Towards Autonomous Commissioning: Integrating Digital Twins, Artificial Intelligence and Smart Sensors for Next-Generation Process Control Systems

Abstract

This article explores the emergent paradigm of autonomous commissioning for process control systems, enabled by the convergence of digital twins, artificial intelligence (AI), and smart sensor networks. As modern industrial systems transition into the era of Industry 4.0 and beyond, the conventional manual commissioning procedures become increasingly inadequate in addressing complexity, variability, and dynamic adaptation. We propose a conceptual framework for integrating virtual commissioning via digital twins, AI-driven self-learning control, and pervasive smart sensing to achieve adaptive, self-configuring process control systems. We review current literature on digital twins (DTs) in manufacturing and process industries, AI-driven decision-making in commissioning, and the role of smart sensors/IIoT in supporting real-time feedback and adaptation. We identify key architectural components, data flows, autonomy levels, and highlight the major technical and organisational challenges including data quality, cybersecurity, sensor fusion, model maintenance, and human–machine interaction. Finally, we provide case‐study exemplars, practical guidelines for engineering deployment, and an agenda for future research. The proposed roadmap offers practitioners and researchers a holistic view of next-generation commissioning where physical systems, virtual counterparts, and intelligent algorithms collaborate to achieve rapid, robust, and recurring commissioning of process-control assets.