Manager of Innovative Technology Laboratory
AGC Inc., JP
Taiga Seki received both BS and MS degrees in Mechanical Engineering, from Keio University. His expertise is IN research and development on glass melting process with a focus on numerical simulation techniques.
Dr. Seki is working as the Manager of Innovative Technology Laboratory at AGC Inc. He has a decade of experience in CFD modelling and the development of numerical simulation techniques for glass furnaces and is qualified as a Grade 1 JSME-Certified Computational Mechanics Engineer in the field of Thermal Fluid Mechanics. In recent years his work has focused on the development of digital twin technology for glass furnaces.
Challenges toward Furnace Digital Twin
Controlling flow of liquidus glass melt inside the furnace is one of the key factors to achieve mass production of high quality glass products. To optimize the design and operations of glass manufacturing process, CFD has been widely used.
Recent concept of Digital Twin is a promising technology also for glass industry. Further optimization of the process operation and predictive maintenance can be expected using detailed real-time insights obtained by DT.
AGC has developed digital twin technology for the glass melting process that integrates an online simulator with a digital prototyping tool. Full-scale operational verification at actual float furnaces begun in 2023, confirming that digital twin enables rapid and detailed understanding of the glass melting process and preliminary studies of furnace operations.
As the next step, AGC is working on improvement of accuarcy and reliability of the digital twin using a technique called “Data Assimilation. Data assimilation is known as a sequential method to estimate both state of the system and the model parameters simultaneously, which has been widely used in weather forecasting. In the present study, we applied data assimilation method to the CFD model for continuous glass melting process to implement DT. From preliminary observing system simulation experiment (OSSE) studies, several model parameters, temperature distribution and flow pattern of glass melt were successfully estimated by ensemble kalman filter (EnKF) using temperature data which are measurable in reality.