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Digital Twin Applications for Predictive Maintenance, Operational Safety, and Gas-Plant Reliability

Eromosele Asibor,, Henry Chukwuemeka Olisakwe,

Abstract

This paper develops a maturity-based digital-twin framework that ties model fidelity and data quality to specific maintenance, safety, and reliability decisions. The study responds to the problem that industrial data often remain fragmented across control, historian, maintenance, inspection, engineering, and business systems, limiting trustworthy prediction and coordinated action. It uses an integrative conceptual review of scholarly and professional literature published to date. The framework organizes eight decision functions: use-case definition, asset and data modelling, sensor-quality assessment, model calibration, state estimation, prediction and diagnosis, decision integration, lifecycle governance. It applies those functions across rotating equipment, heat- transfer equipment, separation vessels, piping and valves, safety instrumented functions, plant utilities. The analysis treats uncertainty, process safety, reliability, environmental performance, resource efficiency, economics, and organizational control as connected requirements. The proposed architecture links a system definition, evidence register, model assumptions, intervention portfolio, verification plan, and learning cycle. Its central proposition is that create governed virtual representations that combine physical models, operational data, asset condition, and decision workflows when decisions preserve traceability from measured conditions to selected actions and verified outcomes. The paper offers testable propositions, an implementation roadmap, asset-specific application protocols, and a research agenda. The framework supports case-study testing and quantitative extension without claiming universal performance before field validation.

Keywords

digital-twin deployment; process engineering; reliability; process safety; risk management; asset integrity; environmental performance; decision framework

References

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