Ammonium estimation in an ANAMMOX SBR treating anaerobically digested domestic wastewater - Vega De Lille, Marisela Ix Chel; Berkhout, Vera; Fröba, Laura; Groß, Frauke; Delgado, Antonio (Chemical Engineering Science, 2015)
Artificial neural networks (ANNs) were used to estimate from online pH-measurements the ammonium concentration in an anaerobic ammonium oxidation (ANAMMOX) sequencing batch reactor (SBR) treating reject water (RW) from the anaerobic treatment of domestic wastewater. The SBR was initially fed with a synthetic autotrophic medium (SM) to assure a stable and ANAMMOX dominating process. After the SBR had been operating stable for 1 month, the removal efficiencies of ammonium and nitrite were equal to 91.22±3.92% and 94.16±8.76%, respectively. The experimental data obtained in this period was taken as basis but not used directly for the training of the ANNs. Instead, the data was used for the calibration of an ordinary differential equations (ODE) model implemented to simulate the nitrogen removal processes that took place in the SBR. This action helped to increase the amount of available data, thereby improving the learning capacity of the networks and reducing the need of extensive experimental analysis. After parameter calibration, the experimental data agreed well with the simulation results in the case of ammonium and nitrite. The simulated ammonium concentration (broadened data set) was then used as target data for the training of different structures of two types of ANNs: multilayer feedforward neural network (MLFNN) and adaptive-network-based fuzzy inference system (ANFIS). The ANNs structures with the best performance after training yielded correlation coefficients (R) of RMLFNN=0.9924 and RANFIS=0.9922. Afterwards, the selected ANNs were validated by comparing the predicted ammonium concentration with the experimental values obtained during the adaptation from SM to the targeted RW. Both types of ANNs were able to predict with good accuracy the ammonium removal inside the SBR even while dealing with the largely fluctuating influent conditions without the need of further training. The results obtained after validation were RMFLNN=0.8440 and RANFIS=0.8454. This shows the potential that ANNs have to model the ANAMMOX process if enough and representative data is available for trainin
UnivIS-Import:2015-07-08:Pub.2015.tech.ITC.stmmec.ammoni
Keywords: Anaerobic ammonium oxidation (ANAMMOX); Artificial neural network (ANN); Mathematical modeling; Ammonium estimation

Messtechnische Datenerfassung und -analyse zur Prozessregelung einer dezentralen Abwasseraufbereitungsanlage - Fröba, Laura; Pettigrew, Liam; Vega De Lille, Marisela Ix Chel; Groß, Frauke; Delgado, Antonio (Proceedings der 22. GALA-Fachtagung "Lasermethoden in der Strömungsmesstechnik", 2014)
UnivIS-Import:2015-04-17:Pub.2014.tech.ITC.stmmec.messte

Start-up of a decentralized pilot plant for the anaerobic treatment of domestic wastewater - Fröba, Laura; Vega De Lille, Marisela Ix Chel; Groß, Frauke; Delgado, Antonio (Water Practice and Technology, 2017)
This paper is about the set-up and start-up of a decentralized anaerobic pilot plant for producing domestic service water from domestic wastewater. The plant consists of a two-stage anaerobic digestion process for degrading organic matter and a third-stage for ammonium removal using the Anammox process. Each reactor was started independently with synthetic wastewater of stage-specific composition. They were then fed incrementally with municipal wastewater (MWW). The average removal efficiency of the two-stage digestion process operated with 100% MWW was 62% with 24-hour retention time. The Anammox stage achieved a maximum ammonium removal efficiency of 95% with 100% MWW, if the NO2 -N to NH4-N ratio was set at 1.14. The plant was operated for 200 days. The average removal efficiencies were 81% for COD and 96% for NH4 -N, with average treated effluent concentrations of 39 mg-COD/l and 1 mg-NH4-N/l. Thus the self-defined service water limits of 75 mg-COD/l and 10 mg-NH4 -N/l were achieved easily.
UnivIS-Import:2018-02-22:Pub.2017.tech.ITC.stmmec.startu
Keywords: Anaerobic digestion; Anammox; Decentralized; Service water; Start-up