Digital Twin Integration for Lifecycle Management of Operating Industrial Facilities Under Continuous Production Conditions

Background: Digital twin (DT) technology has emerged as a transformative approach for industrial asset management, yet the majority of existing frameworks target greenfield facilities or assume production can be interrupted during DT deployment. Operating industrial facilities under continuous production conditions-refineries, chemical plants, and power generation stations-face unique challenges including legacy system heterogeneity, zero-downtime requirements, and scalability constraints that remain insufficiently addressed in the literature. Methods: This paper proposes a five-layer DT integration framework specifically designed for brownfield industrial facilities operating under continuous production. The framework incorporates a phased zero-downtime deployment strategy progressing through passive monitoring, shadow mode, advisory mode, and closed-loop operation. The methodology was validated through a simulation environment replicating a mid-scale continuous petrochemical processing facility comprising 150 monitored assets and 500+ sensor points across heterogeneous control systems (simulation-based validation is standard practice for framework-level contributions where operational data from continuous production facilities remains restricted under non-disclosure agreements). Results: Across five independent simulation runs, the framework demonstrated a statistically significant improvement in Overall Equipment Effectiveness (OEE) from 67.9% (SD 1.12) to 85.0% (SD 0.71, p<0.01) across deployment phases, with DT synchronization accuracy reaching 99.6% after calibration. The predictive maintenance module achieved an AUC of 0.933 for equipment failure prediction within a 12-month horizon. Edge computing architecture reduced synchronization latency to a median of 17 ms compared to 185 ms for cloud-only deployment. Conclusions: The proposed framework addresses critical gaps in brownfield DT implementation by providing a structured, standards-aligned approach that maintains continuous production throughout all deployment phases. The results demonstrate that phased DT integration can deliver substantial operational improvements without compromising production continuity.

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Published
2026-02-28
DOI
https://doi.org/10.66308/air.e2026016
Primary Topic
Digital Transformation in Industry
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article

Digital Twin Integration for Lifecycle Management of Operating Industrial Facilities Under Continuous Production Conditions

Ananya Deshpande, Michael Torres, Bobur Karimov
Digital Transformation in Industry
article

Digital Twin Integration for Lifecycle Management of Operating Industrial Facilities Under Continuous Production Conditions

Ananya Deshpande, Michael Torres, Bobur Karimov
article en

Abstract

Background: Digital twin (DT) technology has emerged as a transformative approach for industrial asset management, yet the majority of existing frameworks target greenfield facilities or assume production can be interrupted during DT deployment. Operating industrial facilities under continuous production conditions-refineries, chemical plants, and power generation stations-face unique challenges including legacy system heterogeneity, zero-downtime requirements, and scalability constraints that remain insufficiently addressed in the literature. Methods: This paper proposes a five-layer DT integration framework specifically designed for brownfield industrial facilities operating under continuous production. The framework incorporates a phased zero-downtime deployment strategy progressing through passive monitoring, shadow mode, advisory mode, and closed-loop operation. The methodology was validated through a simulation environment replicating a mid-scale continuous petrochemical processing facility comprising 150 monitored assets and 500+ sensor points across heterogeneous control systems (simulation-based validation is standard practice for framework-level contributions where operational data from continuous production facilities remains restricted under non-disclosure agreements). Results: Across five independent simulation runs, the framework demonstrated a statistically significant improvement in Overall Equipment Effectiveness (OEE) from 67.9% (SD 1.12) to 85.0% (SD 0.71, p<0.01) across deployment phases, with DT synchronization accuracy reaching 99.6% after calibration. The predictive maintenance module achieved an AUC of 0.933 for equipment failure prediction within a 12-month horizon. Edge computing architecture reduced synchronization latency to a median of 17 ms compared to 185 ms for cloud-only deployment. Conclusions: The proposed framework addresses critical gaps in brownfield DT implementation by providing a structured, standards-aligned approach that maintains continuous production throughout all deployment phases. The results demonstrate that phased DT integration can deliver substantial operational improvements without compromising production continuity.

Vol. 1(1)
Baker Hughes (United States) (US), Bukhara State University (UZ), Tata Consultancy Services (India) (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 34%
Digital Transformation in Industry
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