ACT IVATED SLUDGE P ROCESS
Process Variations
A complete comparison between these variations of the activated sludge process can be found in Grady et a1. The history of the activated sludge process is very interesting and the reader is encouraged to learn more about it by referring to Jeppsson (1 996), Grady et a1.
Modeling of Activated S ludge Systems
- B io mass Decay
- Hydrolysis
- A Basic Mode l
- Advanced Models
- Reduced Order Models
Substrate + Nutrients + Oxygen ---t B io mass + Energy ( 3 . 1 ) The main type of bacteria in activated sludge (called heterotrophic bacteria) use organic carbon in the form of small organic molecules as a substrate, some bacteria (called autotrophic bacteria, nitrifying bacteria or nitrifiers), which are essential for biological nutrient removal, use inorganic carbon as a substrate. Specific growth is called the coefficient of specific rate because it defines the rate of biomass growth in relation to the concentration of active biomass present, i.e. mass of COD biomass produced per unit of time per unit of COD of active biomass present. E are 111a biomass in th Y tern., the rate of rna s wa tage lids, the amount of oxygen to be supplied and the amount of nutrients required.
Recently, several publications have discussed solid flux theory in one way or another. the following is a presentation of some of these recent publications. 1 99 1 ) proposed a double exponential expression representing the settling function, i.e. the relationship between settling velocity and particle concentration. The first is efforts to develop capital cost relationships and can be used in the preliminary cost assessment of new wastewater treatment plants (eg, the EPA report).
The last section presents the model in the form of an optimization problem, while. the other presents an item algol oplimiL.aliull that is u ed for solving the sol- ·e tl-Lis optimization problem. This part of the file gives an exact design of the primary clarifier for a given value of q. 2) The second part of the file uses the Solver function in Excel to find the values for all the state variables in flow 3 (which are the same in the aeration tank) for a given value of S RT and H RT and depending of the primary clarifier design given in ( 1. The above-mentioned adjustments in the design of the aeration tank: i.e., the increase in volume and the reduct ion in SR T have reduced the sludge production to a level as if the primary clarifier was present.
I econdary ed irnentation Models
Wastewater Treatment Plant Costs
A is the area of the surface in m1 Q is the flow in ml/s, V is the volume in ml, Gs is the air flow rate in ml/s, HP IS the IIlst�l Ie power for aeration in kW , Wmu and wop are wage parameters for maintenance and operation, p( IS the cost of energy cents/kW h , W is the installed power for pumping in kW, and i is a co t IIldex. A comp A list of the parameters used in the system design and optimization are shown in Table 6.8, which is one of the reasons that usually encourages the designer to consider denitrification in the system.
New boundaries are proposed in this application and they are then considered in the next part of the analysis. As a result, an upper limit was placed on the overflow rate in the system model. Contrary to the above, the total COD in the effluent shows a completely different effect (as shown in Figure 8. 1.
It is mentioned that the basic design assumes a medium-strength waste water in the inlet pipe.
MODEL DEVELOPMENT
System Components
A complete description of the system requires the specification of two groups of mbol. Examples are kinetic and stoichiometric parameters in the activated sludge process, cost index value, sedimentation constants, etc. The stoichiometric and kinetic parameters considered in the ASM3 model are shown alone in Table 6.3 and Table 6.5, respectively, and are not shown in Table 6.
They specify the dimensions or the design condition of a unit process or a stream in the model. They represent the wastewater characteristics at a specific stage during the treatment process, and they are defined at the eight control points shown in Figure 6. Later, when we need to solve the model, these variables are separated into decision variables and state variables.
Model Formu lation
Where Ap is the surface of the primary clarifier c (m2) and Q2 is the flow rate of the primary effluent (m3/d). Concentrations of solids and such leachable constituents in the subsurface flow were calculated as solutes and thus leachable constituents were calculated in the primary flow. According to defmit ion, it is the RNA of organisms in the reactor divided by the mass of organisms removed from the system each day (Metcalf and Eddy, 1991.
Total oxygen demand is the sum of the oxygen demand for organic matter removal plus the oxygen demand associated with nitrification. The next and last unit operation in the system considered is the secondary sedimentation tank: which will be the subject of the next section. The secondary sedimentation tank flow (as shown in Figure F 6. 1) is the aeration tank flow (stream 3), while its effluent consists of two streams (streams 4 and 5).
The cost of pumping is the product of the power demand and the unit power cost.
Optimization Problem
Optimization Using GAMS
This chapter is devoted to illustrating the use of the optimization model introduced in the previous chapter. This indicates that the solution was obtained in the initial run. ummariz d in Table 7.8) appears to be a robust global optimal solution. The percentage of y tern after increasing X/ in the influent is not much different from the ba e model cost.
It is analogous to applying a strong strength 'N Examples of such studies are given in this work which can be extended to cover all parameters in the model. In the basic solution, the influent flow rate was 40,000 m3/d (1,500 m3/h), which is considered an average for domestic wastewater treatment. This is attributed to the again significant increase in aeration tank volume and AFR. However, it is noticeable that the design of the primary sludge and the secondary sludge are not affected by this change in the strength of the inflowing waste water. The consideration of uncertainty in the design process can provide the answer for such a question. To summarize the above discussion, uncertainty in the introduced optimization model is attributed to various sources. JiA is the autotrophic maximum growth rate responsible for nitrification in the activated sludge process. A possible option is to use the model at one of the wastewater treatment plants operating in the UAE. Three main sources of uncertainty can be identified: uncertainty in the structure of the model itself, variability in the considered characteristics of the influent wastewater and uncertainty in the parameters of the operating models. Uncertainty in the model structure refers to the uncertainty caused by everything that is not modeled, in other words, the uncertainty caused by all processes not included in the model. In the illustrative problem in Chapter 7, the fluent features were assumed to be deterministic and moderately strong, but this is not the case. As an example, the total COD in the inlet to the Mafraq wastewater treatment plant has ranged between 265 and 540 mgIL as COD, and the TSS has varied between 1 24 and 270 mglL as TSS during the month. In this context, wastewater suspended solids are related to MLSS, surface overflow velocity, SVI, and lateral water depth in the tank. M LS and surface overflow velocity are variables in the model, while SVI and lateral water depth (H) are considered as parameters. These include uncertainties, such as the parameter describing the efficiency of the diffuser, which depends on the type of diffuser and the depth to which the air is pumped, this uncertainty has a small effect on the behavior of the model as it does not directly contribute to the calculations. In Chapter 8, the performance of the model for different sets of k i netic parameters at different temperatures was evaluated. In general, temperature variation affects the kinetic parameters, which in turn significantly affects the performance of the model. A minute sign indicates a reduction in cost, while a positive one indicates a mcrea, r fered to values obtained (Table 8. 1) at zero variability of each relevant temperature. It is evident from the table that the variability of the kinetic parameters has a different effect on the optimal solution of the model. Moreover, all changes are negligible except those caused by the variability of bH JiA and KA. In the example shown, the reduction in growth rate required the system to increase SR T to allow more time for nitrification. Conversely, there is also variability in other parameters, but their influence is small compared to the influencing characteristics and the influence of )1A. Such a layout is applicable to most operated activated sludge treatment plants, and with few modifications it can also be applied to different reactor types, flow regimes and variations in activated sludge processes. This obviously indicates that the developed model can be used as an analysis tool in addition to a design tool. By using a model like the one proposed in this study, the interactions can be easily investigated and no worries from biological treatment causing failure in the subsequent settlement. It has been shown in most runs for the system considered that the primary clarifier is not an effective unit and cost savings can be realized by considering a system without a primary clarifier. Regarding the second direct ion, several studies and researches can be conducted using the developed model. Potential research areas can be identified where the system is highly sensitive to a particular parameter. Such can be extended to link a more comprehensive uncertainty analysis tool such as Monte Carlo simulation to the developed model. Rivas Selection of Operational Strategies in Activated Sludge Processes Based on Optimization Algorithms", Water Sci. Dold ( 1 997a), "General Model for Biological Nutrient Removal Activated-Sludge Systems: Model Presentation", Water Environment Research, 69, 5, pp .Durst Design and construction of aeration systems for optimal operation of large wastewater treatment plants", Water Sci. Water Pollution Control Federation, 55, 1 2, p. teady tate Analysis of the Coupling Aerator and Secondary Settling Tank in Activated Sludge Process", Water Res. Dahl One-Dimensional Model for Secondary Settling I nc luding Density Current and Short Circuiting", Water Sci. Siegrist Calibration and Validation of an ASM3-Based Steady-State Model for Activated Sludge Systems - Part I: Prediction of Nitrogen Removal and Sludge Production” Water Res. Kummel A New Control Strategy for Enhanced Nitrogen Removal in an Alternating Activated Sludge Process - Part I", Water Res. BMODEL PER FORMANCE
E ffect of Solids Retention T ime
CO S IDER lNG UNCERTAINTy
Sources of Uncertainty
Sensitivity Analysis
U ncertainty Based Optimal Design
CONCLUSIONS AND FUTURE RESEARCH
Future Research
