costly energy storage or provision of standby power plant capacity that is usually driven by fossil fuels. However, a paradigm shift has emerged using advanced control and communication systems that would deploy predictive analytics for real-time optimization, eliminating the need for expensive solutions such as energy storage and power plant extra capacity. The chapter explores the economic benefits of utilizing such intelligent systems, through the integration of artificial intelligence in the form time-series prediction, and optimization programming for electricity expansion planning and real-time dispatch.
Chapter 8 explores another specific utilization of Industry 4.0 technologies in the water distribution networks. Water pipelines are prone to operational issues such as aging, leakage, water theft, and sabotage attacks. The utilization of smart sensors quipped with wireless and cellular communication technologies opens up new avenues for monitoring and fault detection. Nonetheless, there is always a trade-off between the number of utilized sensors and selected technology (as well as associated costs) and the observability and possibility of fault detection. In this chapter, a systematic method based on multi-objective optimization is presented that, while minimizing the costs, ensures a certain degree of observability for the network, even in the case of multiple sensor failures.
Chapter 9 is focused on the evolution of oil and gas industries utilizing Industry 4.0 technologies. The discussions include the introduction of recent trends such as data acquisition and processing systems, smart and soft sensors, and digital twins, as well as challenges that need to be addressed for their commercial implementation. More attention is paid to the architecture that allows the fusion of Industry 4.0 technologies such as cloud computing, sensor-to-cloud connectivity, 5G, industrial Internet of things (IIoTs), and AI, with emphasis on standards that enable such integration. The chapter concludes by exploring potential future developments.
In recent years, the transformation of fossil-driven vehicles using electric engines has revolutionized the transportation industry. Chapter 10 studies the impact of Industry 4.0 on transportation electrification. The features of interest include the environmental, economic, and societal benefits that are achievable from such transformation, as well as corresponding barriers and challenges. A deep discussion of the electrification technologies is provided with special attention to the degree of electrification, types of electric motors, required battery and charging technologies, as well as connectivity of vehicles to grid (V2G), other vehicles (V2V), infrastructure (V2I), buildings (V2B), and clouds (V2C) technologies, which can promote energy efficiency as well as traffic safety. Other integrating Industry 4.0 technologies include blockchains, artificial intelligence, cyber-security, and robotics are also discussed in this chapter.
Dramatic advances in computational capabilities have also revolutionized the way that products and services are developed and dramatically have shortened their time to market. This is the focus of Chapter 11, with emphasis on the role that computer-aided molecular design (CAMD) plays in reducing the costs of designing new materials and products in the form of predicting their properties and reducing requirements for physical experimentation. Different CAMD methods are discussed, and implementation procedures are presented. This chapter also reviews the emerging and novel applications of CAMD in the industry.
Chapter 12 evaluates the impact of Industry 4.0 on the pharmaceutical industry. The discussions include the regulatory considerations in this sector, and the recent trends in smart manufacturing of pharmaceuticals in the form of continuous processing, analytical technologies, and digitalization.
The final chapter of the book explores additive manufacturing as a paradigm-shifting technology. Various types of 3D and 4D printing are reviewed and their advantages and disadvantages are presented. In addition, current challenges and future potential developments are discussed.
We sincerely hope that this contribution will open up new discussions and motivate novel research into the adaptation of the Industry 4.0 technologies in energy and material supply chains.
Dr. Mahdi Sharifzadeh, On behalf of coauthors
1 Connectivity through Wireless Communications and Sensors
Marzieh Jalal Abadi*and Babak Hossein Khalaj
School of Electrical Engineering, Sharif University of Technology, Tehran, Iran* Corresponding author. School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
1.1 Introduction: Key Technologies Enablers for Industry 4.0 and Supply Chain 4.0
The advent of digitalization in Internet era and mobile technologies yields changes in established business models and the global industry landscape [1]. Currently, manufacturing processes and operation environments are empowered by the Internet of things (IoT), autonomous robotics, and advanced data analytics. Moreover, the implementation of integrated automation and ubiquitous computing systems enables interconnection of human and machines in the context of a cyber-physical system (CPS). These successive technologies significantly improve the performance efficiency and customer satisfaction in the industrial sectors and lead to the fourth industrial revolution, termed as Industry 4.0 [2, 3].
Industry 4.0 is mainly identified by visibility, interconnectivity, autonomous performance, and predictive analysis. It improves agility in supply chain management, service efficiency, and cost reduction. For instance, introduction of Industry 4.0 into manufacturing induces optimized planning and decision-making across end-to-end (E2E) supply chains and delivers customized products to the end-users at competitive cost. In fact, Industry 4.0 brings the concept of Supply Chain 4.0 that changes the way supply chain operations are structured.
In this section, we briefly overview the background of Industry 4.0 followed by its vision on future manufacturing. We then review the key features and enablers of Industry 4.0. Since the focus of this chapter is on connectivity through wireless communication and sensors in the context of Industry 4.0, in the next sections we concentrate only on wireless connectivity in this era.
1.1.1 Background
The main idea of Industry 4.0 was first adopted by Germany in 2011 as a strategic initiative that determined future advanced manufacturing for 2020 [4, 5]. Thereafter, different countries have introduced similar programs for manufacturing research and innovation; for instance, Horizon2020 proposed factories of the future (FoF) in Europe [6, 7], Industrial IoT (IIoT) was introduced in the United States by General Electric (GE) [8], and the industrial value chain initiative (IVI) was founded in Japan [9].
Industry 4.0 is defined as the integration of digitalization, intelligence, and communication technologies to industrial practices [10]. This new industrial paradigm automates work processes, optimizes products, and allows more granular customer services. Subsequently, a high level of productivity and efficiency is available to enterprises and organizations that embrace Industry 4.0 [11]. It employs innovations and disruptive developments such as IoT, CPS, big data, Fog computing, and virtual reality (VR) to accomplish this transformation. The main idea of Industry 4.0 has drawn on earlier perspectives and concepts and expanded over the years, although its landscape has been significantly altered lately [12].
1.1.2 Future Manufacturing Vision
Industry 4.0 has evolved conventional manufacturing systems a leap forward to smart factories. These advanced manufacturing systems, also termed interconnected factories and digital manufacturing, create smart products and processes. The term “smart” in Industry 4.0 framework is defined as an intelligent environment that offers real-time communication and cooperation of various devices to make decisions and act according to the obtained information [13]. In this manufacturing approach, all the components communicate autonomously, work without human intervention, and trigger operations from customers to suppliers [14]. This allows