Modelling the fate of organic carbon and micropollutants in Biological Active Granular Activated Carbon Filters

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The presence of Organic Micropollutants (OMPs) in receiving waterbodies is of great concern, due to their potential threat to the environment and human health. Wastewater Treatment Plants (WWTP) effluents are known to be one major source of OMPs; as a response, legal frameworks are currently under discussion and various technologies for the reduction of OMPs are investigated. Granular Activated Carbon (GAC) filters have established themselves as a suitable technology for OMP removal from WWTP effluents. Alongside adsorptive removal GAC-filters have the ability to also biologically remove organic matter and OMPs. The phenomena governing adsorptive and biological reduction of OMPs as well as the synergies between these two mechanisms are of great importance. The investigation of these processes is, however, highly complex. On the one hand, WWPT effluents are multi-component mixtures and difficult to characterize; on the other hand, the various interactions that take place between the GAC, the biofilm, the OMPs and the organic matter, is rather complicated to measure or track experimentally. Mathematical models are powerful tools to overcome such experimental barriers, to analyze various scenarios and eventually to support the design of further experiments. Using lab and pilot-scale data, a first mathematical model capable of satisfactorily describing the dissolved organic carbon removal in a biological active GAC-filter has been developed. This project aims to improve and extend this model with new key features that are required for further application of the model. In particular, three hypotheses will be tested: (i) Is it possible to introduce the pore size distribution to the model? Pore size distribution is key parameter for the characterization of different GAC-types, thus its implementation into the model is vital. Nevertheless the conventional approaches require parameters that are difficult to determine. (ii) Could a microbial community including the N-cycle improve the explanatory power of the model? With experimental evidence that relates biodegradation of OMPs to the activity of nitrifiers, the project aims to implement co-metabolic processes and to assess their effect on the global modelling results. (iii) How can individual OMPs be included into the model and their behavior be satisfactory reproduced? The prediction of the removal of individual OMPs is of high relevance and therefore, four OMPs will exemplarily be implemented into the model and used as proxy for the removal of other OMPs of interest. Since the mechanistic description of the OMPs may easily get complicated, the modeling approach will combine mechanistical modeling with machine learning methods as support.