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

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.

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 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.

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.

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.

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."

Introduction to Process Control, Second Edition provides a bridge between the traditional view of process control and the current, expanded role by blending conventional topics with a broader perspective of more integrated process operation, control, and information systems. Updating and expanding the content of its predecessor, this second edition addresses issues in today’s teaching of process control. Teaching & Learning Principles Presents a concept first followed by an example, allowing students to grasp theoretical concepts in a practical manner Uses the same problem in each chapter, culminating in a complete control design strategy Includes 50 percent more exercises Content Defines the traditional and expanded roles of process control in modern manufacturing 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 backoff 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 Discusses 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 Web Resource The book’s website offers a user-friendly software environment for interactively studying the examples in the text. The site contains the MATLAB® toolboxes for process control education as well as the main simulation examples from the book. Access the site through the authors’ websites at www.pseonline.net and www.chms.ucdavis.edu/research/web/pse/ahmet/ Drawing on the authors’ combined 50 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 authors help readers see how traditional process control has evolved into an integrated operational environment used to run modern manufacturing facilities.

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.