American International Journal of Research and Innovation

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Digital Twin Technology for Intelligent Industrial Process Optimization

Author(s) Michael Weyrich
Country United States
Abstract The increasing complexity of modern industrial systems has created a demand for intelligent technologies capable of improving operational efficiency, predictive decision-making, and adaptive process management. Digital Twin technology has emerged as a transformative approach that integrates physical assets, computational models, real-time sensor data, artificial intelligence, and advanced analytics to create dynamic virtual representations of industrial processes.
This study examines the role of Digital Twin technology in intelligent industrial process optimization by analysing its architectural components, optimization mechanisms, operational benefits, and implementation challenges. The research develops a conceptual framework explaining how real-time data synchronization, predictive modelling, simulation-based decision support, and intelligent control mechanisms contribute to improved industrial performance.
A simulation-based analytical evaluation is conducted to demonstrate the potential impact of Digital Twin adoption on key industrial performance indicators, including process efficiency, downtime reduction, resource utilization, and operational responsiveness. The findings indicate that Digital Twin-enabled systems provide significant opportunities for enhancing industrial intelligence, reducing uncertainty, and supporting sustainable manufacturing practices. However, challenges related to data interoperability, cybersecurity, model accuracy, and organizational readiness remain important considerations for successful deployment. The study contributes to the emerging body of knowledge by presenting an integrated perspective on Digital Twin applications for next-generation intelligent industrial ecosystems.
Keywords Digital Twin Technology; Industrial Process Optimization; Smart Manufacturing; Artificial Intelligence; Predictive Analytics; Industrial Automation; Intelligent Systems
Field Engineering
Published In Volume 1, Issue 4, July-August 2019
Published On 2019-07-14
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