Lorenzo Benedetti, Ph.D. Project examples.

Transcription

1 Lorenzo Benedetti, Ph.D. Project examples

2 Modelling of the integrated urban wastewater system of the city of Eindhoven (Netherlands) Waterschap De Dommel, Netherlands Eindhoven, Netherlands 21 in course Sewer, river and WWTP modelling; sensitivity and uncertainty analysis; scenario analysis. Waterboard De Dommel and the City of Eindhoven intend to control storm water and waste water flows within the Eindhoven cluster area to a higher extent to efficiently meet the requirements of the European Union Water Framework Directive. The targeted water flows consist of the wastewater, the runoff water and the storm water of the City and nine surrounding communities feeding the waste water treatment plant. After biological nutrient removal, the effluent of the treatment plant is discharged into the river De Dommel. Rain water flow increases the flow to the treatment plant by a factor of five, of which a part is treated in a parallel rain water treatment line. During intense storm water events, about 2 sewer overflows may discharge the surplus of (polluted) storm water into De Dommel, affecting the chemical and ecological quality of the river negatively. Optimising and controlling the different water flows is a technically complex, due to al required measures for monitoring, modelling, collecting and treating the water. Rough cost estimations show that individually solving of these bottlenecks would require investments of about 15 to 2 million Euro. With this project, the consortium approaches the individual problems differently in a new integrated strategy. By applying an innovative combination of monitoring, modelling and controlling water flows and constructing adequate technical measures like pumping an advanced treatment facilities, the storm water and waste water flows will be actively controlled based on water quality and quantity (so called pollution load control). By applying this integrated and high technology approach, it is expected to save approximately 1% to 3% of the overall costs. The integrated model will have: for the sewers, TIS hydraulic model, also no water quality model, and simplification also at spatial level, lumping catchments and modelling only significant pipes and overflows for the WWTP, the model will be the same as the detailed one, as it is already TIS and limited spatial complexity, while the water quality model will not need simplification as it is not the computationally demanding part of the IUWS model for the river, also a TIS model will be made for hydraulics, and water quality model will stay very similar to the detailed one; the spatial discretisation will depend on the significant inputs and on the river hydraulics.

3 The above three models will be implemented independently, calibrated and validated using data from simulations of the detailed models, and then integrated (after developing specific interfaces to translate state variables between models) into a single executable model. This allows to overcome: the communication problems between different software, reducing the possible scenarios to be run, especially regarding integrated RTC; the simulation speed problem of the detailed models, allowing to reduce the time needed to run each (long term) scenario by several orders of magnitude. After exploring scenarios with the IUWS model, the most promising ones may be tested on the detailed models, as a validation step. Son en Breugel Nuenen Eindhoven Veldhoven Geldrop-Mierlo Waalre Eersel Bergeijk Valkenswaard Heeze-Leende Sewer, river and WWTP modelling. Integrated real time control. Wet weather management.

4 Modelling of the WWTPs of Lynetten and Damhusaen (Denmark) Rambøll, Denmark Copenhagen, Denmark 21 in course WWTP modelling; sensitivity analysis; scenario analysis. Within this project, several activities have been performed for the Lynetten WWTP (7, PE) and the Damhusaen WWTP (3, PE): model assessment, improvement and implementation of upgrade options; sensitivity analysis of operational parameters; connection with data from sewer models A model to simulate aeration tank settling (ATS) mode was realized and implemented in the Damhusaen WWT model. The ATS model is able to reproduce the expected behaviour and to show the benefits of ATS with regard to TSS effluent. The global sensitivity analysis (GSA) conducted on operation, ATS model and settling models parameters lead to the general conclusion that, for all outputs, high sensitivity is associated to parameters from the secondary settling model (especially for TSS), followed by the secondary settling underflow recycle ratio, the ATS model, the primary settling model (especially for nitrates and TN), DO set points and biology bypass flow threshold (for maximum values). A model was realised to transform the data coming from a sewer model into inputs to be fed to the WWTP models. SST_r_H return_sludge_control_ratio 3 ATS_split SST_v SST blanket ATS m SST blanket no ATS m T2_T5_ATS 1 ATS_control_Q_OFF days ATS modelling. Sewer model data treatment for integrated modelling. Sensitivity analysis for priority setting.

5 Modelling of IFAS treatment of industrial effluent (Denmark) EnviDan, Denmark Hellerup, Denmark 21 WWTP modelling; scenario analysis. Verification of dimensioning of IFAS process for treatment of beverage production factory. Scenarios of different volumes and aeration capacities. Validation of design options.

6 Modelling of upgrade scenarios for the Riga WWTP (Latvia) EnviDan, Denmark Riga, Latvia 21 WWTP modelling; scenario analysis. The aim of this project was to compare two different upgrade options for the Riga WWTP (1,, PE): the Biodenitro alternative (with several phasing scenarios) and a side stream fermentation alternative (with reject water recycle in line or in side stream) at different temperatures NO3 g/m Side stream fermentation modelling.

7 Modelling of the Drava River (Croatia) MOSTforWATER NV, Belgium Varaždin, Croatia 21 River water quality modeling; scenario analysis. The aim of this project was to illustrate the convenience of models as tools to guide environmental investments. Based on simulations, the effects of river restoration options were tested and optimal scenarios were identified. In this study, the use of a combination of models was illustrated to analyze the relative impact of wastewater discharges on the water quality as well as ecological status of the water system. For this purpose, wastewater treatment, river quality and ecosystem models were coupled. The river water quality model was calibrated using data collected within the project. in_riv3 CF3 sec_b2 sec_b3 sec_b5 sec_1 ch2 sec_12 sec_16 FC2 out_2 in_riv2 CF2 Drava1 sec_3 sec_7 sp_c sec_ sec_1 sec_14 sec_15 sp_d sec_17 FC1 out_1 in_riv1 CF1 ch1 1 1 in_riv4 CF4 sec_a3 sec_4 sec_a5 sec_9 1 no WWTP eff. 1 N P removal in_wwtp CF5.1.1 current no treatment BOD O2 NO3 PO NH COD sec_9 River water quality model calibrated and coupled to ecological status model. Simulations allowed identifying the consequences of wastewater treatment scenarios on the ecological status.

8 Wet weather treatment upgrade scenarios with sensitivity and uncertainty analysis at the Eindhoven WWTP (Netherlands) Waterschap De Dommel, Netherlands Eindhoven, Netherlands 29 WWTP modeling; sensitivity and uncertainty analysis; scenario analysis. With the aim of improving the performance of the waste water treatment plant in wet weather conditions, an existing model of the Eindhoven WWTP was expanded with primary treatment and rain water treatment line. This new model was used to test upgrade scenarios and with a preliminary global sensitivity analysis (by Monte Carlo simulations) to select important operational parameters to find good options for modified operational settings in wet weather. An uncertainty analysis and a longer time series simulation were conducted to test the best resulting scenarios and were helpful in finding successful scenarios. The model realised for this analysis will mainly be used: (1) as instrument for decision making of future investments in emission control, (2) for cost and energy optimization of plant operating, (3) as internal and external communication tool, (4) for its ability to perform scenario analysis considering also influences of climate changes and (5) for the implementation of predictive model based control in future plant operation. 7 NH4 95th [mg/l] optim1 optim2 optim3 optim4 optim5 Very good agreement of data and simulations. Sensitivity analysis for priority settings and scenario analysis for systems optimization.

9 Modelling of the Ostend WWTP (Belgium) for energy consumption reduction Aquafin N.V., Belgium Ostend, Belgium 2 29 Water quality modeling; energy modeling; scenario analysis. At the WWTP of Oostende, operated by Aquafin, an advanced aeration control is installed, based on online measurement of oxygen in the carrousel and of both nitrate and ammonium at the effluent of the activated sludge tanks. As the plant meets the discharge limits at all times, a model was set up to evaluate the possibilities to reduce the energy consumption of the plant. Based on data from the years 26 and 27, the model was calibrated and resulted in good agreement with the available data of daily aeration energy consumption. Monthly averages agree with differences around 5% and never more than 1%. The same conclusion could also be made for total energy. A scenario analysis on the aeration controller settings (based on DO, NH 4 and NO 3 on line sensors) revealed that an energy saving up to 5.5% can be obtained. Total nitrogen would slightly deteriorate (effluent NH 4 increases but NO 3 decreases). So, it is clear that as the plant already has an advanced aeration controller no gain in energy consumption can be achieved without giving up on effluent quality, and that an optimum combination of set points can be selected whether energy or effluent consents are the target. Model of a plant with advanced control for N removal. Energy consumption modeled with good accuracy. Scenario analysis allows to select controller settings favorable to effluent quality or to energy saving.

10 Integrated modelling in the Besos River basin (Spain) Besos River Authority, Spain Girona, Spain 2 Integrated urban wastewater system modeling; sensitivity analysis; scenario analysis. The system is constituted by two communities, their corresponding draining catchments, sewer systems and two WWTPs, which discharge treated water at different locations of the same river. Within this context, the realisation of an integrated model of the river stretch and of the two WWTPs with their sewer systems and draining catchments was necessary. Such a model allows to efficiently simulate and analyse the behaviour of the integrated system and to optimize its performance holistically. This is achieved by identifying, with the help of Monte Carlo simulations, the most performing operational parameters according to environmental and economic criteria in different weather conditions. La Garriga Granollers NH4_control_G.y_S bypass_g.q_out2 NH4_control.y_S Sludge_waste_G.Q_Out2 sp_tank_1_g.q_out2 bypass.q_out2 PST_1_G.Q_Under sp_bp_wwtps.q_out2 PST_1.Q_Under sludge_control.constantratio by-pass NO3_control.y_S sp_tank_1.q_out2 primary_bypass_g.q_out2 Sludge_waste.Q_Out2 Settler_G.Q_Under NO3_control_G.y_S primary_bypass.q_out2 Integrated model of 2 WWTPs, sewers and river. Sensitivity analysis for priority settings. Scenario analysis for systems optimization.

11 Modelling of the Iskar River (Bulgaria) Ghent University, Belgium Sofia, Bulgaria 26 2 River water quality modeling; scenario analysis. The aim of this project was to illustrate the convenience of models as tools to guide environmental investments. Based on simulations, the effects of river restoration options were tested and optimal scenarios were identified. In this study, the use of a combination of models was illustrated to analyze the relative impact of wastewater discharges on the water quality as well as ecological status of the water system. For this purpose, wastewater treatment, river quality and ecosystem models were coupled. The river water quality model was calibrated using data collected within the project. River water quality model calibrated and coupled to ecological status model. Simulations allowed identifying the consequences of wastewater treatment scenarios on the ecological status.