Table of Contents
Cover image
Title page
Copyright
Contributors
Preface
Chapter 1: Smart manufacturing: It's a journey, not a destination
Abstract
1: Introduction
2: The “Smart Manufacturing” north star vision
3: Strategy
4: Execution
5: Conclusion
Chapter 2: Implementing smart manufacturing across an industrial organization
Abstract
1: Introduction
2: Smart Manufacturing examples at Linde
3: Summary
4: Conclusion
Chapter 3: Industrie 4.0 and international perspective
Abstract
1: Introduction
2: RAMI 4.0
3: Asset administration shell
4: Applications
5: Roadmap/ongoing research
6: Conclusion
Chapter 4: Cyberinfrastructure for the democratization of smart manufacturing
Abstract Acknowledgments
1: Introduction
2: Smart Manufacturing and democratization
3: Today's complexity of interconnectedness
4: Reducing the heavy lift of data modeling and contextualization
5: The data-centric view of Smart Manufacturing
6: The building blocks of Smart Manufacturing
7: Operational data models, SM Profiles, and the SM Innovation Platform
8: Overarching R&D considerations
9: Conclusion
Chapter 5: The role of hardware and software in smart manufacturing
Abstract
1: Introduction
2: Hardware
3: Software
4: Conclusion
Chapter 6: Measuring, managing, and transforming data for operational insights
Abstract
Acknowledgments
1: Quick look backward
2: Modern data infrastructure approach to manufacturing data collection and analysis
3: Plant operations—The silo issue
4: Simplifying the integration of plant data and control hierarchy
5: Using operations data for enterprise or division-level data analytics
6: Collecting the data for intelligent analysis
7: Condition-based and predictive asset management
8: Control loop performance monitoring
9: Beyond the enterprise—Community collaboration using OSIsoft's connected services
10: Conclusion
Chapter 7: The role of advanced process modeling in smart manufacturing
Abstract
Acknowledgments
1: Introduction
2: Model development
3: From data and model to value
4: Going on-line
5: Model reduction
6: Case study
7: Industrial needs
8: Conclusion
Chapter 8: Industrial AI and predictive analytics for smart manufacturing systems
Abstract
1: Introduction
2: The four enabling technologies for industry 4 0
3: Case study: Intelligent bandsaw system
4: Challenges
5: Conclusion
Chapter 9: Computational framework for smart manufacturing via parametric optimization and control (PAROC)
Abstract
1: Introduction
2: Smart manufacturing concepts
3: Computational framework in smart manufacturing
4: Case study: Application of PAROC framework to optimal hydrogen storage operation
5: Future opportunities
6: Conclusion
Chapter 10: A systems engineering-driven decomposition approach for large-scale industrial decision-making processes
Abstract
1: Introduction
2: Optimization methods for complex integrated systems
3: Mathematical optimization: A review
4: Multidisciplinary systems design optimization approaches
5: Multicriteria optimization
6: Maximizing steel rolling throughput: Illustrative use case of manufacturing systems formal decomposition and optimization
7: Future steps toward full manufacturing systems autonomy: Advanced probabilistic and control approaches
8: Conclusion
Chapter 11: Model-predictive safety: A new evolution in functional safety
Abstract
Acknowledgments
1: Introduction
2: Current industry-standard functional safety systems
3: Alarm management and interlocks
4: Univariate reactive vs. multivariate predictive safety indicators
5: Data-driven predictive risk assessment
6: Digital twin
7: Model-predictive safety
8: Application of model-predictive safety to a process example
9: Conclusion
Chapter 12: Inferential modeling and soft sensors
Abstract
1: Introduction
2: Characteristics of process data
3: Inferential control and state estimation-based approaches
4: Data-driven soft sensors
5: Points of view
6: Smart manufacturing as new enabler
7: Conclusion
Chapter 13: A decision support framework for sustainable and smart manufacturing
Abstract
Acknowledgments
1: Introduction
2: Decision-support framework
3: Case study
4: Conclusion
Chapter 14: Smart manufacturing pedagogy for the anthropocene
Abstract
Acknowledgments
1: Introduction
2: The landscape of smart manufacturing pedagogy
3: Smart Manufacturing as data-enabled sustainable manufacturing
4: Deploying a Smart Manufacturing pedagogy for the anthropocene
5: Conclusion Index
Copyright
Elsevier
Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands
The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom
50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States
© 2020 Elsevier Inc. All rights reserved.
No part of this publication may be reproduced or transmi ed in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.
This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).
Notices
Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.
Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety
and the safety of others, including parties for whom they have a professional responsibility.
To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a ma er of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.
Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the Library of Congress
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
ISBN: 978-0-12-820027-8
For information on all Elsevier publications visit our website at h ps://www.elsevier.com/books-and-journals
Publisher: Susan Dennis
Editorial Project Manager: Devlin Person
Production Project Manager: Bharatwaj Varatharajan
Cover Designer: Ma hew Limbert
Typeset by SPi Global, India
Contributors
Majid Moradi Aliabadi Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI, United States
Jose Anaya El Camino Community College, Hawthorne, CA, United States
Jeffrey E. Arbogast American Air Liquide, Newark, DE, United States Air Liquide (China) R&D Co., Ltd., Shanghai, China
Styliani Avraamidou Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States
Moslem Azamfar NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS), Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, United States
Osvaldo A. Bascur OSB Digital, LLC, The Woodlands, TX, United States
B. Wayne Beque e Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States
Mark Besser Customer Success, Savigent Software, Bloomington, MN, United States
Sergio Butkewitsch University of Pi sburgh, Swanson School of Engineering, Department of Industrial Engineering, Pi sburgh, PA, United States
Greg Colvin Additive Manufacturing, Honeywell Aerospace Advanced Technology, Phoenix, AZ, United States
James Davis University of California Los Angeles, CESMII, The Smart Manufacturing Innovation Institute, Los Angeles, CA, United States
Richard P. Donovan Sustainable Smart Manufacturing, California Institute for Telecommunications and Information Technologies, Irvine, CA, United States
John Dyck University of California Los Angeles, CESMII, The Smart Manufacturing Innovation Institute, Los Angeles, CA, United States
Helvio Markman Filho Circle Process Management Systems LLC, Glen Allen, VA, United States
Jesus Flores-Cerrillo Linde PLC, Tonawanda, NY, United States
Lance Fountaine Cargill, Wayzata, MN, United States
Sambit Ghosh Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States
Iiro Harjunkoski ABB Power Grids Research, Mannheim, Germany
Gangshi Hu Linde PLC, Tonawanda, NY, United States
Yinlun Huang Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI, United States
Prakashan Korambath University of California Los Angeles, CESMII, The Smart Manufacturing Innovation Institute, Los Angeles, CA, United States
Heiko Koziolek ABB Corporate Research, Ladenburg, Germany
Jay Lee NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS), Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, United States
Haresh Malkani University of California Los Angeles, CESMII, The Smart Manufacturing Innovation Institute, Los Angeles, CA, United States
Leila Samandari Masooleh Department of Chemical and Biological Engineering, Drexel University, Philadelphia, PA, United States
Lawrence Megan Linde PLC, Tonawanda, NY, United States
Jim O’Rourke OSIsoft, LLC, Houston, TX, United States
Gerald S. Ogumerem
Artie McFerrin Department of Chemical Engineering
Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States
Ulku Oktem Near-Miss Management, LLC, Philadelphia, PA, United States
Vibhor Pandhare NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS), Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, United States
Efstratios N. Pistikopoulos
Artie McFerrin Department of Chemical Engineering
Texas A&M Energy Institute, Texas A&M University, College Station, TX, United States
J. Pieter Schmal ExxonMobil Research & Engineering, Annandale, NJ, United States
Dirk Schulz ABB Corporate Research, Ladenburg, Germany
Warren D. Seider Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States
Jaskaran Singh
NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS), Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, United States Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
Masoud Soroush Department of Chemical and Biological Engineering, Drexel University, Philadelphia, PA, United States
Jonathan Wise University of California Los Angeles, CESMII, The Smart Manufacturing Innovation Institute, Los Angeles, CA, United States
Shu Yang Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States
Michael Yost Clean Energy Smart Manufacturing Innovation Institute, Los Angeles, CA, United States
Preface
Research and development efforts in the past 10 years have led to considerable advances in concepts and methods in smart manufacturing. This monograph puts these advances into perspective and shows how process industries can benefit from them. It consolidates results developed by leading academic and industrial groups in this area, along with the computational tools and methods, and presents them in a systematic way. It provides comprehensive coverage of advances in concepts and methodologies. This book is complemented by the companion book entitled “Smart Manufacturing: Industrial Applications and Case Studies,” which focuses on the state-of-the art applications of smart manufacturing concepts and methods in process industries.
Chapter 1, Smart manufacturing: It's a journey, not a destination, provides an introduction to smart manufacturing and points to the broad vision that smart manufacturing really represents. It highlights the importance of aligning a company around a common overarching vision of smart manufacturing, and the critical need to establish a comprehensive strategy to manage progress and success throughout the ongoing journey. It argues that smart manufacturing is a ma er of continuous improvement, not just a pursuit of new technology.
Chapter 2, Implementing smart manufacturing across an industrial organization, summarizes the factors that should be considered in implementing smart manufacturing. Smart manufacturing has the potential to unlock billions of dollars of operating profit across the industry. Recent successes of the authors related to the three pillars of smart manufacturing are described: data analytics, automation, and connectivity.
Chapter 3, Industrie 4.0 and international perspective, discusses the German-driven initiative Industrie 4.0 and addresses commonalities and differences. It suggests possible synergies and
shows how Smart Manufacturing and Industrie 4.0 activities can support and complement each other. It also reviews some applications within Industrie 4.0 and summarizes the development roadmap.
Chapter 4, Cyberinfrastructure for the democratization of smart manufacturing, discusses the importance of the realignment of business, leadership, market and infrastructure to “democratize” “smart” business, technology, operational and workforce data practices industry-wide. It argues that this democratization is needed to realize the full economic potential of Smart Manufacturing.
Chapter 5, The role of hardware and software in smart manufacturing, highlights and explains the roles and responsibilities that hardware and software play in smart manufacturing. The chapter is a primer on legacy and present-day equipment, data that are created, and data that are used in manufacturing decision making.
Chapter 6, Measuring, managing, and transforming data for operational insights, discusses the need for a data-driven strategy to enable operations, maintenance, and business personnel to quickly and easily take corrective actions when abnormal conditions occur. It presents a smart unit template approach that transforms data into information, classifies the operating modes based on the variance from the plant schedule targets, simplifies the analysis and aggregation of production and consumables, and allows for fast tracking of losses by shift.
Chapter 7, The role of advanced process modeling in smart manufacturing, discusses the critical role that modeling plays in many smart manufacturing approaches. It explains how firstprinciples and data-driven models can be developed and what aspects are involved to get to a validated model, including data reconciliation, state estimation, parameter estimation, design of experiments, and model selection. It elaborates on challenges in online implementation and model reduction. It demonstrates different modeling approaches using a case study.
Chapter 8, Industrial AI and predictive analytics for smart manufacturing systems, presents a comprehensive overview of the
important role of key enabling technologies of industrial artificial intelligence in the manufacturing industry in general. Their systematic adoption can aid in producing new value-creation opportunities and avoidance of problems that have not even occurred yet.
Chapter 9, Computational framework for smart manufacturing via parametric optimization and control (PAROC), highlights the need for efficient computational techniques to transform manufacturing data into manufacturing intelligence. It demonstrates how computational frameworks utilize the functionalities of analytical tools to create unique solutions. A case study considers refueling of a metal hydride hydrogen storage system to describe how the PAROC framework leverages high-fidelity modeling, system identification, and parametric programming to develop a unique optimal operating strategy for the system.
Chapter 10, A systems engineering-driven decomposition approach for large-scale industrial decision-making processes, presents an approach that considers industrial production structures as dynamic stochastic systems, whose behavior varies with time, affected by both controllable inputs and uncertainties from various sources. To represent this behavior mathematically, it presents a framework derived from the typically design-centric Systems Engineering methodology, adapted toward manufacturing and its digital technologies. It demonstrates this approach through a case study.
Chapter 11, Model-predictive safety: A new evolution in functional safety, discusses the need for innovation in functional safety to ensure safe operation of intensified processes in smart manufacturing. It presents the concept and the main components of an innovation in functional safety, namely, model-predictive safety (MPS), which unlike conventional functional safety systems, generates alarm signals that are predictive and systematically accounts for process nonlinearities and variable interactions. It describes how in real time, MPS detects potential and imminent future process operation hazards, and prescribes optimal preventive and mitigating actions proactively. It demonstrates the concept of MPS by applying it to a polymerization reactor example.
Chapter 12, Inferential modeling and soft sensors, discusses the increasing role that soft sensors are playing in advanced (smart) manufacturing. It describes the impact of soft sensors based on historical trends, and identifies future opportunities as well as the characteristics of process data and its influence on soft sensor development. A comprehensive review of both first-principles and data-driven methods for soft sensor modeling, including state estimation, principal component analysis, partial least squares, artificial neural networks, and support vector machines, is provided.
Chapter 13, A decision support framework for sustainable and smart manufacturing, introduces a decision support framework for sustainable and smart manufacturing. The framework comprises a data analytics block that is responsible for both sustainability assessment and monitoring of external factors that may affect the sustainability performance of the system in its transition process. It uses a model predictive control strategy to identify and update optimal sustainability strategic plans for achieving short-to-long term sustainability goals. It provides a structured step-by-step guide to decision makers in formulating and updating sustainability strategies.
Chapter 14, Smart manufacturing pedagogy for the anthropocene, presents fundamental principles from which to build a holistic framework of a smart manufacturing pedagogy for the anthropocene. Inspired by a data-centric sustainable manufacturing vision for manufacturing, it describes how fundamental and emerging complex system ideas enable convergence of broad perspectives from the humanities and social sciences into a model to leverage the tools of data science and engineering. It introduces intriguing new perspectives from complex systems and sustainability science that link information and Newtonian mechanics in order to create a dynamic repository of case studies that can drive life-long learning and set us on a path to smartly manufacture products for healthy living on a healthy planet. This book is expected to become a reference for process engineers, managers, and consultants in process industries, postdoctoral researchers, graduate students, and other researchers in academia, national labs, and process industries. It summarizes the most recent
advances in smart manufacturing concepts and methods. This book is complemented by the companion volume entitled “Smart Manufacturing: Industrial Applications and Case Studies,” which focuses on the state-of-the art applications of smart manufacturing concepts and methods in process industries.
