Digital transformation of conventional shell-tube heat exchanger using digital twinning and machine learning

Abstract

Numerical simulation-based digital twins are emerging as a transformative technology capable of significantly enhancing operational efficiency and minimising costly maintenance and human intervention for smart products and services. However, the inherent limitations of physical monitoring and the uncertainties associated with product or service performance can be effectively addressed through the strategic application of parameterised numerical models combined with advanced machine learning (ML) algorithms. To address the research gap, this research investigates how a novel and systematic digital-twin-based design and analysis approach can facilitate the transformation of a conventional Shell-and-Tube Heat Exchanger (STHE) into a smart machine within the evolving framework of Industry 4.0. The methodology involves devising a data-driven digital twin (DT) for the STHE, utilising coupled Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) simulations to numerically investigate key performance parameters. This process enables the integration of virtual sensors, the fusion of physical measurements, and the deployment of advanced machine learning algorithms to precisely identify critical points for sensor and actuator placement in an Internet of Things (IoT)-based machine condition monitoring (MCM) system. The developed STHE digital twin successfully demonstrates its capability to extract data of key performance parameters, which enables seamless integration with sensors and actuators. This digital twinning further empowers a digital design and prototyping process for visualiszed, real-time IoT based machine condition monitoring. Advanced ML algorithms are employed to identify the locations of the critical points in the STHE, where sensors are installed for IoT-based MCM. The developed STHE DT demonstrates its capability of extracting crucial data based on fundamental principles of mass, energy, and momentum, facilitating seamless integration with sensors. The underpinning concept, comprehensive methodological framework, and practical implementation process of the STHE digital twin presented herein provide a robust foundation. This work represents a significant scientific contribution towards enabling the transformation of conventional mechanical systems into intelligent, data-driven smart products, aligning with the objectives of Industry 4.0.