Matthias Gaderer

The currently still high fossil energy demand is forcing the glass industry to search for innovative approaches for the reduction in CO2 emissions and the integration of renewable energy sources. In this paper, a novel power-to-methane concept is presented and discussed for this purpose. A special focus is on methods for the required CO2 capture from typical flue gases in the glass industry, which have hardly been explored to date. To close this research gap, process simulation models are developed to investigate post-combustion CO2 capture by absorption processes, followed by a techno-economic evaluation. Due to reduced flue gas volume, the designed CO2 capture plant is found to be much smaller (40 m3 absorber column volume) than absorption-based CO2 separation processes for power plants (12,560 m3 absorber column volume). As there are many options for waste heat utilization in the glass industry, the waste heat required for CO2 desorption can be generated in a particularly efficient and cost-effective way. The resulting CO2 separation costs range between 41 and 42 EUR/t CO2, depending on waste heat utilization for desorption. These costs are below the values of 50–65 EUR/t CO2 for comparable industrial applications. Despite these promising economic results, there are still some technical restrictions in terms of solvent degradation due to the high oxygen content in flue gas compositions. The results of this study point towards parametric studies for approaching these issues, such as the use of secondary and tertiary amines as solvents, or the optimization of operating conditions such as stripper pressure for further cost reductions potential.

In a biogas plant, the acetic acid concentration is a major component of the substrate as it determines the pH value, and this pH value correlates with the volume of biogas produced. Since it requires specialized laboratory equipment, the concentration of acetic acid in a biogas substrate cannot be measured on-line. The project aims to use NIR sensors and machine learning algorithms to estimate the acetic acid concentration in a biogas substrate based on the measured intensities of the substrate. As a result of this project, it was possible to determine whether the acetic acid concentration in a biogas substrate is higher or lower than 2 g/l using machine learning models.