Digital Twins: Transforming Manufacturing (Specialty Chemical Industry focus) by Joseph E. Johnson

Digital Twins: ?Transforming Manufacturing (Specialty Chemical Industry focus)

By Joseph E. Johnson

Introduction

In an era of rapid technological advancement, ?industries are undergoing a profound transformation. At the forefront of this revolution is a concept that sounds like it belongs in a science fiction novel: digital twins. These virtual doppelgangers of physical assets, operations and processes are reshaping how companies operate, innovate, and compete in an increasingly complex global market (1,2). This article describes what digital twins are, lists some industries using them and why, and has some software references. It also explores their evolution from simple digital representations to sophisticated predictive tools, and examines how they are being leveraged across various industries, with a particular focus on the specialty chemical sector. That being said, digital twins may be applied to almost any industry.

Many industries are using digital technologies to discover remarkable ways of harnessing the power of data and simulation to drive efficiency, sustainability, and innovation (3,4). From optimizing complex processes to predicting equipment failures before they occur, digital twins are proving to be an indispensable tool in the tool-box of forward-thinking chemical companies.

What are Digital Twins? From Basic to Advanced Concepts

At its core, a digital twin is a virtual representation of a physical object, process, or system(5). However, the concept of digital twins has evolved significantly over time, growing in complexity and capability. Here’s the evolution through three levels of sophistication:

1. Basic Level At the most fundamental level, a digital twin is essentially a digital replica of a physical entity. This replica contains basic information about the physical object's characteristics and current state(1). For instance, a simple digital twin of a chemical reactor might include data on its dimensions, materials of construction, and current operating temperature. ?In summary, a real world entity or process uses data to connected to a virtual representation.
2. Intermediate Level As we move to an intermediate level of complexity, digital twins become more dynamic and interactive. At this stage, they incorporate real-time data from sensors and other sources, allowing for continuous monitoring and analysis of the physical counterpart (6). An intermediate digital twin of our chemical reactor would not only know its current state but also track changes over time, potentially alerting operators to deviations from expected performance.
3. Advanced Level The most sophisticated digital twins are powerful predictive tools that leverage artificial intelligence and machine learning . These advanced twins can simulate future scenarios, optimize processes, and even make autonomous decisions. An advanced digital twin of a chemical plant might predict equipment failures, optimize resource allocation across multiple processes, and suggest process improvements to maximize yield and minimize waste.

Digital Twins Across Industries

The versatility of digital twin technology has led to its adoption across a wide range of industries. Here are some key sectors leveraging this technology:

1. Manufacturing: Digital twins are used to optimize production lines, predict maintenance needs, and improve product quality (7). Software: Siemens Tecnomatix, PTC ThingWorx
2. Aerospace: Companies like Boeing use digital twins for aircraft design, testing, and maintenance planning (6). Software: Dassault Systèmes 3DEXPERIENCE, ANSYS Twin Builder
3. Automotive: Automakers employ digital twins for vehicle design, production optimization, and even in-vehicle systems for real-time performance monitoring (7-9). Software: Siemens NX, Autodesk Fusion 360
4. Healthcare: Digital twins of patients are being developed to predict health outcomes and personalize treatments (10). Software: Philips HealthSuite, Siemens Healthineers Syngo.via
5. Urban Planning: Cities are creating digital twins to manage infrastructure, traffic flow, and energy consumption more effectively (11,12). Software: Bentley Systems iTwin, Cityzenith Smart World Pro
6. Energy: Both traditional and renewable energy sectors use digital twins for asset management and production optimization (13-15). Software: GE Digital's Predix Platform, AVEVA's Unified Operations Center

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Digital Twins in Specialty Chemicals: Case Studies

The specialty chemicals industry, with its complex processes and high-value products, is particularly well-suited to benefit from digital twin technology . Here is how some leading companies in this sector are leveraging digital twins and the software they are using (Table 1):

Table 1: Specialty Chemical Company, Digital Twin Use and Results, and Software

These case studies demonstrate the diverse applications of digital twins in the specialty chemicals sector. From optimizing specific processes like glass melting to improving overall plant efficiency, digital twins are proving their value across various aspects of chemical manufacturing.

Evonik's implementation of digital twins, for example, has allowed them to not only optimize their current operations but also to design and construct new plants more efficiently (16). This forward-thinking approach demonstrates how digital twins can impact the entire lifecycle of chemical manufacturing facilities .

AGC Inc.'s use of digital twins in their glass melting processes showcases the technology's potential for significant energy savings (17). By improving fuel gas efficiency by 6%, AGC has not only reduced costs but also decreased their environmental impact.

BASF's application of digital twins for predictive maintenance illustrates how this technology can prevent costly downtime and extend the life of critical equipment (18). By anticipating maintenance needs, BASF can schedule repairs during planned downtime, maximizing plant availability.

AkzoNobel's innovative use of digital twins in marine coatings demonstrates the technology's versatility (19). By predicting hull conditions and optimizing coating processes, AkzoNobel is helping their customers reduce maintenance costs and improve fuel efficiency in the shipping industry.

Dow Chemical's broad implementation of digital twins across their production processes has led to significant improvements in operational efficiency (20). This holistic approach shows how digital twins can be integrated into a company's overall digital transformation strategy.

Conclusion

The adoption of digital twin technology in the specialty chemicals industry represents a significant leap forward in manufacturing efficiency, product quality, and innovation. From the case studies presented, companies that embrace this technology are reaping substantial benefits, from cost savings and improved productivity to enhanced sustainability and product development.

The evolution of digital twins from simple digital representations to sophisticated AI-driven predictive tools mirrors the broader digital transformation occurring across the chemical industry. ?As these technologies continue to advance, we can expect to see even more innovative applications and transformative impacts.

The future of chemical manufacturing undoubtedly contains digital technologies, and digital twins are at the heart of this revolution. ?Companies that invest in this technology now are positioning themselves at the forefront of the industry, ready to meet the challenges and opportunities of tomorrow's market.

For the future, it's clear that digital twins will play an increasingly crucial role in driving innovation, sustainability, and competitiveness in many industries (21-24). ?The question for companies is no longer whether to adopt digital twin technology, but where to apply and how quickly can they integrate ?it into their operations to stay ahead of their competitors in an increasingly digital world.

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References

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2.?????? Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9), 3563-3576.

3.?????? Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022. Rasheed, A., San, O., & Kvamsdal, T. (2020).

4.?????? Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980-22012. Uhlemann, T. H. J., Lehmann, C., & Steinhilper, R. (2017).

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6.?????? The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA (p. 1818). S?derberg, R., W?rmefjord, K., Carlson, J. S., & Lindkvist, L. (2017).

7.?????? Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Annals, 66(1), 137-140. Bruynseels, K., Santoni de Sio, F., & van den Hoven, J. (2018).

8.?????? Digital Twins in Automotive: An In-Depth Overview. Appinventiv. (2023). https://appinventiv.com/blog/digital-twin-in-automotive/

9.?????? EDI Weekly. (2022). Digital Twins in Automotive Manufacturing: Dynamic Virtual Models. EDI Weekly. (2022). ?https://www.ediweekly.com/digital-twins-in-automotive-manufacturing-dynamic-virtual-models/

10.?? Digital twins in health care: ethical implications of an emerging engineering paradigm. Frontiers in genetics, 9, 31. Mohammadi, N., & Taylor, J. E. (2017).

11. Smart city digital twins. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-5). IEEE. Zhuang, C., Liu, J., & Xiong, H. (2018).

12.?? Yates-Roberts, E. (2020). Bentley Systems and Microsoft to digitise urban planning. Technology Record.

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16.?? Digital twin—the simulation aspect. In Mechatronic futures (pp. 59-74). Springer, Cham. Evonik Industries AG. (2021). Annual Report 2020. Retrieved from https://corporate.evonik.com/en/investor-relations/reports-and-presentations

17.?? AGC Inc. (2020). Integrated Report 2020. Retrieved from https://www.agc.com/en/ir/library/annual/

18.?? BASF SE. (2021). BASF Report 2020. Retrieved from https://report.basf.com/2020/en/

19.?? AkzoNobel N.V. (2021). Annual Report 2020. Retrieved from https://report.akzonobel.com/2020/ar/

20.?? The Dow Chemical Company. (2021). 2020 Environmental, Social and Governance Report. Retrieved from https://corporate.dow.com/en-us/esg/report.html Grieves, M., & Vickers, J. (2017).

21.?? Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems (pp. 85-113). Springer, Cham. Stark, R., Kind, S., & Neumeyer, S. (2017).

22.?? Innovations in digital modelling for next generation manufacturing system design. CIRP Annals, 66(1), 169-172. Qi, Q., & Tao, F. (2018).

23.?? Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585-3593. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018).

24.?? Digital Twin in manufacturing: A categorical literature review and classification. IFAC-Papers On Line, 51(11), 1016-1022. Tao, F., Zhang, M., & Nee, A. Y. C. (2019). Digital twin driven smart manufacturing. Academic Press.

25.?? What is a Digital Twin?; Insights, Inside DBM Vircon, Technology, Thought Leadership, August 25, 2021; https://www.dbmvircon.com/what-is-a-digital-twin/

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