Оглавление
Группа авторов. Industry 4.0 Vision for the Supply of Energy and Materials
Industry 4.0 Vision for the Supply of Energy and Materials. Enabling Technologies and Emerging Applications
Contents
List of Figures
List of Tables
Guide
Pages
Preface
1 Connectivity through Wireless Communications and Sensors
1.1 Introduction: Key Technologies Enablers for Industry 4.0 and Supply Chain 4.0
1.1.1 Background
1.1.2 Future Manufacturing Vision
1.1.3 Key Features of Industry 4.0
1.1.4 Key Technologies Enablers for Industry 4.0
1.1.4.1 Cyber-Physical Systems
1.1.4.2 Internet of Things
1.1.4.3 Internet of Service
1.2 Drivers, Motivations, and Applications for Communication
1.2.1 Wireless Instrumentation
1.2.2 Mobile Technologies
1.2.3 Asset and Personnel Management
1.2.4 Safety Management and Systems
1.2.5 Security and Surveillance
1.3 Design Criteria and Communication Requirements in the Industry 4.0 Era
1.3.1 Reliability
1.3.2 Latency
1.3.3 Coverage
1.3.4 Power Efficiency
1.3.5 Simplicity
1.3.6 Scalability
1.3.7 Security
1.4 Wireless Technology and International Standards
1.4.1 Short-Range Wireless Communication
1.4.1.1 IEEE 802.15.4 Standard
1.4.1.2 IEEE 802.15.1 Standard
1.4.1.3 IEEE 802.11 Standard (Wi-Fi)
1.4.2 Long-Range Wireless Communication
1.4.2.1 Low Power Wide Area Network (LPWAN)
1.4.3 Comparative Study of Wireless Standards for Industrial IoT
1.5 Cellular and Mobile Technologies
1.5.1 3GPP Cellular: MTC
1.5.1.1 3GPP MTC Standardization
1.5.1.2 MTC Technical Requirements
1.5.1.3 MTC Trade-Off for Different Cellular Generations
1.5.2 LTE Features Enhancement
1.5.3 4G Features Enhancement
1.5.4 5G Features Enhancement
1.5.4.1 Ultra-Reliable and Low Latency Communications
1.5.4.2 Enhanced Mobile Broadband
1.5.4.3 Massive Machine Type Communication
1.6 Wireless System Design Enablers and Metrics for Emerging IIoT Applications
1.6.1 General Technical Enablers in Design of Wireless Network for IIoT
1.6.1.1 System Modeling and Verification
1.6.1.2 Radio Resource Management
1.6.1.3 Protocol Interface Design
1.6.2 Metrics for Wireless System Design in IIoT
1.6.2.1 Resource Indicators
1.6.2.2 Service Indicators
1.7 MAC Protocols in IIoT
1.7.1 Scheduled-Based Schemes
1.7.2 Contention-Based Schemes
1.7.3 Hybrid Schemes
1.8 Smart Sensors
1.8.1 Benefits of Smart Sensors in the Supply Chain
1.8.2 Criteria for Adoption of Smart Sensors for Industry 4.0
1.8.3 Key Leverage for Smart Sensors in Supply Chain 4.0
1.9 Future Trends in Wireless Communication for Industry 4.0
1.9.1 Diverse Communication Requirements for Different Industrial Use Cases
1.9.1.1 Possible Solutions
1.9.2 Challenges in Cellular and Mobile Technologies for Industrial Networking
1.9.2.1 Possible Solutions
1.9.3 Interoperability of Wireless Communication in Industry 4.0
1.9.3.1 Possible Solutions
1.10 Conclusion
References
Notes
2 Blockchain and Smart Contracts
2.1 Blockchain Overview
2.2 Blockchain Attributes
2.3 Blockchain Taxonomy
2.4 Blockchain Architectures
References
3 Robotics: A Key Driver of Industry 4.0
3.1 Introduction
3.2 Classification of Robotic Systems
3.2.1 Classification Concerning Geometry
3.2.2 Classification Concerning the Actuator System
3.2.3 Classification Concerning the Control Strategy
3.2.4 Classification Concerning Kinematics (Motions)
3.2.5 Classification Concerning the Number of Agents
3.2.5.1 Multi-Robot Systems
3.2.5.1.1 Search and Rescue
3.2.5.1.2 Foraging and Flocking
3.2.5.1.3 Formation and Exploration
3.2.5.1.4 Cooperative Manipulation
3.2.5.1.5 Team Heterogeneity
3.2.5.1.6 Adversarial Environment
3.2.6 Classification with Respect to Robot Materials
3.2.6.1 Main Advantages of Soft Robots
3.2.6.2 Main Applications of Soft Robots
3.2.6.2.1 Locomotion and Exploration
3.2.6.2.2 Human–Machine Interaction and Interface
3.2.6.2.3 Medical and Surgical Applications
3.2.6.2.4 Applications with High Degrees of Freedom
3.2.6.2.5 Rehabilitation and Wearable Robots
3.3 Applications of Robotics
3.3.1 Medical Application
3.3.2 Space Exploration
3.3.3 Military
3.3.4 Education
3.3.5 Agriculture
3.3.6 Oil and Gas Industry
3.3.7 Textile
3.3.8 Railcar Industry
3.3.9 Maintenance and Repair
3.3.10 Construction Industry
3.3.11 Environmental Issues
3.3.12 Security Services
3.3.13 Social Assistance
3.3.14 Travel Industry
3.3.15 Human-Interactive Applications
3.3.15.1 Performance Robots
3.3.15.2 Teleoperated Performance Robots
3.3.15.3 Operation Robots
3.3.15.4 Interactive Autonomous Robots
3.4 Challenge and Future Trends. 3.4.1 General Challenges in the Robotic Field
3.4.1.1 Locomotion Systems
3.4.1.2 Sensors
3.4.1.3 Computer Vision Algorithms
3.4.2 Challenge and Future Trends in Multi-Robot Systems
3.5 Summary and Conclusion
References
4 Cloud Computing and Its Impact on Industry 4.0: An Overview
4.1 Introduction
4.1.1 What Is Cloud Computing?
4.1.2 Concepts and Terminologies
4.1.2.1 IT Resource
4.1.2.2 Cloud
4.1.2.3 Cloud Provider and Cloud Consumer
4.1.2.4 Cloud Service
4.1.2.5 Scaling
4.1.2.6 Virtualization
4.2 Architecture and Types of Services in Cloud Computing
4.2.1 Infrastructure as a Service
4.2.2 Platform as a Service
4.2.3 Software as a Service
4.2.4 Serverless Cloud Computing
4.2.4.1 Serverless Application Patterns
4.3 Main Characteristics
4.3.1 Multitenancy and Resource Pooling
4.3.2 On-Demand Usage
4.3.3 Elasticity
4.3.4 Broad Network Access
4.3.5 Measured Usage
4.3.6 Resiliency
4.4 Cloud Deployment Models. 4.4.1 Types of Clouds
4.4.1.1 Public Clouds
4.4.1.2 Private Clouds
4.4.1.3 Hybrid Clouds
4.4.1.4 Community Clouds (Federations)
4.4.2 Public vs. Private Clouds
4.4.3 Hybrid Clouds vs. Cloud Federations
4.5 Points, Risks, and Challenges
4.5.1 Advantages. 4.5.1.1 Cost Savings
4.5.1.2 Scalability and Elasticity
4.5.1.3 Accessibility
4.5.1.4 Agility
4.5.1.5 Reliability
4.5.1.6 Security
4.5.2 Risks and Challenges
4.5.2.1 Security Issues
4.5.2.2 Less Control
4.5.2.3 Migration
4.5.2.4 Vendor Lock-In
4.5.2.5 Accessibility Compromised
4.5.2.6 Legal Issues
4.6 Grid, Edge/Fog, and Cloud Computing. 4.6.1 Grid Computing
4.6.2 Relationship between Grid and Cloud Computing
4.6.3 Edge Computing
4.6.3.1 Edge Computing vs. Cloud Computing
4.6.4 Fog Computing
4.6.4.1 Relationship between Edge Computing and Fog Computing
4.7 Summary
References
5 Applications of Artificial Intelligence and Big Data in Industry 4.0 Technologies
5.1 Introducing Artificial Intelligence (AI)
Connectionist
Evolutionary or Bio-Inspired
Bayesian
By Analogy or Instance-Based Learning
Inductive Logic Programming
Supervised Machine Learning
Transfer Learning
5.2 Classification Methods
Decision Trees
The (Naive) Bayesian Classifier
K-Nearest Neighbors
Linear Discriminant Analysis (LDA)
Support Vector Machines and Kernel Methods
Relevance Vector Machines
Ensemble Methods
Logistic Regression
5.3 Regression Methods
Ordinary Least Squares (OLS) Regression
Ridge and Lasso Regression
Support Vector Regression
Gaussian Process Regression (GPR)
Thin Plate Spline
5.4 Unsupervised Learning
Clustering
K-Means Clustering
Hierarchical Clustering
Nonlinear Dimensionality Reduction
Laplacian Eigenmaps Method
Topological Data Analysis
5.5 Semi-supervised Learning and Active Learning
Graph-Based Models
Generative Models
5.6 Neural Networks and Deep Learning
Convolutional Neural Networks (CNN)
Recurrent Neural Networks
Long Short-Term Memory (LSTM)
Transformers
Graph Neural Networks
Generative Adversarial Networks (GAN)
Autoencoders
Self-Organizing Maps
5.7 Bio-Inspired Methods
5.8 Reinforcement Learning
5.9 Time Series Analysis
Bibliography
Notes
6 Multi-Vector Internet of Energy (IoE) A Key Enabler for the Integration of Conventional and Renewable Power Generation
6.1 Challenges of the Conventional Power Grid and Need for Energy Internet
6.2 Internet of Energy. 6.2.1 Concept and Definition
6.2.2 IoE Structure
6.2.2.1 Energy Router
6.2.2.2 Energy Hub
6.2.2.3 The IoE Network
6.2.2.3.1 Software-Defined Networks: Concept and Features
6.2.2.3.2 SDN-Based Solutions for Smart Grid Problems
6.2.2.3.3 SDN Architecture for IoE
6.2.2.4 Information Technology for IoE. 6.2.2.4.1 Features of IoE Data
6.2.2.4.2 Architectural Requirements of IoE Big Data
6.2.2.4.3 IoE Big Data Platform
6.2.2.4.4 IoE Big Data Challenges
6.2.2.4.5 IoE Data Uncertainties Management
6.2.3 Applying Blockchain in IoE: Capabilities and Challenges. 6.2.3.1 Blockchain Definition
6.2.3.2 Opportunities for the Application of Blockchains in IoE
6.2.3.3 Challenges of the Application of Blockchains in IoE
6.2.4 Energy Trading in the IoE
6.2.5 Transactive IoE. 6.2.5.1 Transactive IoE: The Concept and Enablers
6.2.5.2 IoT Architecture for Transactive IoE
6.2.5.3 Transactive IoE Infrastructure
6.2.5.3.1 Ami
6.2.5.3.2 Smart Inverter
6.2.5.3.3 Energy Router Differences in Different Energy Domains
6.2.5.3.4 Microgrid
6.2.5.3.5 P2P Energy Trading Platforms
6.2.6 Integrated Demand Response as the Next Generation of Demand Response
6.3 Summary and Conclusion
References
7 The Economic Implication of Integrating Investment Planning and Generation Scheduling The Application of Big Data Analytics and Machine Learning
7.1 Introduction
7.2 Case Study
7.3 Methodology
7.3.1 Machine-Learning Algorithms for Predictive Models
7.3.2 Simultaneous Optimization Programming of Generation Expansion and Electricity Dispatch
7.3.2.1 Objective Function
7.3.2.2 Optimization Decision Variables
7.3.2.3 Constraints of Electricity Balance
7.3.3 Incorporating the Prediction Errors into the Programming of the Stochastic Scenarios
7.4 Results
7.4.1 Predictive Modeling Results
7.4.1.1 Prediction Errors in Machine Learning
7.4.2 Generation Expansion and Electricity Dispatch Results
7.4.2.1 Economic Performance
7.5 Conclusions
7.6 Nomenclature
References
Appendix
7.1.1 Constraints of Generators and Storage
7.1.2 Constraints of Transmission
7.1.3 Prediction Models for Solar Power and Electricity Demand
7.2 Generation Sites and Transmission Lines
7.2.2 The Existing Natural Gas Generation Sites
7.2.3 The Existing Nuclear Generation Sites
7.2.4 The Wind Power Generation
7.2.5 The Solar Power Generation
7.2.5.1. The Energy Storage Capacities
7.2.5.2 The Capacity Factors
7.2.5.3 The Parameters of the Objective Function for Each Generation Technology
References
8 A Systematic Method for Wireless Sensor Placement A Fault-Tolerant Communication Solution for Monitoring Water Distribution Networks
8.1 Introduction
8.2 Problem Statement
8.2.1 Connectivity Constraint
8.3 Multi-Objective Optimization Programming
8.3.1 Multi-objective Genetic Algorithm
Else
8.3.3 Observability Constraint
8.3.5 Check Observability Algorithm for the Network That Is Connected Against nfailure
8.4 Hierarchical Clustering
8.5 Results and Discussion
8.6 Conclusion
References
9 An Overview of the Evolution of Oil and Gas 4.0
9.1 Oil and Gas 4.0: Sensors, Communicators, and Analysis. 9.1.1 Optimizing Operations in the Petroleum Industry
9.1.2 Data Access: Now and in Industry 4.0
9.1.3 The Digital Twin
9.1.4 Challenges in Introducing Industry 4.0
9.2 The RAMI Architecture. 9.2.1 Introduction to the RAMI Architecture
9.2.2 The Life Cycle Dimension
9.2.3 Tying Asset Information Together: The Asset Administration Shell
9.2.4 The Implementation Layers
9.2.5 Industrial Internet of Things
9.3 The Asset and Integration Layer: Novel Sensors. 9.3.1 The Role of Novel Sensors in the Process Industries
9.3.1.1 Spectrometer on a Chip
9.3.1.2 Lab on a Chip
9.3.1.3 Distributed Temperature Sensors
9.3.1.4 Trace Gas Sensors
9.3.2 Soft Sensors
9.4 The Open Integration Layer. 9.4.1 Introduction
9.4.2 Open Process Automation Framework
9.4.3 NAMUR Open Architecture
9.4.4 NAMUR Module Type Package (MTP)
9.4.5 Conclusion
9.5 The Communication and Information Layer. 9.5.1 Industry 4.0 Communication Protocols and Middleware
9.5.2 Opc Ua
9.5.3 Messaging Middleware: MQTT and AMQP
9.6 The Functional Layer. 9.6.1 Introduction
9.6.2 Corrosion Under Insulation
9.6.2.1 Predictive Maintenance
9.6.3 Equinor Use Cases
9.6.3.1 Offshore wind
9.6.3.2 Krafla/Spine Project
9.6.3.3 OPC UA Implementation in Johan Sverdrup
9.7 Conclusions and Way Forward
References
Notes
10 Electrification of Transportation Transition Toward Energy Sustainability
10.1 The Trend of Transportation Electrification
10.2 Transportation Electrification Opportunities
10.2.1 Environmental and Health Benefits
10.2.2 Economic and Social Benefits
10.3 Barriers and Challenges of Transportation Electrification
10.4 Electrification Technologies
10.4.1 Degree of Electrification
10.4.2 Electric Motors
10.4.3 Battery Technology of EVs
10.4.4 Charging Technology and Infrastructures
10.4.5 Developing Technologies of the Future Transportation
10.4.5.1 V2G technology
10.4.5.2 Connected and Autonomous Vehicle Technology
10.4.5.3 Communication Technologies
10.4.5.4 Blockchain Technology
10.4.5.5 Artificial Intelligence and Robotic Technology
10.4.5.6 Cybersecurity in Electrified Transportation
10.5 Summary and Conclusion
References
11 Computer-Aided Molecular Design Accelerating the Commercialization Cycle
11.1 Introduction
11.2 Computer-Aided Molecular Design
11.3 Different Classes of CAMD
11.4 Implementation of a CAMD Problem
11.4.1 Generate and Test Methods
11.4.2 Decomposition Methods
11.4.3 Optimization Methods (Deterministic, Stochastic, and Heuristic)
11.5 Application of Predictive Models in CAMD
11.5.1 Quantitative Structure–Property Relationships
11.5.2 Group Contribution Methods
11.5.3 Quantum Mechanical Models
11.6 Different Applications of CAMD
11.6.1 Fuel- and Oil-Related Applications
11.6.2 Solvent Design and Selection for Separation
11.6.3 Design of Heat Transfer Fluids
11.6.4 Reaction and Reactor Applications
11.6.5 Molecular Catalysts
11.7 Conclusion
References
12 Pharmaceutical Industry Challenges and Opportunities for Establishing Pharma 4.0
12.1 Pharma 4.0: Smart Manufacturing for the Pharmaceutical Industry
12.2 Regulatory Driving Force for Pharma 4.0
12.3 Smart Manufacturing in Pharmaceutical Industry
12.3.1 Continuous Manufacturing
12.3.2 Process Analytical Technologies
12.3.3 Emergence of Microfluidic Techniques for Continuous Manufacturing and PAT
12.3.4 Digitalization of Pharmaceutical Process
12.4 Summary and Outlook
References
13 Additive Manufacturing A Game-Changing Paradigm in Manufacturing and Supply Chains
13.1 The Additive Manufacturing Concept
13.2 Category
13.2.1 Binder Jetting
13.2.2 Direct Energy Deposition
13.2.3 Material Extrusion
13.2.4 Material Jetting
13.2.5 Powder Bed Fusion
13.2.6 Sheet Lamination
13.2.7 Vat Photopolymerization
13.3 The Pros and Cons of Seven AM Processes
13.3.1 Binder Jetting
13.3.2 Direct Energy Deposition
13.3.3 Material Extrusion
13.3.4 Material Jetting
13.3.5 Powder Bed Fusion
13.3.6 Sheet Lamination
13.3.7 Vat Photopolymerization
13.4 New Materials Used in 3D Printing
13.4.1 Digital Materials
13.4.2 Smart Materials for 4D Printing
13.4.3 Ceramics Materials
13.4.4 Electronic Materials
13.4.5 Biomaterials
13.5 Current Challenges and Future Trends. 13.5.1 Volume and Cost of the Production
13.5.2 The Quality of the Product
13.5.3 Post-Processing
13.5.4 Repairs and Maintenance
13.5.5 Low Variety of Materials
13.5.6 3D Printing Technology AM Costs
13.6 Conclusion
References
Notes
Glossary
Index
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