Digital Twins in the Chemical Industry: Enhancing Efficiency and Innovation

Digital twins have emerged as transformative tools in the chemical industry, revolutionizing traditional manufacturing methods and driving innovation. By creating virtual representations of physical assets and processes, digital twins enable real-time monitoring, simulation, and optimization of complex chemical systems. This review synthesizes findings from peer-reviewed literature and industry reports to provide a comprehensive analysis of digital twin applications, integration with advanced technologies like the Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML) and an evaluation of challenges and opportunities in the chemical sector. The study highlights the significant impact of digital twins on process optimization, predictive maintenance, safety management, and product development. However, the adoption of this technology faces challenges related to data quality and integration, cybersecurity concerns, skill gaps, and high initial investments. Despite these limitations, the review provides a roadmap for chemical industries to harness digital twins effectively, emphasizing strategies for overcoming challenges and leveraging opportunities for innovation, efficiency, and sustainability. The integration of digital twins with emerging technologies like blockchain, extended reality (XR), and cross-industry collaboration is expected to further enhance their capabilities and drive the digital transformation of the chemical industry. As the sector embraces the digital future, digital twins are poised to play a crucial role in redefining operational dynamics and ensuring a competitive advantage in an increasingly complex global marketplace.

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Publication Details

Journal
Afinidad
Published
2025-09-19
DOI
https://doi.org/10.55815/432158
Primary Topic
Digital Transformation in Industry
Type
article
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article

Digital Twins in the Chemical Industry: Enhancing Efficiency and Innovation

Adhithya Balasubramanian
Afinidad
Digital Transformation in Industry
article

Digital Twins in the Chemical Industry: Enhancing Efficiency and Innovation

Adhithya Balasubramanian
article en

Abstract

Digital twins have emerged as transformative tools in the chemical industry, revolutionizing traditional manufacturing methods and driving innovation. By creating virtual representations of physical assets and processes, digital twins enable real-time monitoring, simulation, and optimization of complex chemical systems. This review synthesizes findings from peer-reviewed literature and industry reports to provide a comprehensive analysis of digital twin applications, integration with advanced technologies like the Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML) and an evaluation of challenges and opportunities in the chemical sector. The study highlights the significant impact of digital twins on process optimization, predictive maintenance, safety management, and product development. However, the adoption of this technology faces challenges related to data quality and integration, cybersecurity concerns, skill gaps, and high initial investments. Despite these limitations, the review provides a roadmap for chemical industries to harness digital twins effectively, emphasizing strategies for overcoming challenges and leveraging opportunities for innovation, efficiency, and sustainability. The integration of digital twins with emerging technologies like blockchain, extended reality (XR), and cross-industry collaboration is expected to further enhance their capabilities and drive the digital transformation of the chemical industry. As the sector embraces the digital future, digital twins are poised to play a crucial role in redefining operational dynamics and ensuring a competitive advantage in an increasingly complex global marketplace.

AfinidadVol. 82(606)
Openalex Percentile: Top 64%
Digital Transformation in Industry
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Digital Twins in the Chemical Industry: Enhancing Efficiency and Innovation — Adhithya Balasubramanian · Afinidad (2025) | TGRS Research Map | TGRS