Sustainable Manufacturing Systems E-Book

Lin Li

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Series on Systems Science and Engineering
• Sprache: Englisch

Seitenzahl: 552

Veröffentlichungsjahr: 2022

Beschreibung

Sustainable Manufacturing Systems Learn more about energy efficiency in traditional and advanced manufacturing settings with this leading and authoritative resource Sustainable Manufacturing Systems: An Energy Perspective delivers a comprehensive analysis of energy efficiency in sustainable manufacturing. The book presents manufacturing modeling methods and energy efficiency evaluation and improvement methods for different manufacturing systems. It allows industry professionals to understand the methodologies and techniques being embraced around the world that lead to advanced energy management. The book offers readers a comprehensive and systematic theoretical foundation for novel manufacturing system modeling, analysis, and control. It concludes with a summary of the insights and applications contained within and a discussion of future research issues that have yet to be grappled with. Sustainable Manufacturing Systems answers the questions that energy customers, managers, decision makers, and researchers have been asking about sustainable manufacturing. The book's release coincides with recent and profound advances in smart grid applications and will serve as a practical tool to assist industrial engineers in furthering the green revolution. Readers will also benefit from: * A thorough introduction to energy efficiency in manufacturing systems, including the current state of research and research methodologies * An exploration of the development of manufacturing methodologies, including mathematical modeling for manufacturing systems and energy efficiency characterization in manufacturing systems * An analysis of the applications of various methodologies, including electricity demand response for manufacturing systems and energy control and optimization for manufacturing systems utilizing combined heat and power systems * A discussion of energy efficiency in advanced manufacturing systems, like stereolithography additive manufacturing and cellulosic biofuel manufacturing systems Perfect for researchers, undergraduate students, and graduate students in engineering disciplines, especially for those majoring in industrial, mechanical, electrical, and environmental engineering, Sustainable Manufacturing Systems will also earn a place in the libraries of management and business students interested in manufacturing system cost performance and energy management.

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IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief

Jón Atli Benediktsson

Andreas Molisch

Diomidis Spinellis

Anjan Bose

Saeid Nahavandi

Ahmet Murat Tekalp

Adam Drobot

Jeffrey Reed

Peter (Yong) Lian

Thomas Robertazzi

Sustainable Manufacturing Systems

An Energy Perspective

Lin Li

University of Illinois at Chicago

Chicago, IL, USA

MengChu Zhou

University Heights

Newark, NJ, USA

IEEE Press Series on Systems Science and Engineering

MengChu Zhou, Series Editor

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Author Biography

Dr. Lin Li joined the Department of Mechanical and Industrial Engineering, University of Illinois Chicago in 2011, and is now a Professor in Mechanical and Industrial Engineering. He also serves as the Director of U.S. Department of Energy Industrial Assessment Center and the founding Director of the Sustainable Manufacturing Systems Research Laboratory at the University of Illinois Chicago. He received a B.E. degree in Mechanical Engineering from Shanghai Jiao Tong University in 2001, and an M.S.E. degree in Mechanical Engineering, an M.S.E. degree in Industrial and Operations Engineering, and a Ph.D. degree in Mechanical Engineering from the University of Michigan, Ann Arbor, in 2003, 2005, and 2007, respectively. His research interests include energy control and electricity demand response of manufacturing systems, environmental sustainability of additive manufacturing processes, cost-effective cellulosic biofuel manufacturing system, lithium-ion electric vehicle battery remanufacturing and reliability assessment, multi-machine system modeling and throughput estimation, and intelligent maintenance of manufacturing systems. He is a recipient of Harold A. Simon Award and University of Illinois Chicago Teaching Recognition Program Award. He is a founding member of the technical committee of Sustainable Production and Service Automation in the IEEE Robotics and Automation Society, an academic Editor for journal Sustainability, and was Chair of quality and reliability technical committee, ASME Manufacturing Engineering Division.

Dr. MengChu Zhou joined the Department of Electrical and Computer Engineering, New Jersey Institute of Technology in 1990, and is now a Distinguished Professor in Electrical and Computer Engineering. His interests are in intelligent automation, semiconductor manufacturing, AI, Petri nets, Internet of Things, edge/cloud computing, and big data analytics. He has over 1000 publications including 12 books, over 700 journal papers including over 600 IEEE transactions/journal/magazine papers, 31 patents and 30 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is founding Chair/Co-chair of Technical Committee on AI-based Smart Manufacturing Systems of IEEE Systems, Man, and Cybernetics Society, Technical Committee on Semiconductor Manufacturing Automation and Technical Committee on Digital Manufacturing and Human-Centered Automation of IEEE Robotics and Automation Society. He is a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award fromIEEE Systems, Man, and Cybernetics Society, and Edison Patent Award from the Research & Development Council of New Jersey. He is Fellow of IEEE, International Federation of Automatic Control, American Association for the Advancement of Science, Chinese Association of Automation and National Academy of Inventors.

Preface

Sustainable Manufacturing Systems are one of modern technologies and have played a significant role in economic growth worldwide. Currently, the total value added by the global manufacturing industry reaches USD 13.5 trillion, accounting for nearly 16% of the global economy. Despite the continued strength of manufacturing industry, it also faces a pressing concern over energy consumption and environmental sustainability. Approximately, the industry sector possesses near one‐quarter of the total energy consumption in the U.S., where over 75% of energy use is primarily attributed to manufacturing activities.

The issues of resource scarcity and environmental impacts are becoming vital due to the constantly rising demand for energy in the manufacturing sector. Several critical questions arise in proposing energy management strategies in manufacturing and evoke different aspects of energy efficiency studies, including (i) improving the energy efficiency of manufacturing systems considering the complex manufacturing conditions, (ii) reducing the energy cost with no sacrifice of manufacturing productivity, and (iii) generating policies or incentives to promote energy efficiency in the manufacturing industry and encourage the manufacturers’ transition to environmentally conscious manufacturing. All these questions lead to the joint modeling and analysis of production and energy for manufacturing systems.

This book provides a holistic view of energy efficiency assessment and improvement measures for sustainable manufacturing systems, delivered through the state‐of‐the‐art on sustainable manufacturing and energy efficiency issues, fundamentals and mathematical tools for manufacturing system modeling, and energy management methodologies for different manufacturing systems. Meanwhile, this book transfers the recent academic research results into various representative examples and case studies, which provide insights into the current sustainable practices and energy management strategies in manufacturing systems at different scales and levels. From the application aspect, this book is expected to help (i) energy consumers, participants and administrators in energy efficiency programs, and (ii) research participants embrace the opportunities for advanced energy management. Furthermore, this book is intended to bring about learning initiatives for students in mechanical, industrial, environmental, and electrical engineering programs by effectively integrating concepts in academic research into real‐world problem solving, which helps cultivate the student’s enthusiasm for energy conservation and green manufacturing.

Organization of the Book

Part I: Introductions to Energy Efficiency in Manufacturing Systems

Chapter 1 provides an overview of this book and introduces background knowledge about manufacturing systems and concepts of sustainable manufacturing. First, it reviews the current status and development of the manufacturing industry and demonstrates a series of representative manufacturing systems. Then, it presents the key concepts of sustainable manufacturing and discusses the existing challenges that may impede sustainable development in manufacturing industries. Finally, it generalizes the problem statements and scopes of research in the context of sustainable manufacturing systems.

Chapter 2 provides more detailed background information on energy efficiency in manufacturing systems. The overall energy consumption and major energy end‐users in manufacturing facilities are first introduced, followed by the discussions on the energy‐saving potentials and energy management strategies at the machine, system, and plant levels. In addition, the significance of demand‐side energy management is illustrated with the detailed explanations of associated techniques.

Part II: Mathematical Tools and Modeling Basics

Chapter 3 introduces the necessary mathematical tools used in the following chapters of this book. Specifically, the fundamentals of probability theory and application scenarios of several common probability distributions used in manufacturing system modeling are introduced, followed by the demonstration of Petri nets for the visual representation of manufacturing systems as discrete event systems and discussions on the optimization problems with metaheuristics algorithms, specifically a particle swarm optimizer.

Chapter 4 presents the mathematical modeling techniques for manufacturing systems, which play a critical role in sustainable manufacturing system design and analysis. This chapter introduces the basics of manufacturing system modeling, followed by detailed discussions on some typical modeling approaches to simple two‐machine production lines and complex multi‐machine ones.

Chapter 5 extends the modeling and analysis techniques discussed in the previous chapter into energy efficiency characterization in manufacturing systems. First, the energy consumption modeling approaches are discussed based on the inter‐process dependency or the machines’ operation schemes. Then the energy cost models of manufacturing systems under different electricity tariffs are demonstrated with illustrative examples.

Part III: Energy Management in Typical Manufacturing Systems

Chapter 6 presents the electricity demand response (DR) strategies for manufacturing systems. The instant high demand can hinder the stability of a power grid, and thus the utility providers charge industrial customers specifically for their electricity demand in addition to the total energy consumption. In this chapter, the time‐of‐use (TOU) and critical peak pricing (CPP) tariffs are first introduced. The production scheduling methods that can respond to electricity price signals based on the system models are then discussed. Finally, case studies are presented to compare the peak demand and energy costs under TOU, CPP, and traditional flat‐rate tariffs.

Chapter 7 extends the DR scheduling methods presented in the previous chapter by integrating a combined heat and power (CHP) system with manufacturing systems. As an on‐site energy generation method, a CHP system can provide electricity and heat to the manufacturing plant, leading to a reduction in the grid power demand of the manufacturing plant. In this chapter, the key concepts of a CHP system are first reviewed, followed by the formulation of an energy cost optimization model for a combined CHP and manufacturing systems. The case studies are presented to demonstrate the effectiveness of the combined system in demand and energy cost reduction.

Chapter 8 addresses an energy management problem in manufacturing systems considering the heating, ventilation, and air conditioning (HVAC) system, which is one of the primary contributors to the direct non‐process end use energy consumption in manufacturing plants. The heat emissions from manufacturing operations can significantly affect the thermal load of an HVAC system, and the relationships between manufacturing and HVAC systems are discussed in this chapter. Specifically, the formulation of an energy cost optimization problem for the integrated systems is first introduced, and then the metaheuristic algorithm used to solve the problem is discussed in detail. Finally, case studies demonstrate the optimal DR strategy for the integrated system.

Part IV: Energy Management in Advanced Manufacturing Systems

Chapter 9 specifically focuses on the energy analysis of additive manufacturing (AM) systems. In this chapter, stereolithography (SL), one of the most commonly used AM technologies, is adopted to demonstrate the energy modeling and analysis methods for an AM process. This chapter starts with the introduction of the technical advantages of AM technologies and a detailed description of an SL process. Then, it presents the energy consumption model of such SL process and its experimental validation results. The impacts of different parameters on the overall energy consumption are revealed through a Design‐Of‐Experiments (DOE) methodology. Finally, it gives case studies to illustrate the optimal combination of control parameters.

Chapter 10 presents the energy efficiency modeling and optimization of cellulosic biofuel manufacturing systems. The background knowledge and major processes of cellulosic biofuel manufacturing are first introduced. Then, the formulation of the energy consumption model for cellulosic biofuel manufacturing is illustrated by considering the intra‐process and inter‐process variables. Afterward, the optimization problem is solved through a metaheuristic algorithm, and the energy efficiency improvement under optimal process variables is presented at the end of this chapter.

Chapter 11 demonstrates the energy consumption modeling using Petri nets (PN) and production scheduling optimization for flexible manufacturing systems (FMS). In this chapter, the formulation of a place‐timed PN model for FMS is first introduced, followed by a discussion of a dynamic programming (DP) algorithm to find production schedules that can minimize the energy consumption of small‐size FMS. Next, a Modified DP (MDP) algorithm is presented to solve large‐scale problems by addressing the state explosion issue. Finally, experimental results on FMS are presented to show the effectiveness of MDP.

Part V: Summaries and Conclusions

Chapter 12 summarizes the contribution of this book and highlights several important future research directions. The following figure illustrates the organization of the contents in this book.

How to Read

This book can be used as a reference or a text book for senior and graduate students in mechanical, industrial, environmental, and electrical engineering programs as well as researchers, engineering professionals and policymakers in the areas of energy management and sustainable manufacturing.

The chapters in Parts I and II provide background knowledge and a mathematical foundation for the later chapters and are especially recommended to be read by students and new researchers. The chapters in Part III discuss the energy efficiency and power demand response in typical manufacturing systems and are encouraged to be read in order, as each chapter builds on the concepts in the previous chapter. The chapters in Part IV present the energy management in advanced manufacturing systems and can more or less be approached in any order, as each chapter discusses a different type of manufacturing systems. The last chapter as Part V summarizes this book and is recommended to be read in the end.

Acknowledgements

From the first author of this book:

I would like to thank all the people who have contributed to this book, and the research team at the Sustainable Manufacturing Systems Research Laboratory at the University of Illinois at Chicago for their full dedication and quality research. In particular, I would like to acknowledge the following individuals.

First, I would like to express my great appreciation to this book’s co‐author, Professor MengChu Zhou from the New Jersey Institute of Technology, for his inspirational advices and insightful suggestions to help strengthen the visions and concepts of this book.

I would like to thank the significant help from my doctoral students Lingxiang Yun and Muyue Han for content and material preparations, as well as the research outcomes from my former doctoral students, especially Dr. Yong Wang from Binghamton University, Dr. Zeyi Sun from Missouri University of Science and Technology, Dr. Fadwa Dababneh from German Jordanian University, and Dr. Yiran (Emma) Yang from University of Texas at Arlington.

I would like to appreciate the Wiley‐IEEE Press for providing the opportunity to publish this book, and the esteemed editor and anonymous reviewers for reviewing our work. Special thanks are given to Ms. Teresa Netzler, Senior Managing Editor of Wiley‐IEEE Press at United Kingdom, who is nice and patient to help us move smoothly during our book writing and preparation period.

I would like to acknowledge the funding support for the research contents partially covered in Chapters 6, 7, 9, and 10 from the U.S. National Science Foundation, under Grants CMMI‐1131537, CMMI‐1434392, and CBET‐1604825.

Finally, I truly appreciate the continuous support and endless love from my family, especially my parents, who taught and trained me never giving up anything that you feel deserving of putting efforts.

From the second author of this book:

Numerous collaboration was behind this book and its related work. It would be impossible to reach this status without the following collaborators, some of whom were already mentioned in the first author’s message. In particular, I would like to acknowledge the following individuals:

Professor Keyi Xing, the State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi’an Jiaotong University, China, and his group members, e.g., Dr. Yanxiang Feng (Department of Computer Science and Technology and the State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, China), Dr. Xiaoling Li (now with the School of Electronic and Control Engineering, Chang’an University, Xi’an, China), and Dr. Jianchao Luo (now with the Research and Development Institute and the School of Software, Northwestern Polytechnical University, Xi’an, China), have collaborated with me for many years in the areas of Petri net theory and applications to automated manufacturing systems. Specifically, we have developed several scheduling methods based on timed‐place Petri nets. Some of our collaborated contributions are reflected in Chapter 11 of this book. I have enjoyed much collaboration with many outstanding researchers in the area of intelligent automation and transportation and sustainable manufacturing, e.g., Professors Naiqi Wu (Fellow of IEEE, Macau Institute of Systems Engineering, Macau University of Science and Technology, China), Zhiwu Li (Fellow of IEEE, Macau Institute of Systems Engineering, Macau University of Science and Technology, China), and Maria Pia Fanti (Fellow of IEEE, Dipartimento di Elettrotecnica ed Elettronica, Polytechnic of Bari, Italy).

I have enjoyed the great support and love from my family (my wife, Fang Chen, my two sons, Albert and Benjamin) for long. It would be impossible to accomplish this book and many other achievements without their support and love.

The work presented in this book was in part supported by the National Natural Science Foundation of China (NSFC) under grant no. 61573278, FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia) under Grant No. 0047/2021/A1, and Lam Research Corporation through its Unlock Ideas program.

Lin Li, University of Illinois, Chicago, IL, USAMengChu Zhou, New Jersey Institute of Technology, Newark, NJ, USA20 May 2022

List of Figures

Figure 1.1 Changes in MVA among countries of different regions over the time from 2004 to 2017

Figure 1.2 Challenges in the manufacturing sector

Figure 1.3 The US energy consumption by sector in 2019

Figure 1.4 The total US GHG emissions by sector in 2018

Figure 1.5 The three key pillars in sustainability

Figure 1.6 Illustration of the connections among manufacturing, remanufacturing, and de‐manufacturing

Figure 1.7 Illustrations of product manufacturing stages

Figure 1.8 Schematic diagram of a job shop, where A, B, and C represent three types of machines

Figure 1.9 Schematic diagram of a project shop, where A, B, and C represent three types of machines

Figure 1.10 Schematic diagram of a cellular system, where A, B, C, and D represent four types of machines

Figure 1.11 Schematic diagram of a flow line, where A–F represent six types of machines

Figure 1.12 Schematic diagram of a continuous system, where A, B, and C represent three types of machines

Figure 1.13 Selections of manufacturing systems

Figure 1.14 Four layers of sustainable manufacturing problems

Figure 2.1 The US primary energy consumption by energy source in 2019

Figure 2.2 The US electricity generation by energy source in 2020

Figure 2.3 Illustration of the supply chain of electricity

Figure 2.4 The schematic diagram of a typical steam generation and distribution system

Figure 2.5 The schematic diagram of a typical compressed air distribution system

Figure 2.6 Illustration of energy consumption in the US manufacturing sector by energy

Figure 2.7 The direct process end use energy consumption in the US manufacturing sector

Figure 2.8 The direct non‐process end use energy consumption in the US manufacturing sector

Figure 2.9 The US industrial energy consumption in the AEO 2020 reference case

Figure 2.10 Total GHG combustion emissions in US manufacturing by energy end‐use type

Figure 2.11 The cost of energy consumption in US manufacturing by energy type

Figure 2.12 Three levels of energy management in manufacturing

Figure 2.13 A typical electrical load profile of a machining center

Figure 2.14 Schematic diagram of a typical serial production line with N machines and N − 1 WIP buffers

Figure 2.15 Energy efficiency comparison between CHP system and separated heat and power generation systems

Figure 2.16 Illustration of convective and radiant heat transfer due to manufacturing operations

Figure 2.17 Impacts of energy efficiency and demand response programs on electricity demand

Figure 2.18 Illustration of cost compositions of electricity in a manufacturing plant

Figure 2.19 The peak demand for time‐of‐use rate

Figure 2.20 Illustration of seven‐step guidelines for energy management

Figure 2.21 Industrial sector annual incremental savings resulting from energy efficiency programs

Figure 2.22 The US hourly electric demand in December 2020

Figure 2.23 Classification of demand response programs

Figure 2.24 TOU rate plans offered by SCE with the price per kilowatt‐hour

Figure 2.25 Industrial sector total annual savings resulting from demand response programs

Figure 3.1 Illustration of Venn diagram

Figure 3.2 Venn diagram of mutually exclusive events

Figure 3.3 Venn diagram of complement

Figure 3.4 Venn diagram of inclusion

Figure 3.5 Venn diagram of addition

Figure 3.6 Venn diagram of conditional probability

Figure 3.7 The Venn diagram of total probability

Figure 3.8 Examples of PMF (left) and CDF (right) of a discrete random variable

Figure 3.9 Two example PMFs of Bernoulli distribution

Figure 3.10 The PMFs of Poisson distribution with different value of λ

Figure 3.11 Examples of the survival function of geometric distribution

Figure 3.12 The state transition diagram of a geometric machine

Figure 3.13 Examples of PDF and CDF for a continuous random variable

Figure 3.14 Example survival functions of the exponential distribution

Figure 3.15 State transition diagram of an exponential machine

Figure 3.16 The survival function of Weibull distribution with parameter values

Figure 3.17 State transition diagram

Figure 3.18 Illustration of fundamental components in Petri net

Figure 3.19 The Petri net for Example 3.8

Figure 3.20 The solutions for Example 3.9

Figure 3.21 Sequential structure in Petri net

Figure 3.22 Concurrent structure in Petri net

Figure 3.23 Conflicting structure in Petri net

Figure 3.24 Cyclic structure in Petri net

Figure 3.25 (a) The workflow for circuit board soldering, (b) determination of states, and (c) the final state machine Petri net

Figure 3.26 (a) The workflow of a demonstrative machine, (b) determination of sub‐nets, and (c) the final marked graph

Figure 3.27 The workflow of a system with three machines two buffers

Figure 3.28 Sub‐nets for subsystems: (a) Machine M1 and M2, (b) buffer B1 and B2, and (c) machine M3

Figure 3.29 The complete Petri net for the entire system

Figure 3.30 Solutions for Example 3.10: (a) convert a place, (b) convert an arc between place and transition, and (c) convert an arc between transition and place

Figure 3.31 Example of stochastic timed Petri net

Figure 3.32 The solution space of a constrained two‐dimensional optimization problem

Figure 3.33 Illustration of local and global optima

Figure 3.34 Global optimum and near optima

Figure 3.35 The schematic diagram of Pareto‐optimal solutions

Figure 3.36 The flow chart of a genetic algorithm (GA)

Figure 3.37 The process of constituting the next generation based on the previous generation

Figure 3.38 Different types of crossover methods: (a) one‐point crossover, (b) two‐point crossover, and (c) uniform crossover

Figure 3.39 An example of the mutation operation

Figure 3.40 The basic flow chart of the PSO algorithm

Figure 3.41 An example of pbest and gbest updating in a one‐dimensional minimization problem: (a) first iteration, (b) second iteration, and (c) third iteration

Figure 3.42 Updating the velocity and position of a particle j

Figure 4.1 The schematic diagram of parallel machines

Figure 4.2 The schematic diagram of synchronous‐dependent machines

Figure 4.3 Aggregating parallel or synchronous‐dependent machines

Figure 4.4 The schematic diagram of a serial production line

Figure 4.5 The schematic diagram of an assembly system

Figure 4.6 The schematic diagram of a production line with rework

Figure 4.7 The schematic diagram of the time to failure and the time to repair

Figure 4.8 The schematic diagram of a segment of a production line

Figure 4.9 Situations where (a) Mi is not blocked, and (b) Mi is blocked

Figure 4.10 Situations where (a) Mi is not starved, and (b) Mi is starved

Figure 4.11 The relationship among blockage, starvation, up and down states of a machine

Figure 4.12 The layout of a two‐machine production line

Figure 4.13 The relationships among machine states for (a) M1 and (b) M2

Figure 4.14 State transition diagram of buffer B

Figure 4.15 PMF of buffer occupancy in two‐machine line with identical Bernoulli machines

Figure 4.16 PMFs of buffer occupancy in a two‐machine production line with nonidentical Bernoulli machines

Figure 4.17 The layout of a production line with N machines and N − 1 buffers

Figure 4.18 The relationships among different machine states

Figure 4.19 State transition diagram of buffer Bi

Figure 4.20 A flowchart of an iteration‐based method

Figure 4.21 Evolution of state probabilities for (a) buffer B1, (b) buffer B2, (c) buffer B3, and system measure (d) WIP

Figure 4.22 Evolution of WIPSYS with different initial conditions

Figure 4.23 System layout of a production system coupled with MHS

Figure 4.24 Evolution of state probabilities for (a) buffer B1, (b) buffer B2, (c) buffer B3, and system measure (d) WIP

Figure 5.1 Classifications of energy consumption modeling methods

Figure 5.2 Illustration of the operation‐based energy model

Figure 5.3 Illustration of the machine energy profile

Figure 5.4 The relationships between the control variable and machine states

Figure 5.5 Illustration of a component‐based energy model

Figure 5.6 Schematic diagram of a machine consisting of n components

Figure 5.7 Schematic diagram of the major components in a stereolithography‐based 3D printing machine

Figure 5.8 The relationship among inter‐process variables, intra‐process variables, and processes

Figure 5.9 Block diagram of the biofuel production from biomass

Figure 5.10 Illustrations of three window sliding strategies. (a) Sliding from left to right, (b) sliding from right to left, and (c) sliding continuously from left to right or from right to left

Figure 5.11 Energy profile of the system in Example 5.3

Figure 5.12 Different time periods in a (a) summer and (b) winter weekday

Figure 5.13 Comparisons between TOU and CPP

Figure 6.1 Scenario switching savings (%) of the following cases: (a) One‐shift, Scenario 0→1; and (b) One‐shift, Scenario 0→2

Figure 6.2 Scenario switching savings (%) of the following cases: (a) Two‐shift, Scenario 0→1; and (b) two‐shift, Scenario 0→2

Figure 6.3 Scenario switching savings (%) of the following cases: (a) three‐shift, Scenario 0→1; and (b) three‐shift, Scenario 0→2

Figure 6.4 A typical serial production system with N machines and N − 1 buffers

Figure 6.5 Best schedules for Formulation 1 based on the TOU rates in summer

Figure 6.6 Best schedules for Formulation 1 based on the TOU rates in winter

Figure 6.7 Best schedules for Formulation 2 based on the TOU rates in summer

Figure 6.8 Best schedules for Formulation 2 based on the TOU rates in winter

Figure 6.9 The number of historical events in PGE (a) by month and (b) by weekday

Figure 6.10 The number of historical events in SCE (a) by month and (b) by weekday

Figure 6.11 The number of historical events in SDGE (a) by month and (b) by weekday

Figure 7.1 Comparison between CHP system and SHP generation systems

Figure 7.2 Illustration of a combined manufacturing and CHP system

Figure 7.3 Encoding scheme of position and velocity matrices of each particle

Figure 7.4 Heat demand (kWh) of the facility

Figure 7.5 Power consumption of each interval in Scenario I

Figure 7.6 Heat demand and supply of each interval in Scenario I

Figure 8.1 Schematic diagram of the discrete time and continuous time

Figure 8.2 Illustrations of the convective and radiant heat transfer due to manufacturing operations at different time intervals

Figure 8.3 Encoding scheme of position and velocity matrices of each particle

Figure 8.4 Radiant heat fraction series

Figure 8.5 Outdoor temperature during each interval

Figure 8.6 Outdoor temperature on a warmer day during each interval

Figure 8.7 Target temperature set by HVAC and indoor temperature evolution in Scenario I

Figure 8.8 Power consumption of each interval in Scenario I

Figure 8.9 Power consumption of each interval in Scenario II

Figure 9.1 A schematic diagram of the MIP SL‐based AM process

Figure 9.2 The major components in MIP SL‐based AM process

Figure 9.3 Factorial analysis results: (a) Pareto chart and (b) normal plot

Figure 9.4 Adequacy checking for factorial design model

Figure 9.5 Adequacy checking for the refined statistical model

Figure 9.6 Response optimization results and surface plots: (a) response optimization results; (b) surface plot of energy consumption versus A and B, A and D, B and D

Figure 9.7 Product surface quality comparison: (a) default conditions, (b) different layer thickness, (c) different curing time, (d) optimized conditions

Figure 10.1 Block diagram of biofuel production from cellulosic biomass

Figure 10.2 Reaction temperature profile during pretreatment, enzymatic hydrolysis, and fermentation processes

Figure 10.3 Distribution of energy consumption by process

Figure 10.4 Heating energy breakdown

Figure 10.5 Averaged optimal results of energy consumption with 95% confidence interval

Figure 11.1 The PNS model of an FMS in Example 11.1: (a) PNS formulation step one, and (b) PNS formulation step two

Figure 11.2 (a) R(p) is in occupied state during [τl − 1, τl]; (b) R(p) is in working state during [τl − 1, h(p, i)] and occupied state in [h(p, i), τl]; (c) R(p) is in working state during [τl − 1, τl]

Figure 11.3 The controlled PNS

Figure 11.4 Partial RG of the model in Figure 11.3

Figure 11.5 The first four stages of RRG

Figure 11.6 The PNS model of the FMS

Figure 12.1 The life cycle of a product and associated research topics

Figure 12.2 Smart manufacturing towards enhanced sustainability

Figure 12.3 An example of industrial symbiosis

Figure 12.4 Supply chain planning strategy for bioethanol production in the state of Illinois in the United States

Figure 12.5 Illustration of strategies involved in the circular economy

Figure 12.6 Illustration of lifecycle stages involved in the LCA

Part IIntroductions to Energy Efficiency in Manufacturing Systems

1Introduction

In this chapter, the background knowledge about manufacturing systems and concept of sustainable manufacturing are demonstrated. In Section 1.1, an overview of the current status of manufacturing industry development is given, followed by a discussion on existing challenges that need to be addressed in order to sustain the continuous growth in manufacturing sectors. More specifically, the significant obstacles that may impede the sustainable development of manufacturing industries are discussed, and the implications for sustainability and energy efficiency in manufacturing systems are depicted. In addition, the definition of sustainable manufacturing and associated essential factors are demonstrated in Section 1.1.2. To better illustrate the significance of the industrial transition to sustainable manufacturing, several industrial paradigms and representative case studies are presented to strengthen the connections between the concepts of sustainable manufacturing and real‐world problems. In Section 1.2, the key components of manufacturing systems are discussed from the perspective of a product life cycle. A series of representative manufacturing systems are demonstrated, which are associated with the discussions on system configurations, component functionality, and respective system performances. Section 1.3 is the overview of the problem statement and scope, which are facilitated by the hierarchical categorization of research expertise under the context of sustainable manufacturing.

1.1 Definitions and Practices of Sustainable Manufacturing

1.1.1 Current Status of Manufacturing Industry

Ever since the conception of industrialization, manufacturing production, as an indispensable corner stone, is of decisive significance to the development of the world economy. The concept of the manufacturing value added (MVA) is proposed as one of the main indicators to measure the growth rate of manufacturing industry. By definition, it represents the total estimate of net output of all resident manufacturing activity units obtained by adding outputs and subtracting intermediate consumption [1]. According to the data released from the World Bank, in 2018, the total MVA has reached USD 13.976 trillion worldwide, which corresponds to approximately 16.82% of the global gross domestic product (GDP). Although the trend of declining manufacturing has been reported in developed regions due to the increased wage share of skilled worker and the competition from the service industry and other tertiary sectors [2, 3], manufacturing industries still maintain a good growth momentum and sustain their market value, especially in some developing countries and emerging economies. In reference to the World Bank report [4], Figure 1.1 illustrates the variations of MVA among countries of different regions over the period from 2004 to 2017. In particular, United States, European Union, China, and India are selected as the demonstrative regions.

Figure 1.1 Changes in MVA among countries of different regions over the time from 2004 to 2017.

Source: Adapted from [4].

As demonstrated in Figure 1.1, in the developed regions, such as United States and European Union, a steady increase in MVA can be observed. For example, the MVA in the United States was USD 1.61 trillion in 2004, and it increased by 34.8% to USD 2.17 trillion in 2017; a similar increment in MVA was also reported in the European Union. It is worth noting that fluctuations accompany the rise in MVA after 2008, which can be attributed to the relatively slower growth in manufacturing sectors after the nations survive during the post‐economic‐crisis period. Distinctively, in terms of the developing region, rapidly industrializing countries such as China and India possessed a significant increase in MVA over the past decades. In particular, the value added of the China manufacturing sector was USD 0.63 trillion in 2004, which was approximately 2.5 times less than the counterpart in the United States during the same period. The MVA in China was continuously increasing during the study period and reached USD 3.46 trillion in 2017, which indicates a total increase of 449.2% since 2004. The continuous rise of MVA also suggests that the manufacturing sectors remain one of the main propellers of economic growth. It can be depicted that the manufacturing industry has a pivotal position in the world economy and should sustain its pace of change in the foreseeable future.

1.1.2 Sustainability in the Manufacturing Sector and Associated Impacts

Despite the rapid development in a manufacturing sector, the proliferation of manufacturing systems also brings about sustainability concerns. More specifically, manufacturing in the traditional sense refers to processing raw materials to make useful products. In such manufacturing systems, production activities are built upon the consumption of feedstock materials, energy, and other resources. Meanwhile, production processes are often coupled with manufacturing emissions, waste generation, and residual heat. With the increasing consensus on resource scarcity and environmental sustainability, nowadays, the manufacturing industries face more and more challenges, such as efficient energy management, greenhouse gas (GHG) emissions, waste material, and resource reclamation. In general, the main challenges in the manufacturing industry can be examined from the following three aspects: economy, environment, and society, as demonstrated in Figure 1.2.

In terms of economic challenges, considering the high dependence of manufacturing industries on energy resources, the cost fluctuations in the energy market can significantly affect the manufacturing output and overall production cost. For example, the price of crude oil reached about USD 166 per barrel in 2008, which dealt a severe blow to the manufacturing industry during the 2008 international financial crisis [5]