Frauke Groß

Modified ADM1 for high-rate anaerobic co-digestion of thermally pre-treated brewery surplus yeast wastewater

Co-digestion of surplus yeast with brewery wastewater is a potentially economical method for recovering energy, in the form of biogas, from this difficult to dispose of by-product. In this work a modified version of the ADM1 (Anaerobic Digestion Model No. 1) was calibrated for an anaerobic digester fed with thermally pre-treated brewery yeast surplus wastewater. The model could predict changes to reactor methane production and reduction of biodegradable matter when fed with both pre-treated and untreated yeast surplus wastewater at varying loading rates. Model calibration focused on low temperature thermal pre-treatment as experiments into a combined thermal-alkaline pre-treatment did not show any significant improvements. A low temperature pre-treatment of 60 °C for 30 minutes was sufficient for yeast inactivation and allowed for stable and more efficient operation of the high-rate anaerobic digester over a period of 232 days. The low temperature and time for pre-treatment also reduced the evaporation of easily biodegradable residual ethanol present in the influent, while still maintaining a low level of suspended matter. Inline measurements of gas composition, production and effluent chemical oxygen demand were sufficient for reliable model calibration of these same outputs. More detailed characterization of influent and effluent is required if organic acid concentrations for pH control are needed.

Artificial Neural Networks: Applications in Food Processing

Artificial neural networks (ANNs) represent a mathematical approach that permits to mimic the learning process occurring in the brain of mammalians. The present article presents a short overview on ANNs. The basic need of the employment of ANNs in up-to-date food management systems, the theoretical background, and design of ANNs are described shortly. Examples of ANNs in food technology applications are also presented. The last section discusses current and future ANN methods in food technology.