Development of AI-Enabled Cyber-Physical Digital Twin Architecture for Sustainable Glass Manufacturing in Industry 5.0

One of the high-temperature industries that have an enormous contribution to carbon emissions in the world is the glass manufacturing industry, which operates high-energy furnaces and produces continuously. Sustainable manufacturing solutions are needed desperately with an emitted CO₂ per ton of glass of almost one ton. The paper provides a review and conceptual framework of an AI-based Cyber-Physical Digital Twin architecture of sustainable glass production within the Industry5.0 framework. The research combines the breakthroughs in AI, cyber-physical systems (CPS), digital twins, and automation to solve the problem of high fuel consumption, inefficient combustion, and heat losses. It focuses on predictive AI models (ANN and hybrid SVR Firefly optimization) applied along with real-time CPS monitoring and digital twin simulations to optimize the process.

The proposed framework allows the smart control of important parameters including air-fuel ratio, cullet, and thermal efficiency, as well as robotic inspection and human-oriented work. It also identifies research gaps in the application of AI, CPS, and sustainability-based digital twins in the glass industry. The 10-15% fuel savings, 15-20% CO₂ reduction, better product quality, and more predictive maintenance are expected. The work offers a guide to building sustainable and intelligent glass manufacturing systems that are harmonized with Industry 5.0.