XMTVb XMVssltm1056

where XM ,, is the settled MLSS concentration. Thus, it can be seen that when the settled MLSS concentration is as large as possible, the retained volume, Vb„ will be as small as possible. Although experience with treatment of a particular wastewater is the best way to select the maximum achievable value of XM Ir, it can also be estimated from the definition of the sludge volume index (SVI). As discussed in Section 10.2.1, the SVI is defined as the volume in mL occupied by a gram of solids after 30 minutes of quiescent settling. If we take the SVI as being indicative of the highest concentration to which the MLSS can be settled, then the maximum attainable settled solids concentration, XM.Tt.max, (in mg/L) will be given by:


It follows from Eq. 10.56 that the smallest retained volume, V,Hm„„ is given by:

Consequently, the lower limit on the sequencing hatch reactor (SBR) volume is obtained by substituting Eq. 10.58 into Eq. 10.55:

Although care should be exercised in selection of the XM ,, SBRAS systems offer some flexibility in the control of the MLSS settling properties. Recall that SBRs can achieve any hydraulic characteristic between a CSTR and a PFR simply by changing the length of the fill period. This means that the instantaneous process loading factor can be changed simply by changing the length of the fill period. Since the process loading factor influences the competition between filamentous and Hoc-forming bacteria, as discussed in Section 10.2.1, changing the length of the fill period allows control of the settling properties, provided that the oxygen transfer system is capable of meeting the imposed oxygen requirement. Thus, steps can be taken during operation to ensure that the selected SVI is achieved, as long as a realistic value was selected during design.

With the above information in mind, we can now set forth the steps in the design of an SBR system. After selection of the effective SRT. the mass of MLSS in the system is calculated with Eq. 9.11. The number of cycles per day is then selected, taking into account the length of time in each cycle to be devoted to settling and decanting. Since the flow to be treated is known, this fixes both F, and Estimation of an attainable SVI value allows computation of XM li m„ with Eq. 10.57, which allows calculation of the minimum possible bioreactor volume with Eq. 10.59. Selection of the design bioreactor volume requires consideration of the oxygen requirement and the constraints on the rate of oxygen transfer and floe shear, just as with all of the other activated sludge systems. Equations 9.13 and 10.16 can be used to calculate the total daily oxygen requirement. While the mass of oxygen required per cycle will just be the total daily requirement divided by the number of cycles per day, the oxygen transfer rate will depend on the length of the fill period during that cycle. If the fill period lasts throughout the entire react cycle, then the system will behave like a CSTR and the oxygen transfer rate will be equivalent to that in a CMAS system, allowing the techniques for that system to be used. On the other hand, the shorter the fill period relative to the react period, the more the system will behave like a PFR, requiring the techniques used during CAS design to be used. No matter which technique is employed, however, the design reactor volume must be selected to ensure proper mixing and oxygen transfer. Floe shear is seldom a problem with an SBRAS process because flocculation will generally occur later in the react period when aeration rates are lower. Suspension of solids is not a problem in facilities which provide separate mixing equipment. Nevertheless, the designer should verify, either by calculation or logical analysis, that these constraints will be satisfied. After the volume is known, the anticipated MLSS concentration can be calculated by dividing (XM• V)Sv,„m, as calculated with Eq. 9.11, by the selected bioreactor volume. The effective HRT can then be determined with Eq. 7.8 and the How rate of waste solids can be calculated with Eq. 7.9 after it has been determined when in the cycle solids will be wasted, thereby determining XN, ,„. The required solids mass

wastage rate is also given by Eq. 9.12, so a system check can be made. The following example illustrates the unique aspects of SBRAS design.


An SBRAS facility is to be constructed to treat the wastewater considered throughout Section 10.3. A partial degree of sludge stabilization is important, so an effective SRT of 10 days is to be maintained. Four cycles per day are to be utilized at average flow to allow more cycles to be used to treat peak flows. A value of £ of 0.5 will be used for design purposes, again to allow operational flexibility during peak flows. Experience indicates that the SVI in the process will generally not exceed 120 mL/g. What is the minimum possible bioreaclor volume that could be used and what MLSS concentration would be associated with its use? What effective HRT would the SBRAS have at average flow?

a. What value of (X m i ■ V)Swtl„ should be used for design purposes?

(XM- V)Swo„ is calculated using Eq. 9.11 at the winter operating temperature of 15°C. Values of all of the parameters in Eq. 9.1 1 are given in Table E10.2. Using these values and (•),. = 10 days:

. 435 + [1 + (0.20)(0.15)( 10.0)](0.5)( 115 + 150)"

b. What value of XM ,,„,,, is appropriate for this design?

As noted above, the SVI for this application is expected to be less than 120 mL/g. Using Eq. 10.57,

10" 10"

svi 120 * • y c. What is the minimum possible bioreactor volume?

Since there will be four cycles per day, and the average daily flow rate is 40,000 m'/day, the flow per cycle, F„ is 10,000 m\ Substitution of this value into Eq. 10.59 gives:



d. What is the bioreactor MLSS concentration?

This is calculated by dividing the mass of MLSS in the system, as calculated in Part a, by the bioreactor volume.


What is the effective HRT of the bioreactor. From Eq. 7.8,

10.3.7 Process Optimization Using Dynamic Models

As discussed in Section 9.4.3, dynamic simulation is an important tool that allows an engineer to refine the design of an activated sludge system or to evaluate alternative operating strategies for an existing facility. The necessity for using this tool increases as the system to be designed is further removed from the assumptions inherent in the simple stoichiometric models that are the basis for the analytical expressions in this chapter. For example, if a CMAS system were to be used to treat a wastewater flowing at a constant rate and containing a constant concentration of soluble pollutants, the approach presented in this chapter would be very accurate because the assumptions in the model are totally consistent with the nature of the problem. In fact, the equations would even do a very reasonable job under dynamic loading conditions. If part of the pollutants were particulate, on the other hand, the equations would still be accurate for a CMAS system operated under constant loading conditions, but they would be more approximate as dynamic loads were applied because they contain no rate expressions for hydrolysis of particulate substrates. However, by using approximations based on experience, it is still possible for the approach to give adequate information about transient-state oxygen requirements in a single tank system. The approaches presented in this chapter are weakest for design of multitank systems treating wastewaters containing both soluble and particulate pollutants, regardless of the nature of the influent flow. This is because the apportionment of the oxygen requirement among the various tanks requires assessment of the rates of degradation of both soluble and particulate constituents, information that is not incorporated into the simple model upon which the approaches are based. However, experience can help the engineer make the decisions required, although the information will always be approximate. Consequently, as the system configuration becomes more complex and as the influent conditions become more dynamic, the engineer must exercise more caution in the application of the approaches presented and should rely more and more on experience to make decisions.

Given the situation described in the preceding paragraph, what should be done when the experience base is small, either for an individual engineer or for the profession? The answer to that question is to rely more on dynamic simulation. As discussed in Part II of this text, IAWQ ASM Nos. 1 and 2 adequately represent a number of suspended growth biological treatment systems, and are particularly effective for activated sludge systems of the type presented here. Furthermore, software packages implementing them are readily available, as indicated in Table 6.4. Using the techniques described in Chapter 8, the parameters in ASM No. 1 can be assessed with sufficient accuracy to allow it to adequately mimic the performance of real systems. Therefore, an engineer can use the approaches presented in this chapter to decide on tentative sizes for the various bioreactors in an activated sludge system, and then use simulation to investigate the oxygen requirement in each vessel in the system under a variety of anticipated dynamic loading scenarios. That output can then be used to evaluate the ability to transfer the needed oxygen while meeting the constraints on floe shear and mixing, allowing modification of the design as needed. Additional rounds of simulations can then be used to further refine the design. Because the model is known to reflect reality,7 the engineer can have more confidence in the proposed design than he/she could have in a design based on the approximate approaches presented here. This allows smaller factors of safety to be used, resulting in more economic designs, etc.

In addition to the benefits presented above, simulation allows a neophyte engineer to build an experience base in a relatively short time. By investigating a variety of activated sludge variations by simulation, and comparing the output to the approximations obtained through the analytical equations, an engineer can quickly learn how much uncertainty is associated with the approximate analytical approach for a given configuration. That information then becomes part of the engineer's experience base, which allows better use to be made of the approximate techniques when they are suitable. Returning to the concepts presented in Chapter 9. a good engineer will use the tool that is appropriate for the job at hand. Simulation is one of those tools, but it should not be viewed as something that is only justified for large and complex systems. Rather, the ease with which it can be done suggests strongly that it should be used to learn more about systems, just as it was used in Chapters 6 and 7, thereby increasing the engineer's experience base. Thus, it should be thought of as an important extension of this book.

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