IWA TG on Benchmarking of Control Strategies for WWTPs How to generate realistic input data?

Transcription

1 IWA TG on Benchmarking of Control Strategies for WWTPs 10 September 2008 Vienna, Austria Dr Krist V. Gernaey, Dept. of Chemical and Biochemical Engineering, Technical University of Denmark, Denmark Dr, BIOMATH, Ghent University, Belgium 1

2 Outline Introduction and purpose Basic model principles Dynamic influent model: Example Conclusions and perspectives 2

3 Outline Introduction and purpose Basic model principles Dynamic influent model: Example Conclusions and perspectives 3

4 Benchmark Simulation Model no. 1 (BSM1) ASM1 ASM1 ASM1 ASM1 ASM1 Takacs Influent Anoxic; V = 1000 m 3 Anoxic; V = 1000 m 3 Aerobic; V = 1333 m 3 Aerobic; V = 1333 m 3 Aerobic; V = 1333 m 3 Settler Effluent Reactor 1 Reactor 2 Reactor 3 Reactor 4 Reactor 5 Internal recycle, Q intr = m 3 /d Sludge recycle, Q r = m 3 /d Waste sludge Q w =385 m 3 /d Benchmark Simulation no. 1 plant: Predenitrification system Activated sludge tanks: ASM1 Settler: non-reactive Takacs model (10 layers) 4

5 Benchmark Simulation Model no. 1 Protocol that allows objective comparison of control strategies in biological N removal activated sludge plants Evaluation of control strategies is done based on 3 different weather files 1 week of data is used to evaluate the impact of a proposed control strategy Evaluation is based on a number of specified criteria (effluent quality, operational cost, sludge production, energy usage and number/magnitude of effluent violations, etc.) 5

6 BSM1 limitations Evaluation period is too short for comparing process monitoring algorithms No plant-wide control strategies can be evaluated 6

7 BSM2 (Jeppsson et al., WWTmod 2008) IWA Task Group on Benchmarking of Control Strategies for Wastewater Treatment Plants Influent wastewater BSM2 influent Primary clarifier HRT: 1h BSM1_LT influent Activated sludge reactors HRT: 14h Bypass BSM1, BSM1_LT Secondary clarifier SL: 0.6m/h Effluent water TSS: 3% Controllable flow rate Valve Thickener Gas TSS: 7% Storage tank HRT: 1d ASM/ADM interface Anaerobic digester SRT: 19d ADM/ASM interface Dewatering TSS: 28% Sludge removal 7

8 Why a 1 year evaluation period? Long-term effect of control strategies can be considered: DO control: shift in activated sludge populations Waste sludge control Plant performance is tested over a wide range of operating conditions Seasonal variations (T) Range of rain events (intensity, duration) Provides a more realistic framework for comparison of control strategies 8

9 Why an influent disturbance model? Almost impossible to collect a data set on a real system (1.5 years of dynamic data, 15 minute sampling interval) Allows generating characteristics that are necessary for a thorough evaluation of the monitoring algorithms/control systems in BSM1_LT/BSM2 Will result in minimising dynamic influent profile generation efforts Some phenomena to be included in BSM1_LT, (e.g. toxic influent shock loads) would necessitate a model Applications that reach beyond the BSM1_LT/BSM2 system ( WWTP influent scenario builder ) 9

10 Outline Introduction and purpose Basic model principles Dynamic influent model: Example Conclusions and perspectives 10

11 Basic influent model principles Model building in Matlab/Simulink (also other platforms) Model simplicity E.g. limit number of model parameters Model transparency E.g. parameters with physical meaning where possible Model flexibility E.g. The model user can replace one or more model blocks with his/her own model code, or with data (e.g. Rainfall time series data) 11

12 Methodology (2) data IWA Task Group on Benchmarking of Control Strategies for Wastewater Treatment Plants Model function of number of inhabitants presence of industry loads per capita of households and industry size of the catchment length of the sewer system rainfall data interactions with groundwater. WATERMATEX 2007, 7-9 May 2007, Washington DC 12/20

13 Outline Introduction and purpose Basic model principles Dynamic influent model: Example Conclusions and perspectives 13

14 Schematic representation of the influent flow rate model Households Industry Sewer Flow rate profile Seasonal correction infiltration + + Soil + + Rainfall generator 1-aH ah 14

15 One year time series of rainfall: Alpine climate time [d] 15

16 Daily dynamic load patterns for households water COD sol COD part TKN 2 factor [-] :00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 time [h] 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 16

17 Weekly patterns for household loads Alpine time [d] 17

18 Yearly patterns for household loads: Alpine PE Jul Aug Sep Oct Nov Dec Jan FebMar Apr May Jun time [m] 18

19 Output example: one week Alpine PE pcod conc Q pcod load 19

20 Outline Introduction and purpose Basic model principles Dynamic influent model: Example Conclusions and perspectives 20

21 Conclusions and perspectives Phenomenological models of limited complexity can be used to build WWTP influent scenarios, without the need of complex deterministic models of the urban drainage system The models are used to generate the influent data for BSM1_LT and BSM2 Potential applications within simulation-based evaluation of WWTP design, upgrade and control scenarios 21

22 Thank you for your attention! Questions? 22