514925

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Process Modelling and Model Analysis by Ian T. Cameron,Katalin Hangos Pdf

Process Modelling and Model Analysis describes the use of models in process engineering. Process engineering is all about manufacturing--of just about anything! To manage processing and manufacturing systematically, the engineer has to bring together many different techniques and analyses of the interaction between various aspects of the process. For example, process engineers would apply models to perform feasibility analyses of novel process designs, assess environmental impact, and detect potential hazards or accidents. To manage complex systems and enable process design, the behavior of systems is reduced to simple mathematical forms. This book provides a systematic approach to the mathematical development of process models and explains how to analyze those models. Additionally, there is a comprehensive bibliography for further reading, a question and answer section, and an accompanying Web site developed by the authors with additional data and exercises. Introduces a structured modeling methodology emphasizing the importance of the modeling goal and including key steps such as model verification, calibration, and validation Focuses on novel and advanced modeling techniques such as discrete, hybrid, hierarchical, and empirical modeling Illustrates the notions, tools, and techniques of process modeling with examples and advances applications

Data-Driven Modeling for Additive Manufacturing of Metals

National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics

Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics
Publisher : National Academies Press
Page : 79 pages
File Size : 42,5 Mb
Release : 2019-11-09
Category : Technology & Engineering
ISBN : 9780309494205

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Data-Driven Modeling for Additive Manufacturing of Metals by National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics Pdf

Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.

Data-Driven Scheduling of Semiconductor Manufacturing Systems

Li Li,Qingyun Yu,Kuo-Yi Lin,Yumin Ma,Fei Qiao

Author : Li Li,Qingyun Yu,Kuo-Yi Lin,Yumin Ma,Fei Qiao
Publisher : Springer Nature
Page : 276 pages
File Size : 47,7 Mb
Release : 2023-05-20
Category : Technology & Engineering
ISBN : 9789811975882

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Data-Driven Scheduling of Semiconductor Manufacturing Systems by Li Li,Qingyun Yu,Kuo-Yi Lin,Yumin Ma,Fei Qiao Pdf

This book systematically discusses the intelligent scheduling problem of complex semiconductor manufacturing systems from theory to method and then to application. The main contents include data-driven scheduling framework of semiconductor manufacturing system, data preprocessing of semiconductor manufacturing system, correlation analysis of performance index of semiconductor production line, intelligent release control strategy, dynamic dispatching rules simulating pheromone mechanism, and load balancing dynamic scheduling of semiconductor production line, performance index-driven dynamic scheduling method of semiconductor production line, scheduling trend of semi-conductor manufacturing system in big data environment. This book aims to provide readers with valuable reference and assistance in the theoretical methods, techniques, and application cases of semiconductor manufacturing systems and their intelligent scheduling.

Methods to Assess and Manage Process Safety in Digitalized Process System

Faisal Khan

Author : Faisal Khan
Publisher : Academic Press
Page : 670 pages
File Size : 51,9 Mb
Release : 2022-07-06
Category : Technology & Engineering
ISBN : 9780323988988

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Methods to Assess and Manage Process Safety in Digitalized Process System by Faisal Khan Pdf

Methods to Assess and Manage Process Safety in Digitalized Process System, Volume Six, the latest release in the Methods in Chemical Process Safety series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Methods in Chemical Process Safety series Provides the authority and expertise of leading contributors from an international board of authors

Dynamic Modeling Of Complex Industrial Processes Data Driven Methods And Application Research

Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research by Chao Shang Pdf

Data-Driven Fault Detection for Industrial Processes by Zhiwen Chen Pdf

Data-Driven Prediction for Industrial Processes and Their Applications by Jun Zhao,Wei Wang,Chunyang Sheng Pdf

Dynamic Mode Decomposition by J. Nathan Kutz,Steven L. Brunton,Bingni W. Brunton,Joshua L. Proctor Pdf

Dynamic Process Modeling by Anonim Pdf

Modeling and Control of Batch Processes by Prashant Mhaskar,Abhinav Garg,Brandon Corbett Pdf

Computational Science — ICCS 2004 by Marian Bubak,Geert Dick van Albada,Peter M.A. Sloot,Jack Dongarra Pdf

Data-Driven Fault Detection and Reasoning for Industrial Monitoring by Jing Wang,Jinglin Zhou,Xiaolu Chen Pdf

Data-Driven Design of Fault Diagnosis Systems by Adel Haghani Abandan Sari Pdf

Model Based Control by Paul Serban Agachi,Zoltán K. Nagy,Mircea Vasile Cristea,Árpád Imre-Lucaci Pdf

Modelling and Control of Dynamic Systems Using Gaussian Process Models by Juš Kocijan Pdf

Process Modelling and Model Analysis by Ian T. Cameron,Katalin Hangos Pdf

Data-Driven Modeling for Additive Manufacturing of Metals by National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,National Materials and Manufacturing Board,Board on Mathematical Sciences and Analytics Pdf

Data-Driven Scheduling of Semiconductor Manufacturing Systems by Li Li,Qingyun Yu,Kuo-Yi Lin,Yumin Ma,Fei Qiao Pdf

Methods to Assess and Manage Process Safety in Digitalized Process System by Faisal Khan Pdf

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