Advanced Data Analysis And Modelling In Chemical Engineering


Advanced Data Analysis And Modelling In Chemical Engineering
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Advanced Data Analysis And Modelling In Chemical Engineering


Advanced Data Analysis And Modelling In Chemical Engineering
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Author : Denis Constales
language : en
Publisher: Elsevier
Release Date : 2016-08-23

Advanced Data Analysis And Modelling In Chemical Engineering written by Denis Constales and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-23 with Technology & Engineering categories.


Advanced Data Analysis and Modeling in Chemical Engineering provides the mathematical foundations of different areas of chemical engineering and describes typical applications. The book presents the key areas of chemical engineering, their mathematical foundations, and corresponding modeling techniques. Modern industrial production is based on solid scientific methods, many of which are part of chemical engineering. To produce new substances or materials, engineers must devise special reactors and procedures, while also observing stringent safety requirements and striving to optimize the efficiency jointly in economic and ecological terms. In chemical engineering, mathematical methods are considered to be driving forces of many innovations in material design and process development. Presents the main mathematical problems and models of chemical engineering and provides the reader with contemporary methods and tools to solve them Summarizes in a clear and straightforward way, the contemporary trends in the interaction between mathematics and chemical engineering vital to chemical engineers in their daily work Includes classical analytical methods, computational methods, and methods of symbolic computation Covers the latest cutting edge computational methods, like symbolic computational methods



Integrated Process Modeling Advanced Control And Data Analytics For Optimizing Polyolefin Manufacturing


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.



Empirical Modeling And Data Analysis For Engineers And Applied Scientists


Empirical Modeling And Data Analysis For Engineers And Applied Scientists
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Author : Scott A. Pardo
language : en
Publisher: Springer
Release Date : 2016-07-19

Empirical Modeling And Data Analysis For Engineers And Applied Scientists written by Scott A. Pardo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-19 with Mathematics categories.


This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation) Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process. Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter. The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.



Statistics For Chemical And Process Engineers


Statistics For Chemical And Process Engineers
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Author : Yuri A.W. Shardt
language : en
Publisher: Springer Nature
Release Date : 2022-01-04

Statistics For Chemical And Process Engineers written by Yuri A.W. Shardt and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-04 with Science categories.


A coherent, concise, and comprehensive course in the statistics needed for a modern career in chemical engineering covers all of the concepts required for the American Fundamentals of Engineering Examination. Statistics for Chemical and Process Engineers (second edition) shows the reader how to develop and test models, design experiments and analyze data in ways easily applicable through readily available software tools like MS Excel® and MATLAB® and is updated for the most recent versions of both. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text, and it now contains an introduction to the use of state-space methods. The reader is given a detailed framework for statistical procedures covering: data visualization; probability; linear and nonlinear regression; experimental design (including factorial and fractional factorial designs); and dynamic process identification. Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB are also available for download. With its integrative approach to system identification, regression, and statistical theory, this book provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries, and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.



Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches


Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches
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Author : Fouzi Harrou
language : en
Publisher: Elsevier
Release Date : 2020-07-03

Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches written by Fouzi Harrou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Technology & Engineering categories.


Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods



Process Modelling And Model Analysis


Process Modelling And Model Analysis
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Author : Ian T. Cameron
language : en
Publisher: Elsevier
Release Date : 2001-05-23

Process Modelling And Model Analysis written by Ian T. Cameron and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-05-23 with Technology & Engineering categories.


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



Applying Multiple Reaction Stoichiometry To Chemical Reactor Modelling


Applying Multiple Reaction Stoichiometry To Chemical Reactor Modelling
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Author : Guillermo Fernando Barreto
language : en
Publisher: Springer Nature
Release Date :

Applying Multiple Reaction Stoichiometry To Chemical Reactor Modelling written by Guillermo Fernando Barreto and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.




Advances In Polymer Reaction Engineering


Advances In Polymer Reaction Engineering
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Author :
language : en
Publisher: Academic Press
Release Date : 2020-10-31

Advances In Polymer Reaction Engineering written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-31 with Technology & Engineering categories.


Advances in Polymer Reaction Engineering, Volume 56 in the Advances in Chemical Engineering series is aimed at reporting the latest advances in the field of polymer synthesis. Chapters in this new release include Polymer reaction engineering and composition control in free radical copolymers, Reactor control and on-line process monitoring in free radical emulsion polymerization, Exploiting pulsed laser polymerization to retrieve intrinsic kinetic parameters in radical polymerization, 3D printing in chemical engineering, Renewable source monomers in waterborne polymer dispersions, Importance of models and digitalization in Polymer Reaction Engineering, Recent Advances in Modelling of Radical Polymerization, and more. Covers recent advances in the control and monitoring of polymerization processes and in reactor configurations Provides modelling of polymerization reactions and up-to-date approaches to estimate reaction rate constants Includes authoritative opinions from experts in academia and industry



Advanced Chemical Kinetics


Advanced Chemical Kinetics
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Author : Muhammad Akhyar Farrukh
language : en
Publisher: BoD – Books on Demand
Release Date : 2018-02-21

Advanced Chemical Kinetics written by Muhammad Akhyar Farrukh and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-21 with Science categories.


The book on Advanced Chemical Kinetics gives insight into different aspects of chemical reactions both at the bulk and nanoscale level and covers topics from basic to high class. This book has been divided into three sections: (i) "Kinetics Modeling and Mechanism," (ii) "Kinetics of Nanomaterials," and (iii) "Kinetics Techniques." The first section consists of six chapters with a variety of topics like activation energy and complexity of chemical reactions; the measurement of reaction routes; mathematical modeling analysis and simulation of enzyme kinetics; mechanisms of homogeneous charge compression ignition combustion for the fuels; photophysical processes and photochemical changes; the mechanism of hydroxyl radical, hydrate electron, and hydrogen atom; and acceptorless alcohol dehydrogenation. The understanding of the kinetics of nanomaterials, to bridge the knowledge gap, is presented in the second section. The third section highlights an overview of experimental techniques used to study the mechanism of reactions.



Dynamic Model Development Methods Theory And Applications


Dynamic Model Development Methods Theory And Applications
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Author : S. Macchietto
language : en
Publisher: Elsevier
Release Date : 2003-08-04

Dynamic Model Development Methods Theory And Applications written by S. Macchietto and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-04 with Technology & Engineering categories.


Detailed mathematical models are increasingly being used by companies to gain competitive advantage through such applications as model-based process design, control and optimization. Thus, building various types of high quality models for processing systems has become a key activity in Process Engineering. This activity involves the use of several methods and techniques including model solution techniques, nonlinear systems identification, model verification and validation, and optimal design of experiments just to name a few. In turn, several issues and open-ended problems arise within these methods, including, for instance, use of higher-order information in establishing parameter estimates, establishing metrics for model credibility, and extending experiment design to the dynamic situation. The material covered in this book is aimed at allowing easier development and full use of detailed and high fidelity models. Potential applications of these techniques in all engineering disciplines are abundant, including applications in chemical kinetics and reaction mechanism elucidation, polymer reaction engineering, and physical properties estimation. On the academic side, the book will serve to generate research ideas. Contains wide coverage of statistical methods applied to process modelling Serves as a recent compilation of dynamic model building tools Presents several examples of applying advanced statistical and modelling methods to real process systems problems