Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review
Abstract
:1. Introduction
2. Description of Target Literature
- (1)
- For the papers published within recent three years, only the literature from top journals or conferences in the corresponding field are chosen.
- (2)
- For the papers that have been published more than three years ago, the literature with high citations are considered.
- (3)
- For the papers have been published more than seven years ago, they are only used as an introduction to the key concept or descriptions of conventional technologies.
3. Green and Sustainable Manufacturing
3.1. The Definition and Characterization
- no or little harm to the environment and society
- no reduction of natural resource
- capability of satisfying nowadays and future energy needs
- high efficiency
- no toxic emission to air, land or water
- no or little greenhouse gases emission
- no pressure on future populations
3.2. Current Development and Policy
4. Smart Manufacturing
4.1. The Definition
4.2. The Role of Sustainability in Smart Manufacturing
4.3. The Hierarchy of Smart Manufacturing
4.3.1. Process Level
4.3.2. Machine/Station Level
4.3.3. Factory Level
4.4. Smart Manufacturing in the Energy Industry
5. Applications of Smart Techniques in Sustainable Manufacturing
5.1. Deep Learning
- Convolutional neural networks (CNN)
- Long short-term memory networks (LSTM)
- Deep belief networks (DBN)
- Deep stacking networks (DSN)
- Fault diagnostic (detection, identification, estimation of magnitudes)
5.2. Smart Grid and Smart Metering
- Smart meters
- State estimation
- Distributed generations
- Renewable energy integrations (REI)
- Bidirectional communication system (BCS)
- Automatic healing capability (AHC)
- Data security/cyber security
- Carbon emission reduction
- Meter data management (MDM)
- Field area networks (FAN)
- IT and back office computing
- Demand response
- Electricity storage devices
- Distribution automation
5.3. Radio-Frequency Identification (RFID)
- Ease of identification. Because the tag attached to the item is assigned unique information, such as manufacturing conditions and product type, the part is easy to identify and track during the production. This feature is very important in mass production.
- Simultaneous communication. The tag not only specifies what task to be done on the part, but also keep updating during the production, recording the complete task as well as quality diagnostics, which enables real-time inspection and monitoring.
- Automation. By attaching the tags to the item, the production process, including assembling, packaging, and delivery is finished automatically. In addition, the performance history is recorded, so that the manufactures can use such information as feedback to modify the production process and improve the quality of products.
- Improving efficiency at the enterprise level. RFID technology integrates designing, and customer needs as well as manufacturing. Because the information on the tag is constantly updated, the designers can change the production process easily to meet some special customer needs. By shortening the response time, manufacturing efficiency is greatly improved, and the waste (energy consumption and production cost) due to rescheduling induced by different customer needs in mass customization production is minimized.
5.4. Big Data Analytics and Data Mining
- Petrol waste analytics. By analyzing the data collected from vehicles, the trend of fuel consumption is predicted to improve combustion efficiency, which saves energy and reduces emissions.
- Emission control. Transportation emissions are the important source of greenhouse and toxic gas in the air. Data mining provides an effective method to develop a decision-making system or set a reference for policy makers.
5.5. Cloud Computing and High-Performance Computing
- Oil and gas industry modeling
- Electronic design automation
- Climate modeling
- Media and entertainment
- Biosciences
5.6. Additive Manufacturing
6. Applications in Energy Industry
6.1. Sustainable Energy
6.1.1. Solar Energy
6.1.2. Wind Energy
6.1.3. Hydropower
6.2. Energy Devices Applications
6.2.1. Energy Production Devices
6.2.2. Energy Storage Devices
6.3. Smart Energy Systems
6.3.1. Modeling
6.3.2. Monitoring
6.3.3. Decision-Making
7. Prospective and Conclusion
- Deep learning and data mining are core techniques that drive the advancement of smart manufacturing and transform traditional manufacturing styles to modern paradigms. Nevertheless, sustainability and energy efficiency have not been fully considered. First, applications on sustainability and energy efficiency problems are less studied. This is partly because that those problems are essentially different with problems popularly studied in the computer science community regarding data types, data volume, and objectives. Furthermore, when developing smart manufacturing techniques, the consideration of energy efficiency and sustainability will yield multi-objective, multi-constraint problems, which can be so complicated that conventional methods are incapable.
- Cloud computing and HPC are key technologies of smart manufacturing. Nevertheless, the deployment of these supercomputing techniques in manufacturing is still at its nascent stage and requires substantial efforts. Particularly, the choice between cloud computing and HPC, how to effectively incorporate supercomputing powers into daily manufacturing practices, and cybersecurity issues need more investigation. Moreover, due to costly computation and maintenance, supercomputing facilities are a major energy consumption source themselves. Thus, research on reducing energy use, reducing maintenance cost, and achieving a tradeoff between performance and energy efficiency remains to be done.
- Additive manufacturing, as an innovative technique, has attracted a lot of interests from both industries and academia. It increases the customization of products and enables producing products with a complicated geometric shape. Although additive manufacturing is compatible with sustainable manufacturing, there are still many things researchers can do to improve sustainability, such as recycling materials. Additionally, the enhancement of product quality using inline sensing and monitoring, real-time control, and sampling inspection methodology also shows great potential in increasing the sustainability of additive manufacturing.
- Smart manufacturing technologies have improved the production efficiency and sustainability of some renewable energies with large shares. However, the applications for bioenergy and the energies with small shares are limited. The existing smart manufacturing approaches for large shares cannot be directly applied to small share applications as a result of different time scales, varied production rates, and different process dynamics. As such, fundamental research on extending existing methods and developing new methods is critically needed.
- The electricity storage devices have drawn many scholars’ attention, and the ongoing research has made some major progress in improving the quality and efficiency of battery manufacturing. To further enhance the sustainability in energy storage systems, research on industrial heat and cooling systems are highly desired. In addition, the deployment of big data based decision-making, such as online process monitoring, real-time control, and battery performance monitoring, is able to greatly promote quality and bring energy saving.
Author Contributions
Funding
Conflicts of Interest
References
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Smart Manufacturing System | Traditional Manufacturing System |
---|---|
Multiple Resources | Restricted and Prearranged Resources |
Dynamic Routing | Static Routing |
Instant Interconnection | No Interconnection |
Self-organization | Independent Control |
Big Data | Isolated Information |
Application Fields | Improvements Brought by Additive Manufacturing |
---|---|
Medical instruments | Permitting to scan and build a physical model of defective tissue from patients and better treatment plan for doctors |
Architectural design and modeling | Providing powerful technique support for architects to make creating physical models much easier |
Fuel cells manufacturing | Precisely depositing a very thin layer of platinum, needed for the oxidation and reduction reaction, with high utilization efficiency of the platinum |
Lightweight machines | Enabling the manufacture of complex cross-sectional areas like the honeycomb cell or every other material part that contains cavities and cut-outs which reduce the weight-strength relation |
Art creation | Providing the possibility of virtually manufacturing the most complex form imaginable |
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Meng, Y.; Yang, Y.; Chung, H.; Lee, P.-H.; Shao, C. Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability 2018, 10, 4779. https://doi.org/10.3390/su10124779
Meng Y, Yang Y, Chung H, Lee P-H, Shao C. Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability. 2018; 10(12):4779. https://doi.org/10.3390/su10124779
Chicago/Turabian StyleMeng, Yuquan, Yuhang Yang, Haseung Chung, Pil-Ho Lee, and Chenhui Shao. 2018. "Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review" Sustainability 10, no. 12: 4779. https://doi.org/10.3390/su10124779
APA StyleMeng, Y., Yang, Y., Chung, H., Lee, P.-H., & Shao, C. (2018). Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability, 10(12), 4779. https://doi.org/10.3390/su10124779