Data Analytics For Process Engineers
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Data Analytics For Process Engineers
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Author : Daniela Galatro
language : en
Publisher: Springer Nature
Release Date : 2024-01-20
Data Analytics For Process Engineers written by Daniela Galatro and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-20 with Science categories.
This book provides an industry-oriented data analytics approach for process engineers, including data acquisition methods and sources, exploratory data analysis and sensitivity analysis, data-based modelling for prediction, data-based modelling for monitoring and control, and data-based optimization of processes. While many of the current data analytics books target business-related problems, the rationale for this book is a specific need to understand and select applicable data analytics approaches pragmatically to analyze process engineering–related problems; this tailored solution for engineers gets amalgamated with governing equations, and in several cases, with the physical understanding of the phenomenon being analyzed. We also consider this book strategically conceived to help map Education 4.0 with Industry 4.0 since it can support undergraduate and graduate students to gain valuable and applicable data analytics stills that can be further used in their workplace. Moreover, it can be used as a reference book for professionals, a quick reference to data analytics tools that can facilitate and/or optimize their process engineering tasks.
Robust Quality
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Author : Rajesh Jugulum
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Robust Quality written by Rajesh Jugulum and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Business & Economics categories.
Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies. Features: Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution
Process Engineering Data Book
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Author : Nicholas P. Cheremisinoff
language : en
Publisher: CRC Press
Release Date : 1995-12-22
Process Engineering Data Book written by Nicholas P. Cheremisinoff and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-12-22 with Science categories.
This is a convenient, one-volume reference that provides process engineers with quick information on the major equipment, processes and materials used in chemical, food, water/wastewater, fuel and other types of process engineering. The data is presented in short articles, supplemented and illustrated by tables, diagrams, charts and formulas. The data is organized in twenty short chapters with a detailed index for easy reference. Much of the data is economically presented in tables.
Digital Transformation For The Process Industries
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Author : Osvaldo A. Bascur
language : en
Publisher: CRC Press
Release Date : 2020-10-27
Digital Transformation For The Process Industries written by Osvaldo A. Bascur and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-27 with Technology & Engineering categories.
Imagine if your process manufacturing plants were running so well that your production, safety, environmental, and profitability targets were being met so that your subject matter experts could focus on data-driven business improvements. Through proper use and analysis of your existing operations data, your company can become an industry leader and reward your stakeholders. Written in an engaging and easily understandable manner, this book demonstrates a step-by-step process of how an organization can effectively utilize technology and make the necessary culture changes to achieve operational excellence. You will see how several industry-leading companies have used an effective real-time data infrastructure for mission-critical business use cases. The book also addresses challenges involved, such as effectively integrating operational (OT) data with business (IT) systems to enable a more proactive, predictive management model for a fleet of process plants. Some of the things you will take away: Learn how a real-time data infrastructure enables transformation of raw sensor data into contextualized information for operational insights and business process improvement. Understand how reusing the same operational data for multiple use cases significantly impacts fleet management, profitability, and asset stewardship. See how a simple digital unit template representing production flows can be repeatedly used to identify critical inefficiencies in plant operations. Discover best practices of deploying real-time situational awareness alerts and predictive analytics. Realize how to transform your organization into a data-driven culture for continuous sustainable improvement. Find out how leading companies integrate operations data with business intelligence and predictive analytics tools in a corporate on-premises or cloud-enabled environment. Learn how industry-leading companies have imaginatively used a real-time data infrastructure to improve yields, reduce cycle times, and slash operating costs. This book is targeted for process industries production and operations leadership, senior engineers, IT management, CIOs, and service providers to those industries. Academics will benefit from latest data analysis strategies. This book guides readers to use the best, results-proven approaches to ensure operational excellence.
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
Advanced Analytics In Mining Engineering
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Author : Ali Soofastaei
language : en
Publisher: Springer Nature
Release Date : 2022-02-23
Advanced Analytics In Mining Engineering written by Ali Soofastaei and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-23 with Business & Economics categories.
In this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time. Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results. From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing. Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in your company’s data. The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries.
Process Engineering
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Author : Michael Kleiber
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2023-11-20
Process Engineering written by Michael Kleiber and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-20 with Technology & Engineering categories.
"Reading the book, you can feel the long practical experience of the author. The text is easy to read, even where concepts can be complex. The strong theoretical background of the author is well known from other publications. In this book, however, the topics are presented on a level that every engineer and scientist in the chemical industry and process industry should know and can understand... This book would have been very helpful at the beginning of my career to close the addressed gap. Therefore, I can strongly recommend it not only to all students close to their degree, but also to engineers and scientists just starting their industrial career in the related industrial sectors that are subsumed under the term process industry (chemical or petrochemical industry, pharmaceutical industry, food industry, biochemical industry, environmental technology, etc.). The book is like an investment. Doing a better job and getting a better job evaluation might pay for the book ..." Prof. Dr.-Ing. Claus Fleischer, Frankfurt University of Applied Sciences Process Engineering is based on almost 30 years of practical experience of the author in process simulation, design and development. The book is a missing link between students and practitioners. The author has coached many graduates in their first months and knows what the typical questions are. Coming from the university, graduates often do not know which relevance their knowledge has and how to apply it in real life, whereas established practitioners often stick to the narrow way of their experience, forgetting that science continuously makes progress. There is a gap to be bridged. From his own professional experience, the author covers many topics of the process engineering business, but three guest contributions are a valuable supplement to the content of the third edition. Already in the 2nd edition, Verena Haas from BASF SE wrote an excellent chapter on dynamic process simulation. For the new 3rd edition, Gökce Adali and Michael Benje added two chapters on digitalization and patents, respectively. Preparing the reader for the everyday business!
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 Process Control Engineers
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Author : Myke King
language : en
Publisher: John Wiley & Sons
Release Date : 2017-08-10
Statistics For Process Control Engineers written by Myke King 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 2017-08-10 with Technology & Engineering categories.
The first statistics guide focussing on practical application to process control design and maintenance Statistics for Process Control Engineers is the only guide to statistics written by and for process control professionals. It takes a wholly practical approach to the subject. Statistics are applied throughout the life of a process control scheme – from assessing its economic benefit, designing inferential properties, identifying dynamic models, monitoring performance and diagnosing faults. This book addresses all of these areas and more. The book begins with an overview of various statistical applications in the field of process control, followed by discussions of data characteristics, probability functions, data presentation, sample size, significance testing and commonly used mathematical functions. It then shows how to select and fit a distribution to data, before moving on to the application of regression analysis and data reconciliation. The book is extensively illustrated throughout with line drawings, tables and equations, and features numerous worked examples. In addition, two appendices include the data used in the examples and an exhaustive catalogue of statistical distributions. The data and a simple-to-use software tool are available for download. The reader can thus reproduce all of the examples and then extend the same statistical techniques to real problems. Takes a back-to-basics approach with a focus on techniques that have immediate, practical, problem-solving applications for practicing engineers, as well as engineering students Shows how to avoid the many common errors made by the industry in applying statistics to process control Describes not only the well-known statistical distributions but also demonstrates the advantages of applying the large number that are less well-known Inspires engineers to identify new applications of statistical techniques to the design and support of control schemes Provides a deeper understanding of services and products which control engineers are often tasked with assessing This book is a valuable professional resource for engineers working in the global process industry and engineering companies, as well as students of engineering. It will be of great interest to those in the oil and gas, chemical, pulp and paper, water purification, pharmaceuticals and power generation industries, as well as for design engineers, instrument engineers and process technical support.
Industrial Data Analytics For Diagnosis And Prognosis
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Author : Shiyu Zhou
language : en
Publisher: John Wiley & Sons
Release Date : 2021-07-21
Industrial Data Analytics For Diagnosis And Prognosis written by Shiyu Zhou 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 2021-07-21 with Mathematics categories.
Discover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model In Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach, distinguished engineers Shiyu Zhou and Yong Chen deliver a rigorous and practical introduction to the random effects modeling approach for industrial system diagnosis and prognosis. In the book’s two parts, general statistical concepts and useful theory are described and explained, as are industrial diagnosis and prognosis methods. The accomplished authors describe and model fixed effects, random effects, and variation in univariate and multivariate datasets and cover the application of the random effects approach to diagnosis of variation sources in industrial processes. They offer a detailed performance comparison of different diagnosis methods before moving on to the application of the random effects approach to failure prognosis in industrial processes and systems. In addition to presenting the joint prognosis model, which integrates the survival regression model with the mixed effects regression model, the book also offers readers: A thorough introduction to describing variation of industrial data, including univariate and multivariate random variables and probability distributions Rigorous treatments of the diagnosis of variation sources using PCA pattern matching and the random effects model An exploration of extended mixed effects model, including mixture prior and Kalman filtering approach, for real time prognosis A detailed presentation of Gaussian process model as a flexible approach for the prediction of temporal degradation signals Ideal for senior year undergraduate students and postgraduate students in industrial, manufacturing, mechanical, and electrical engineering, Industrial Data Analytics for Diagnosis and Prognosis is also an indispensable guide for researchers and engineers interested in data analytics methods for system diagnosis and prognosis.