The Activated Sludge Model No 1 ASM1. Bengt Carlsson

Transcription

1 The Activated ludge Model No 1 AM1 Bengt Carlsson

2 The Peterson process matrix General expression for a component z in a completely mixed reactor. V z = Qz Qz. z = Dz in in Vr z Dz r z where V=volume of the reactor, Q=inflow rate=outflow rate, D=Q/V=dilution rate, and r z is the process rate Note that r = μ( ) for simple biomass growth. In the process matrix discussed below, only process rates are described.

3 Example 1 Biomass with Monod growth rate and decay: r = μ max K b If we assume that all decayed biomass becomes substrate the process rate for substrate is: 1 r = μ max b Y K

4 Peterson matrix for Example 1 Components r Process Growth 1 1 Y μ max K Decay -1 1 b

5 lowly biodeg substrate s Hydrolysis The bisubstrate model Easily biodeg substrate s Biomass Growth of biomass 1-f Decay of biomass f Inert matter P

6 Example 2. A Bisubstrate model Assumptions: Biomass has growth rate r and a decay b. A typical example of r is r = μ max K O KO The fraction (1-f) of the decayed biomass becomes slowly biodeg matter s, and fraction f becomes inert matter p. The yield is Y and hydrolysis of s has growth rate k O

7 ubstrate dynamics: d dt = 1 Y r k Change of slowly biodeg substrate: d dt = (1 f ) b k Accumulation of inert matter: d P = f b dt Growth and decay of biomass d dt =1r 1b

8 P rate Growth -1/Y 1 r Decay (1-f) f -1 b Hydrolysis 1-1 k

9 In AM1, a bisubstrate model is used for the carbon oxidation process. If the substrate and biomass is measured in COD [g COD/m3] the oxygen consumption is easy to calculate by adding the factors in the growth process: 1 (1 Y ) r 1 1 r = Y Y r

10 AM1 AM1 proposed 1987, probably the most used model for the AP. Previously called IAWQ Model No 1 AM2: 1995, model for biological P removal AM3 1999, improvements of AM1. ubstrate t goes through a storage process ADM1 2001, Anaerobic Digester Model No 1

11 AM1 describes an activated sludge system with carbon oxidation, nitrification, denitrification. In total t l8 processes are modelled: d Growth of biomass (3), decay (2), ammonification of organic N (1), hydrolysis (2) Heterotrophic biomass B,H : - Oxidise carbon under aerobic conditions - Denitrify ( NO =>N 2 ) under anoxic conditions (if substrate available). Autotropic biomass BA : Autotropic biomass B,A : - Nitrify ( NH => NO ) under aerobic conditions.

12 Divisions of carbonaceous material Total COD Biodeg. COD: oluble Particulate Nonbiodeg Active biomass COD: Heterotrophs oluble I Particulate I and P B,H Autothrops B,A

13 Nitrogeneous components Most important components: Ammonia NH Nitrate (and nitrite) NO Biodeg N: ND and ND In summary: 12 components ( alkalinity)

14 Growth rates and processes: growth of heterotrophs B,H - Aerobic growth - Anoxic growth - Decay O H B d, = μ NO H O H B O O H K K K dt,, = η μ μ H B H H B NO O H O g H b K K K,,, η μ H B H,

15 Growth of autotrophs B,A - Aerobic growth - Decay d dt B, A NH O = μ A K NH NH KO, A O B, A b A B, A

16 Model parameters AM1 has 19 model parameters, where some (typically max growth rates) are temperature dependent. Default values exist, but several parameters may need to be tuned to mimic a specific plant. ome set of parameters may lead to (approximately) the same model behaviour! Also the influent water needs to be characterized. ee: Calibrating identifiability and optimal ee: Calibrating, identifiability and optimal experimental design of activated sludge models, B Peterson, PhD Thesis available for download

17

18

19

20 Limitations of AM1 Examples: The ph is assumed to be near neutrality Many of the relations are empirical or based on hypothesis Cells need nutrients to grow which are not modelled Constant temperature. In order to allow for temperature variations, Arrerenius relations may be used

21 Model use Example of use: Testing and evaluation new control strategies Education and process understanding Evaluating new processes and/or operating modes Process optimization Prediction The goal of the modelling should determine how The goal of the modelling should determine how careful the model should be calibrated.

22 imulators, examples GP-, Hydromantis imba, IFAK Efor, DHI West, Hemmis JA U l i JA, Uppsala univ

23 Currently simulators are not used very much by plant personell. Reasons include Lack of computer experience (a decreasing problem) Lack of time (a non decreasing problem) imulators often not very user-friendly Lack of trust

24 IMULATION How? olve d/dt x(t)=f(x(t),u(t)) in discrete time steps by using suitable approximation method, ie: Euler forward/backward Runge Kutta Adams/Gear tandard choice in Matlab: ode45 Numerical problems Errors due to approximations tability tiff problems require special care

25 Things to consider when buying/building a simulator: Pre-defined models, libraries Validated models Flexibility Graphics Numerical methods Optimization tools Operating systems Price, support UER FRIENDLY