Digital twins: Transforming the chemical process industry—A review
Bibliographic record
Abstract
Abstract Digital twin (DT) technology represents a significant advancement in the digital transformation of the chemical process industry (CPI), offering innovative capabilities for real‐time monitoring, predictive maintenance, and process optimization. This review investigates the current deployment status, frameworks, architectures, and applications of DTs within CPI, highlighting their transformative potential in improving operational efficiency, enhancing safety, and promoting sustainability. By examining case studies from industry leaders and analyzing recent advancements, this study elucidates the critical roles of DTs in asset health monitoring, process optimization, and environmental performance. The review identifies key components of DT frameworks, including data integration, hybrid modelling, and real‐time analytics, which are essential for effective implementation. It further explores challenges such as high computational requirements, integration with legacy systems, cybersecurity risks, and the lack of standardization, which impede widespread adoption. Despite these challenges, the paper emphasizes opportunities for leveraging advanced technologies such as artificial intelligence, edge computing, and 5G connectivity to enhance DT capabilities and scalability. In addition, this review underscores the importance of DTs in addressing global sustainability goals, mainly through their ability to optimize energy consumption, reduce emissions, and facilitate circular economy practices. By synthesizing insights from academia and industry, this study provides a comprehensive understanding of DTs' current state and future potential in CPI, offering strategic directions for research and development. The findings contribute to advancing the deployment of DTs as a cornerstone technology in achieving operational excellence, safety, and sustainability in the chemical process industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".