Nutrient recovery from anaerobic sludge by membrane filtration: pilot tests at a 2.5 MWe biogas plant

Abstract Purpose Membrane filtration is recently applied to recover nutrients and dischargeable water from anaerobic sludge. The purpose of this study is to quantify nutrient separation, membrane performance, and process stability and to increase the economical applicability of the process by energetic optimisation. Methods At the site of a 2.5 MWe agricultural biogas plant, a membrane pilot plant was operated over a period of 7 months. It consisted of a screw press separator, a decanter centrifuge, an ultrafiltration unit, and a three-stage reverse osmosis unit. Mass and nutrient balances were generated by sampling and analysing every process stream. Process performance was analysed by monitoring separation efficiencies, membrane flux, cleaning intervals, and energy demand. Results Solid/liquid separation resulted in separation efficiencies of 70% for total solids and 80% for phosphorus. The solid fraction contained high concentrations of organics and particle-ligated nutrients (20% TS, 8 kg t−1 Ntotal, 5.5 kg t−1 P2O5). The retentate of the reverse osmosis had high concentrations of dissolved ammonia and potassium (4 kg t−1 NH4–N and 10 kg t−1 K2O). 38% of the sludge volume was recovered as clean water. Conclusion The membrane pilot plant successfully produced a solid N/P-fertiliser, a liquid N/K-fertiliser and clean water. The results contribute to a sound understanding and growing database for future adaption of the process chain. Hydrodynamic optimisation within the pilot plant reduced the energy demand of the ultrafiltration step by 50%, which considerably contributes to the economy of the process

Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS

Villmann T, Strickert M, Brüß C, Schleif F-M, Seiffert U. Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS. In: Verleysen M, ed. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007. Evere, Belgium: d-side publications; 2007: 103-108

Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas

Brüß C, Bollenbeck F, Schleif F-M, Weschke W, Villmann T, Seiffert U. Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas. In: Proc. of ESANN 2006. 2006: 563-569

Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes

Villmann T, Seiffert U, Schleif F-M, Brüß C, Geweniger T, Hammer B. Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In: Schwenker F, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Berlin: Springer; 2006: 46-56