The Use of Uncertainty Quantification and Numerical Optimization to Support the Design and Operation Management of Air-Staging Gas Recirculation Strategies in Glass Furnaces

The reduction in energy consumption and the increasingly demanding emissions regulations have become strategic challenges for every industrial sector. In this context, the glass industry would be one of the most affected sectors due to its high energy demand and emissions productions, especially in terms of NOx. For this reason, various emission abatement systems have been developed in this field and one of the most used is the air staging system. It consists in injecting air into the upper part of the regenerative chamber on the exhaust gases side in order to create the conditions for combustion that reduces NOx emissions. In this work, the combined use of CFD with data analysis techniques offers a tool for the design and management of a hybrid air staging system. Surrogate models of the bypass mass flow rate and uniformity index in the regenerative chamber have been obtained starting from DoE based on different simulations by varying the air mass flow rate of the two injectors located in a bypass duct that connects the two regenerative chambers. This model allows a UQ analysis to verify how the uncertainty of the air injectors can affect the bypass mass flow rate. Finally, an optimization procedure has identified the optimal condition for the best bypass mass flow rates and uniformity of the oxygen concentration in the chamber. High values of the mass flow rate of the pros injector and medium-low values for the cons injectors are identified as operating parameters for best conditions.