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

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

Y. A. Liu,Niket Sharma
Integrated Process Modeling Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing Book PDF

Author : Y. A. Liu,Niket Sharma
Publisher : John Wiley & Sons
Page : 1027 pages
File Size : 45,8 Mb
Release : 2023-07-25
Category : Technology & Engineering
ISBN : 9783527843824

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Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing by Y. A. Liu,Niket Sharma Pdf

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.

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

Yih An Liu,Niket Sharma
Integrated Process Modeling Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing Book PDF

Author : Yih An Liu,Niket Sharma
Publisher : Unknown
Page : 0 pages
File Size : 47,8 Mb
Release : 2023
Category : Polyolefin industry
ISBN : 3527352694

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Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing by Yih An Liu,Niket Sharma Pdf

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, 2 Volume Set

Y. A. Liu,Niket Sharma
Integrated Process Modeling Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing 2 Volume Set Book PDF

Author : Y. A. Liu,Niket Sharma
Publisher : Wiley-VCH
Page : 0 pages
File Size : 41,6 Mb
Release : 2023-07-24
Category : Technology & Engineering
ISBN : 3527352678

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Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing, 2 Volume Set by Y. A. Liu,Niket Sharma Pdf

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.

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

Yih An Liu,Niket Sharma
Integrated Process Modeling Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing Book PDF

Author : Yih An Liu,Niket Sharma
Publisher : Unknown
Page : 0 pages
File Size : 49,5 Mb
Release : 2023
Category : Polyolefin industry
ISBN : 3527352686

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Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing by Yih An Liu,Niket Sharma Pdf

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.

Refinery Engineering

Ai-Fu Chang,Kiran Pashikanti,Y. A. Liu
Refinery Engineering Book PDF

Author : Ai-Fu Chang,Kiran Pashikanti,Y. A. Liu
Publisher : John Wiley & Sons
Page : 521 pages
File Size : 51,7 Mb
Release : 2013-03-01
Category : Technology & Engineering
ISBN : 9783527666850

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Refinery Engineering by Ai-Fu Chang,Kiran Pashikanti,Y. A. Liu Pdf

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.

Digitalization and Analytics for Smart Plant Performance

Frank (Xin X.) Zhu
Digitalization and Analytics for Smart Plant Performance Book PDF

Author : Frank (Xin X.) Zhu
Publisher : John Wiley & Sons
Page : 48 pages
File Size : 50,5 Mb
Release : 2021-04-06
Category : Technology & Engineering
ISBN : 9781119634102

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Digitalization and Analytics for Smart Plant Performance by Frank (Xin X.) Zhu Pdf

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

Jose A. Romagnoli,Ahmet Palazoglu
Introduction to Process Control Second Edition Book PDF

Author : Jose A. Romagnoli,Ahmet Palazoglu
Publisher : CRC Press
Page : 647 pages
File Size : 44,5 Mb
Release : 2012-02-14
Category : Science
ISBN : 9781439854860

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Introduction to Process Control, Second Edition by Jose A. Romagnoli,Ahmet Palazoglu Pdf

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.

Petroleum Refinery Process Modeling

Y. A. Liu,Ai-Fu Chang,Kiran Pashikanti
Petroleum Refinery Process Modeling Book PDF

Author : Y. A. Liu,Ai-Fu Chang,Kiran Pashikanti
Publisher : John Wiley & Sons
Page : 600 pages
File Size : 48,9 Mb
Release : 2018-06-05
Category : Technology & Engineering
ISBN : 9783527344239

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Petroleum Refinery Process Modeling by Y. A. Liu,Ai-Fu Chang,Kiran Pashikanti Pdf

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.

New Directions in Bioprocess Modeling and Control

Michael A. Boudreau,Gregory K. McMillan
New Directions in Bioprocess Modeling and Control Book PDF

Author : Michael A. Boudreau,Gregory K. McMillan
Publisher : ISA
Page : 356 pages
File Size : 55,7 Mb
Release : 2007
Category : Science
ISBN : 1556179057

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New Directions in Bioprocess Modeling and Control by Michael A. Boudreau,Gregory K. McMillan Pdf

Models offer benefits even before they are put on line. Based on years of experience, the authors reveal in New Directions in Bioprocess Modeling and Control that significant improvements can result from the process knowledge and insight that are gained when building experimental and first-principle models for process monitoring and control. Doing modeling in the process development and early commercialization phases is advantageous because it increases process efficiency and provides ongoing opportunities for improving process control. This technology is important for maximizing benefits from analyzers and control tool investments. If you are a process design, quality control, information systems, or automation engineer in the biopharmaceutical, brewing, or bio-fuel industry, this handy resource will help you define, develop, and apply a virtual plant, model predictive control, first-principle models, neural networks, and multivariate statistical process control. The synergistic knowledge discovery on bench top or pilot plant scale can be ported to industrial scale processes. This learning process is consistent with the intent in the Process Analyzer and Process Control Tools sections of the FDA_s Guidance for Industry PAT _ A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. It states in the Process Analyzer section of the FDA_s guidance: _For certain applications, sensor-based measurements can provide a useful process signature that may be related to the underlying process steps or transformations. Based on the level of process understanding these signatures may also be useful for the process monitoring, control, and end point determination when these patterns or signatures relate to product and process quality._

Integrated Process Design and Operational Optimization via Multiparametric Programming

Baris Burnak,Nikolaos A. Diangelakis,Efstratios N. Pistikopoulos
Integrated Process Design and Operational Optimization via Multiparametric Programming Book PDF

Author : Baris Burnak,Nikolaos A. Diangelakis,Efstratios N. Pistikopoulos
Publisher : Morgan & Claypool Publishers
Page : 260 pages
File Size : 43,7 Mb
Release : 2020-09-04
Category : Technology & Engineering
ISBN : 9781681739557

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Integrated Process Design and Operational Optimization via Multiparametric Programming by Baris Burnak,Nikolaos A. Diangelakis,Efstratios N. Pistikopoulos Pdf

This book presents a comprehensive optimization-based theory and framework that exploits the synergistic interactions and tradeoffs between process design and operational decisions that span different time scales. Conventional methods in the process industry often isolate decision making mechanisms with a hierarchical information flow to achieve tractable problems, risking suboptimal, even infeasible operations. In this book, foundations of a systematic model-based strategy for simultaneous process design, scheduling, and control optimization is detailed to achieve reduced cost and improved energy consumption in process systems. The material covered in this book is well suited for the use of industrial practitioners, academics, and researchers. In Chapter 1, a historical perspective on the milestones in model-based design optimization techniques is presented along with an overview of the state-of-the-art mathematical tools to solve the resulting complex problems. Chapters 2 and 3 discuss two fundamental concepts that are essential for the reader. These concepts are (i) mixed integer dynamic optimization problems and two algorithms to solve this class of optimization problems, and (ii) developing a model based multiparametric programming model predictive control. These tools are used to systematically evaluate the tradeoffs between different time-scale decisions based on a single high-fidelity model, as demonstrated on (i) design and control, (ii) scheduling and control, and (iii) design, scheduling, and control problems. We present illustrative examples on chemical processing units, including continuous stirred tank reactors, distillation columns, and combined heat and power regeneration units, along with discussions of other relevant work in the literature for each class of problems.

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

Chao Shang
Dynamic Modeling of Complex Industrial Processes Data driven Methods and Application Research Book PDF

Author : Chao Shang
Publisher : Springer
Page : 143 pages
File Size : 41,5 Mb
Release : 2019-03-19
Category : Technology & Engineering
ISBN : 9811338892

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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.

Model Based Control

Paul Serban Agachi
Model Based Control Book PDF

Author : Paul Serban Agachi
Publisher : Wiley-VCH
Page : 304 pages
File Size : 53,6 Mb
Release : 2006-11-10
Category : Science
ISBN : UOM:39015066759559

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Model Based Control by Paul Serban Agachi 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. Zoltán K. Nagy received his PhD from Babes-Bolyai University of Cluj, where he worked as a lecturer until 2005. Before taking up his current appointment as a faculty member at Loughborough University, UK, he was NATO research fellow and visiting lecturer at the University of Illinois at Urbana-Champaign, and research associate at the University of Stuttgart, University of Heidelberg and ETH Zürich. His main research interest is in the model based control and optimization of chemical processes. He worked on industrial implementation of model-based control strategies with companies such as BASF and ABB, and has published over 80 papers in the field. Arpad Imre-Lucaci received his M.S. and Ph.D. degrees in chemical engineering from Babes-Bolyai University of Cluj-Napoca in 1985 and 1999, respectively. Since 1988 he has worked in the Chemical Engineering Department of BBU Cluj-Napoca, Romania, and spent research stays at University of Stuttgart (1994) and ETH Zürich (in 2002 and 2003). His main research fields are mathematical modeling, simulation and optimization in process industries, on which he has published over 20 scientific papers. Cristea Vasile Mircea graduated the Faculty of Electrotechnics, Romania, with specialization on process control and computer science and holds a Ph.D. degree in process control. After 8 years spent in industry he is at present Associate Professor at Babes-Bolyai University, Cluj-Napoca; his interests lie in systems theory, chemical process control, advanced process control, data acquisition and control, linear and nonlinear model based predictive control, and fuzzy control. He was director of CNCSIS Projects and has published 3 books as well as over 55 scientific papers. Professor Paul Serban Agachi graduated in 1970 in Control Engineering at the Politehnica University of Bucharest. Obtained his Ph.D. in Chemical Engineering from the University Petroleum & Gas Ploiesti, Romania. Professional experience: design engineer, system analyst, researcher in fuel cells, process modeling, optimization and control. At present, professor of Process Control at the Department of Chemical Engineering of Babes-Bolyai University, Cluj-Napoca and member of the Academy of Technical Sciences of Romania. He has been visiting associate at California Institute of Technology, invited professor at Eötvös Lorand University, UNESCO Higher Education consultant. He has published 8 books and 96 scientific papers.

Introduction to Process Control

José Alberto Romagnoli,Ahmet Palazoğlu
Introduction to Process Control Book PDF

Author : José Alberto Romagnoli,Ahmet Palazoğlu
Publisher : Unknown
Page : 712 pages
File Size : 40,8 Mb
Release : 2020
Category : SCIENCE
ISBN : 0367508737

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Introduction to Process Control by José Alberto Romagnoli,Ahmet Palazoğlu Pdf

"The new edition blends conventional topics with a modern perspective of integrated process operation, control, and information systems. Updated throughout, it addresses smart manufacturing, new data preprocessing techniques, and machine learning and artificial intelligence concepts. It guides the reader to resources needed to solve modeling, classification, and monitoring problems. It introduces the link between process optimization and process control and links discussion of modern architectures of industrial computer control systems with real case studies and applications to pilot-scale operations. It features exercises throughout and downloadable MATLAB toolboxes to reinforce learning"--

Practical Grey-box Process Identification

Torsten P. Bohlin
Practical Grey box Process Identification Book PDF

Author : Torsten P. Bohlin
Publisher : Springer Science & Business Media
Page : 351 pages
File Size : 45,5 Mb
Release : 2006-09-07
Category : Technology & Engineering
ISBN : 9781846284038

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Practical Grey-box Process Identification by Torsten P. Bohlin Pdf

This book reviews the theoretical fundamentals of grey-box identification and puts the spotlight on MoCaVa, a MATLAB-compatible software tool, for facilitating the procedure of effective grey-box identification. It demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. In addition, the book answers common questions which will help in building accurate models for systems with unknown inputs.

Profit Maximization Techniques for Operating Chemical Plants

Sandip K. Lahiri
Profit Maximization Techniques for Operating Chemical Plants Book PDF

Author : Sandip K. Lahiri
Publisher : John Wiley & Sons
Page : 432 pages
File Size : 50,9 Mb
Release : 2020-05-01
Category : Technology & Engineering
ISBN : 9781119532170

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Profit Maximization Techniques for Operating Chemical Plants by Sandip K. Lahiri Pdf

A systematic approach to profit optimization utilizing strategic solutions and methodologies for the chemical process industry In the ongoing battle to reduce the cost of production and increase profit margin within the chemical process industry, leaders are searching for new ways to deploy profit optimization strategies. Profit Maximization Techniques For Operating Chemical Plants defines strategic planning and implementation techniques for managers, senior executives, and technical service consultants to help increase profit margins. The book provides in-depth insight and practical tools to help readers find new and unique opportunities to implement profit optimization strategies. From identifying where the large profit improvement projects are to increasing plant capacity and pushing plant operations towards multiple constraints while maintaining continuous improvements—there is a plethora of information to help keep plant operations on budget. The book also includes information on: ● Take away methods and techniques for identifying and exploiting potential areas to improve profit within the plant ● Focus on latest Artificial Intelligence based modeling, knowledge discovery and optimization strategies to maximize profit in running plant. ● Describes procedure to develop advance process monitoring and fault diagnosis in running plant ● Thoughts on engineering design , best practices and monitoring to sustain profit improvements ● Step-by-step guides to identifying, building, and deploying improvement applications For leaders and technologists in the industry who want to maximize profit margins, this text provides basic concepts, guidelines, and step-by-step guides specifically for the chemical plant sector.