Date of Award

Spring 5-1-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Technology Management

Department

College of Technology

First Advisor

William Clyburn

Second Advisor

Alister McLeod

Third Advisor

John Pickard

Abstract

Industrial Control Systems (ICS) are used for process control in almost all industries. An ICS combines Operational Technologies (OT) with Information Technologies (IT) to allow human supervision of a process through surveillance of process variables and manipulation of controlling elements such as valves to maintain stable process conditions. ICSs have been in-service for several decades and may remain operational past their technological service life. Organizational personnel interact with the ICS through visual displays that both indicate the process variables and also the controlling elements. The Human Machine Interface (HMI) allows visibility of the process and the ability to manipulate controlling elements to maintain stable process conditions. The HMI is the bridge between computational resources and human intelligence. Digital twins have been implemented over the last two decades with increasing maturity and capability. Advances in computational power and Artificial Intelligence (AI) have enabled increasingly greater efficiency in industrial process control. Despite the ubiquitous implementations of digital twins, there has been a gap in understanding early-stage digital twin implementation within an industrial process environment. This study focused on the initial implementation of a digital twin within an Oil and Gas (O&G) Subsea Production System (SPS) in order to assess how early stage digital twin implementation could affect the operator response to potential abnormal process conditions and to assess a digital twin’s ability to help the human operator differentiate a true abnormal process condition from a benign control system failure. Using an in-service ICS as a test bed, this study found that early-stage digital twin implementation resulted in increased improvement in the operational response to potential abnormal process conditions for two levels of digital twin maturity. Research methodology controls were implemented to mitigate threats to both internal and external validity. The findings provide practical significance for organizations wanting to implement a digital twin, but may not be able to perform full scale implementations.

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