Digital twins: Transforming the chemical process industry—A review

The Canadian Journal of Chemical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 26, 2025

Abstract Digital twin (DT) technology represents a significant advancement in the digital transformation of chemical process industry (CPI), offering innovative capabilities for real‐time monitoring, predictive maintenance, and optimization. This review investigates current deployment status, frameworks, architectures, applications DTs within CPI, highlighting their transformative potential improving operational efficiency, enhancing safety, promoting sustainability. By examining case studies from leaders analyzing recent advancements, this study elucidates critical roles asset health optimization, environmental performance. The identifies key components DT including data integration, hybrid modelling, analytics, which are essential effective implementation. It further explores challenges such as high computational requirements, integration with legacy systems, cybersecurity risks, lack standardization, impede widespread adoption. Despite these challenges, paper emphasizes opportunities leveraging advanced technologies artificial intelligence, edge computing, 5G connectivity to enhance scalability. In addition, underscores importance addressing global sustainability goals, mainly through ability optimize energy consumption, reduce emissions, facilitate circular economy practices. synthesizing insights academia industry, provides comprehensive understanding DTs' state future strategic directions research development. findings contribute advancing cornerstone achieving excellence, industry.

Язык: Английский

Causal Time-Series Synchronization for Multi-Dimensional Forecasting

Procedia Computer Science, Год журнала: 2025, Номер 253, С. 2655 - 2664

Опубликована: Янв. 1, 2025

Язык: Английский