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Advanced Data Analysis and Modelling in Chemical Engineering

Advanced Data Analysis and Modelling in Chemical Engineering
Author: Denis Constales
Publisher: Elsevier
Total Pages: 416
Release: 2016-08-23
Genre: Technology & Engineering
ISBN: 0444594841
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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 Process Data Analytics

Advanced Process Data Analytics
Author: Weike Sun (Ph. D.)
Publisher:
Total Pages: 498
Release: 2020
Genre:
ISBN:
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Process data analytics is the application of statistics and related mathematical tools to data in order to understand, develop, and improve manufacturing processes. There have been growing opportunities in process data analytics because of advances in machine learning and technologies for data collection and storage. However, challenges are encountered because of the complexities of manufacturing processes, which often require advanced analytical methods. In this thesis, two areas of application are considered. One is the construction of predictive models that are useful for process design, optimization, and control. The other area of application is process monitoring to improve process efficiency and safety. In the first area of study, a robust and automated approach for method selection and model construction is developed for predictive modeling. Two common challenges when building data-driven process models are addressed: the high diversity in data quality and how to select from a wide variety of methods. The proposed approach combines best practices with data interrogation to facilitate consistent application and continuous improvement of tools and decision making. The second area of study focuses on process monitoring for complex manufacturing systems, which includes fault detection, identification, and classification. Four sets of algorithms are developed to address limitations of traditional monitoring methods. The first set provides the optimal strategy for Gaussian linear processes, including deep understanding of the process monitoring structure and optimal fault detection based on a probabilistic formulation. The second set aims at building a self-learning fault detection system for changing normal operating conditions. The third set is developed based on information-theoretic learning to address limitations of second-order statistical learning for both fault detection and classification. The fourth set tackles the problem of nonlinear and dynamic process monitoring. The proposed methodologies and algorithms are tested on several case studies where the value of advanced process data analytics is demonstrated.

Advanced Process Engineering Control

Advanced Process Engineering Control
Author: Paul Serban Agachi
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 440
Release: 2023-11-20
Genre: Technology & Engineering
ISBN: 3110789736
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As a mature topic in chemical engineering, the book provides methods, problems and tools used in process control engineering. It discusses: process knowledge, sensor system technology, actuators, communication technology, and logistics, design and construction of control systems and their operation. The knowledge goes beyond the traditional process engineering field by applying the same principles, to biomedical processes, energy production and management of environmental issues. The book explains all the determinations in the "chemical systems" or "process systems", starting from the beginning of the processes, going through the intricate interdependency of the process stages, analyzing the hardware components of a control system and ending with the design of an appropriate control system for a process parameter or a whole process. The book is first addressed to the students and graduates of the departments of Chemical or Process Engineering. Second, to the chemical or process engineers in all industries or research and development centers, because they will notice the resemblance in approach from the system and control point of view, between different fields which might seem far from each other, but share the same control philosophy.

Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing

Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing
Author: Y. A. Liu
Publisher: John Wiley & Sons
Total Pages: 1027
Release: 2023-07-25
Genre: Technology & Engineering
ISBN: 3527843825
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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.

Applied Business Analytics

Applied Business Analytics
Author: Nathaniel Lin
Publisher: Pearson Education
Total Pages: 321
Release: 2015
Genre: Business & Economics
ISBN: 0133481506
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Now that you've collected the data and crunched the numbers, what do you do with all this information? How do you take the fruit of your analytics labor and apply it to business decision making? How do you actually apply the information gleaned from quants and tech teams? Applied Business Analytics will help you find optimal answers to these questions, and bridge the gap between analytics and execution in your organization. Nathaniel Lin explains why "analytics value chains" often break due to organizational and cultural issues, and offers "in the trenches" guidance for overcoming these obstacles. You'll learn why a special breed of "analytics deciders" is indispensable for any organization that seeks to compete on analytics; how to become one of those deciders; and how to identify, foster, support, empower, and reward others who join you. Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at every level: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes: How analytical and conventional decision making differ -- and the challenging implications How to determine who your analytics deciders are, and ought to be Proven best practices for actually applying analytics to decision-making How to optimize your use of analytics as an analyst, manager, executive, or C-level officer

Advanced Deep Learning Applications in Big Data Analytics

Advanced Deep Learning Applications in Big Data Analytics
Author: Bouarara, Hadj Ahmed
Publisher: IGI Global
Total Pages: 351
Release: 2020-10-16
Genre: Computers
ISBN: 1799827933
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Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

Advanced Data Analytics for Power Systems

Advanced Data Analytics for Power Systems
Author: Ali Tajer
Publisher: Cambridge University Press
Total Pages: 601
Release: 2021-04-08
Genre: Computers
ISBN: 1108494757
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Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.

Advanced Data Science and Analytics with Python

Advanced Data Science and Analytics with Python
Author: Jesus Rogel-Salazar
Publisher: CRC Press
Total Pages: 424
Release: 2020-05-05
Genre: Business & Economics
ISBN: 0429822324
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Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Smart Log Data Analytics

Smart Log Data Analytics
Author: Florian Skopik
Publisher: Springer Nature
Total Pages: 210
Release: 2021-08-28
Genre: Computers
ISBN: 3030744507
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This book provides insights into smart ways of computer log data analysis, with the goal of spotting adversarial actions. It is organized into 3 major parts with a total of 8 chapters that include a detailed view on existing solutions, as well as novel techniques that go far beyond state of the art. The first part of this book motivates the entire topic and highlights major challenges, trends and design criteria for log data analysis approaches, and further surveys and compares the state of the art. The second part of this book introduces concepts that apply character-based, rather than token-based, approaches and thus work on a more fine-grained level. Furthermore, these solutions were designed for “online use”, not only forensic analysis, but also process new log lines as they arrive in an efficient single pass manner. An advanced method for time series analysis aims at detecting changes in the overall behavior profile of an observed system and spotting trends and periodicities through log analysis. The third part of this book introduces the design of the AMiner, which is an advanced open source component for log data anomaly mining. The AMiner comes with several detectors to spot new events, new parameters, new correlations, new values and unknown value combinations and can run as stand-alone solution or as sensor with connection to a SIEM solution. More advanced detectors help to determines the characteristics of variable parts of log lines, specifically the properties of numerical and categorical fields. Detailed examples throughout this book allow the reader to better understand and apply the introduced techniques with open source software. Step-by-step instructions help to get familiar with the concepts and to better comprehend their inner mechanisms. A log test data set is available as free download and enables the reader to get the system up and running in no time. This book is designed for researchers working in the field of cyber security, and specifically system monitoring, anomaly detection and intrusion detection. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems. Forward-thinking practitioners, who would benefit from becoming familiar with the advanced anomaly detection methods, will also be interested in this book.

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
Author: Fouzi Harrou
Publisher: Elsevier
Total Pages: 330
Release: 2020-07-03
Genre: Technology & Engineering
ISBN: 0128193662
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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