Improvement of Energy Performance of Glass Furnaces Using Modelling and Optimization Techniques

Abstract

Glass furnaces are a key component of the energy-intensive glass industry. Therefore, optimization of their energy performance is crucial for both economic and environmental sustainability. This study focused on optimizing the performance of an electric-boosted natural gas glass furnace. For this purpose, firstly, raw operational data were collected from a glass furnace. Next, reconciled data were obtained via a modelling process, data reconciliation, and gross error detection to establish a reliable dataset. Two linear regression models were developed and tested using both raw and reconciled data and compared with each other. The constrained optimization problem was constructed using a linear regression model and other process constraints and solved via the interior-point method to minimize specific energy consumption. The findings indicate that the reconciled data-based linear regression model yielded more reliable results. The specific energy consumption can be reduced to a minimum of 3660.088 kJ/kg-glass under an optimal setpoint for raw material, cullet, water, raw material temperature, electric boosting, and fuel. Furthermore, the analysis reveals that energy performance is enhanced with increased glass production and greater utilization of electric boosting. These results emphasize that the integrated statistical modelling approach provides valuable and actionable insights for energy performance improvements in the glass industry.