Modeling studies of biological gas desulfurization under haloalkaline conditions

<strong>Abstract</strong> Biogas, synthesis and natural gas streams often require treatment because of the presence of gaseous hydrogen sulphide (H2S). About 25 years ago, a biotechnological gas treatment process was developed as an alternative to the conventionally applied technologies. This process is known as the Thiopaq process and offers a number of advantages compared to the existing physical-chemical processes. Depending on the process conditions, H2S is oxidized to elemental bio-sulfur (90-94 mol%) and sulphate (6-10 mol%). In order to enable cost effective large scale applications, the selectivity for sulfur production should be increased to more than 97 mol%. Hence, a better understanding of the combined effect of abiotic and biological reaction kinetics and the relation to hydrodynamic characteristics is required. The first part of this PhD study focuses on biological reaction kinetics and biological pathways for sulphide oxidation that occur in the process at haloalkaline conditions. It was found that two different sulfide oxidizing enzyme systems are present in haloalkaline sulfide oxidizing bacteria. It has been hypothesized that the different enzymatic routes are determined by the process conditions. Both enzyme systems were taken into account to propose and validate a new physiological mathematical model that can handle multi-substrates and multi-products. In the second part of the thesis, this model was evaluated via a normalized sensitivity method and it was demonstrated that certain key parameters affect the activity of the biomass at different substrate levels. Furthermore, from CSTR simulations it has been demonstrated that non-linear effects are of importance when scaling up from lab-scale to full-scale industrial units. Finally, the developed kinetic models have been incorporated in a full-scale biodesulfurization model that includes the effects of turbulent flow regimes and mass transfer of oxygen. This enables us to better understand the overall process. Moreover, the model can also be used as a tool to design model-based control strategies which will lead to better overall process performance, i.e. maximize sulfur production and minimize chemical consumption rates.