WEFTEC 2014
Inverse Modeling of Nitrification-Denitrification Processes: A Case Study on the Blue Plains Wastewater Treatment Plant in Washington, DC. Jamal Alikhani1, Imre Takacs2, Ahmed Al Omari3, Sudhir Murthy3, Yalda Mokhayeri4, Arash Massoudieh1* 1234-
Civil Engineering, The Catholic University of America,Washington, DC 20064, USA Dynamita, 26110 Nyons, France DC Water and Sewer Authority, Washington, DC 20032, USA AECOM, Washington, DC 20032
*- Corresponding Author (Email:
[email protected])
Abstract Evaluating the uncertainty associated with the predictions of Activated Sludge Models (ASMs) is essential in designing and optimization of biological wastewater treatment systems. The sources of ASM model prediction uncertainties can be classified into influent and environmental factor uncertainties, parameter uncertainty and epistemic uncertainty due to model abstraction. In this study, observed data obtained from nitrification-denitrification processes at the Blue Plains advanced wastewater treatment plant is used to quantifying the uncertainty associated with ASM parameter estimation. The posterior distributions obtained for parameters are then used in a Monte Carlo simulation to assess the uncertainties of predicted effluent constituents expressed as the 95% credible intervals of effluent concentrations. Using the outcome of the parameter estimation step, two scenarios of methanol loading scheme including one based on flow-rate (baseline) and the second one based on total nitrogen in the influent were evaluated. The treatment performance of the system was quantified based on the probability distributions of effluent violation with respect to certain water quality standards. The results show that methanol loading based on influent flow-rate is less effective compared to determining methanol loading based on total nitrogen loading in the influent.
Key words: Inverse modeling, Parameter estimation, Bayesian Inference, Markov Chain Monte Carlo, Activated Sludge Model.
Introduction Activated Sludge Models (ASMs) are widely used for the design, control and optimization of suspended biological treatment processes in municipal wastewater treatment plants (WWTPs) (Han et al. 2014, Yang et al. 2014, Busch et al. 2013). Optimization of activated sludge processes can substantially reduce the capital and operational costs of WWTP both in design and operation
Copyright ©2014 Water Environment Federation 4556
WEFTEC 2014
stages, and to find out cost-effective improvement strategies for the existing plants. Finding the values of stoichiometeric and reaction parameters is typically one of the challenging steps in applying such models to existing plants. Different parameter values have been reported in the literature for different conditions (Hauduc et al. 2011, Cox 2004, Henze et al. 2000) which makes determining their optimal values from the range of each parameter provided difficult. Moreover, batch experiments or pilot studies under the similar condition of plant can be performed to quantify the parameter values of the most important parameters (Bullock et al. 1996), but this approach has its own difficulties and drawbacks including but not limited to the challenges in experimental set up, investment in time and money, the availability of appropriate and accurate equipment and methods, and finally interpreting the experimental results to obtain the desired parameters. Another approach is estimation of the parameter values by comparing the model results with measured observed data obtained from the plant and then adjusting the parameters until a good agreement is obtained (also referred to as model calibration or inverse modeling). Deterministic approaches resulting in an optimum set of parameters obtained by minimization of an error function between modeled and measured concentrations of wastewater constituents has been used for ASM parameter estimation in the past (Keskitalo and Leiviskä 2012). Deterministic methods provide a single set of parameters, and therefore it is not clear how much deviation from those estimated values should be considered within the range of acceptable values and what the shape of the region of plausibility in the parameter space looks like. To evaluate the uncertainty associated with the model predictions when using ASMs for optimization of the operation and design of biological treatment systems, it is necessary that the parameter uncertainties be quantified. Different source of uncertainties as a result of non-uniqueness of optimum parameters or lack of sensitivity for some parameters, measurement errors and model structural errors are inevitably propagated into the estimated parameters that need to be quantified (Sharifi et al. 2014, Sin et al. 2011, Sin et al. 2009). Knowing the probability distribution of model parameters, they can be propagated into model outputs which results in a probability distribution of model constituents that can be used to evaluate the credible intervals of the effluent water quality indicators. These credible intervals can in turn be used for better decision making in the design or optimization (Sin et al. 2009). Performing uncertainty analysis can result in more realistic safety factors that can reduce the cost, energy consumption and increase the reliability of the biological treatment processes (Bixio et al. 2002). Bayesian inference is a powerful tool that is capable of providing joint probability distributions of the parameters representing the degree of belief about the parameters using the observed data (Sharifi et al. 2014, Albrecht 2013). The prior ranges of the parameters which are known based on literature survey, expert knowledge or lab experiments can be used to create the multidimensional plausibility region of the un-known parameters. In this study the application of a modified version of a previously developed parameter estimation framework for activated sludge systems, BIOEST (Sharifi et al. 2014) on the estimation of the parameters of nitrificationdenitrification process using data collected at the Blue Plains wastewater treatment plant is demonstrated. Both influent/effluent wastewater characteristics and profiling data collected inside the bioreactor over a period of 100 days was used for the parameter estimation. Using these estimated parameters, the effect of two operation strategies in the external methanol injection are examined in a probabilistic way and the preferable operation strategy in term of cost and the frequency of exceeding nutrient discharge threshold has been identified.
Copyright ©2014 Water Environment Federation 4557
WEFTEC 2014
Methods and Materials Bayesian framework In the Bayesian framework, the joint probability distribution of the parameters after incorporating the observations (referred to as posterior distribution) can be expressed through Bayes Theorem as (Kaipio and Somersale 2004): )= P(Θ, Γ | C
| Θ, Γ) ⋅ P(Θ, Γ) P(C ) P(C
Where p indicates probability, Θ and C are respectively the ASM un-known parameter and observed data vectors, and, Γ is the observation error variance matrix. Assuming a given prior distribution of parameters, P(Θ, Γ) , and calculating the likelihood function, P(C | Θ, Γ) using an error structure, the posterior distribution, P(Θ, Γ | C ) , can be obtained. In this study, both the prior and likelihood considered as log-normal distributions. For detail explanation in application of Bayesian in the ASM stochastic modeling, BIOEST framework, the interested readers can refer to Sharifi et al. (2014). The BIOEST framework can perform both deterministic parameter estimation using a hybrid Genetic Algorithm and stochastic parameter estimation using MCMC. The code is flexible in terms of allowing users to define their own reaction network with the number of parameters, constituents and reactions being limited by the hardware’s ability to solve the system. The reaction rate expressions and stoichiometric constants can be expressed as userdefined functions of parameters and constituent concentrations. Observed data Stochastic parameter estimation of the nitrification-denitrification (Nit-DeNit) phase of the Blue Plains advanced WWTP, in Washington, DC is performed as a case study. The Blue Plains NitDeNit configuration is consisted of eight continuous stages, including two large and six in smaller compartments. The eight stage reactor system having an active volume of 17,500 m3 considered for modeling using the Bayesian parameter estimation program framework is shown in Figure 1. Nitrification occurs in stages 1 through 3 which are under aerobic conditions. Stage 4 is not aerated and functions as deoxygenation zone. Methanol is added in stage 5 to aid in denitrification which occurs in stages 5 through 7 under anoxic conditions. The last stage is aerated to improve biomass settling in the clarifier. Daily flow rates of influent and its characteristic, waste activated sludge (WAS) flow-rate and return activated sludge (RAS) flow-rate is shown in the Figure 2. The concentrations of total/volatile suspended solids (TSS/VSS), dissolved oxygen (DO), soluble COD (sCOD), TKN, ammonia (NH 3 ), nitrite-nitrate (NO x ) and methanol concentration from samples collected at different locations and during the February and June of 2010 was used as observed (Figure 3).
Copyright ©2014 Water Environment Federation 4558
WEFTEC 2014
Fig. 1. Eight tank configuration in the Ni-DeNit model. The volumes are not to scale and each tank is considered a completely mixed reactor (CMR).
150000
150000
1200 800 400 0
1/22/10
3/3/10
4/12/10
5/22/10
Date
7/1/10
WAS (m3/day)
75000
12 12 20
8
10 3 2 1
1/22/10
3/3/10
4/12/10
5/22/10
NOxx (gr N/m3)
Inflow (m3/day)
75000
16
S S (gr COD/m 3)
100000
225000
RAS (m3/day)
125000
20
NH-N (gr N/m3)
b)
a)
0 7/1/10
Date
Fig. 2. a) Daily influent flow-rate, RAS, WAS. b) Daily influent biodegradable soluble COD (Ss), ammonia (NH-N) and Nitrate (NOx).
Modified activated sludge model The Blue Plains advanced WWTP uses methanol as external carbon source for denitrification. Thus, we added a new type of heterotrophic microorganism category, referred to as methylotrophs that can solely utilize methanol in anoxic conditions (Dold et al. 2008). Consequently, three new reaction processes was added to the ASM1 (Henze et al. 1987) reaction matrix. The modified ASM model is henceforth referred to as M-ASM1, and is shown in the Table 1. In this table the process description, stoichiometery coefficients for each constituent in each reaction, and the reaction rates are reported. In addition to adding the three new processes, some modification on decay rates is also done. Also, two new constituents involving methanol concentration (S m ) and methylotrophic biomass (X B,M ) is added to the 13 original constituents of ASM1. The list of M-ASM1 parameter is listed in the Table 2.
Copyright ©2014 Water Environment Federation 4559
WEFTEC 2014
Table 1- Process reaction rates and constituents stoichiometeries in M-ASM1 J
Process
1
Aerobic growth of heterotrophs
2
Aerobic growth of heterotrophs on Methanol
3
Anoxic growth of heterotrophs
4
Anoxic growth of Methylotrophs
5
Rate 𝑆𝑆 𝑆𝑂 𝑆𝑁𝐻 μH 𝑋 𝐾𝑆 + 𝑆𝑆 𝐾𝑂,𝐻 + 𝑆𝑂 𝐾𝑁𝐻 + 𝑆𝑁𝐻 𝐵,𝐻 𝑆𝑀 𝑆𝑂 𝑆𝑁𝐻 μH 𝑋 𝐾𝑀,𝐻 + 𝑆𝑀 𝐾𝑂,𝐻 + 𝑆𝑂 𝐾𝑁𝐻 + 𝑆𝑁𝐻 𝐵,𝐻 𝑆𝑆 𝐾𝑂,𝐻 𝑆𝑁𝑂 𝑆𝑁𝐻 μH η 𝑋 𝐾𝑆 + 𝑆𝑆 𝐾𝑂,𝐻 + 𝑆𝑂 𝐾𝑁𝑂,𝐻 + 𝑆𝑁𝑂 𝐾𝑁𝐻 + 𝑆𝑁𝐻 𝑔 𝐵,𝐻 𝑆𝑀 𝐾𝑂,𝑀 𝑆𝑁𝑂 𝑆𝑁𝐻 μM 𝑋 𝐾𝑀,𝑀 + 𝑆𝑀 𝐾𝑂,𝑀 + 𝑆𝑂 𝐾𝑁𝑂,𝑀 + 𝑆𝑁𝑂 𝐾𝑁𝐻 + 𝑆𝑁𝐻 𝐵,𝑀 𝑆𝑂 𝑆𝑁𝐻 μA 𝑋 𝐾𝑂,𝐴 + 𝑆𝑂 𝐾𝑁𝐻,𝐴 + 𝑆𝑁𝐻 𝐵,𝐴 𝑆𝑂 𝑆𝑁𝑂 𝐾𝑂,𝐻 bH � + ηℎ �𝑋 𝐾𝑂,𝐻 + 𝑆𝑂 𝐾𝑁𝑂,𝐻 + 𝑆𝑁𝑂 𝐾𝑂,𝐻 + 𝑆𝑂 𝐵,𝐻 𝑆𝑂 𝑆𝑁𝑂 𝐾𝑂,𝑀 bM � + ηℎ �𝑋 𝐾𝑂,𝑀 + 𝑆𝑂 𝐾𝑁𝑂,𝑀 + 𝑆𝑁𝑂 𝐾𝑂,𝑀 + 𝑆𝑂 𝐵,𝑀 𝑆𝑂 𝑆𝑁𝑂 𝐾𝑂,𝐴 bA � + ηℎ �𝑋 𝐾𝑂,𝐴 + 𝑆𝑂 𝐾𝑁𝑂,𝐴 + 𝑆𝑁𝑂 𝐾𝑂,𝐴 + 𝑆𝑂 𝐵,𝐴
Aerobic growth of autotrophs
6
Decay of heterotrophs
7
Decay of Methylotrophs
8
Decay of autotrophs
9
Ammonification of soluble organic Nitrogen
10
Hydrolysis of entrapped organics
11
Hydrolysis of entrapped organic nitrogen
k a 𝑆𝑁𝐷 𝑋𝐵𝑖𝑜 , kH
� 𝑋𝐵𝑖𝑜 = 𝑋𝐵,𝐻 + 𝑋𝐵,𝑀 + 𝑋𝐵,𝐴 �
𝑋𝑆 ⁄ 𝑋𝐵𝑖𝑜 𝑆𝑂 𝑆𝑁𝑂 𝐾𝑂,𝐻 � + ηℎ �𝑋 𝐾𝑋 + 𝑋𝑆 ⁄ 𝑋𝐵𝑖𝑜 𝐾𝑂,𝐻 + 𝑆𝑂 𝐾𝑁𝑂,𝐻 + 𝑆𝑁𝑂 𝐾𝑂,𝐻 + 𝑆𝑂 𝐵𝑖𝑜
𝑋𝑆 R 𝑋𝐵𝑖𝑜 10
Table 1- Continued: constituents stoichiometeries J 1 2 3 4
SS −1 𝑌𝐻 −1 𝑌𝐻
5
XS
−1 𝑌𝐻,𝑀
9 1
X B,A
XP
1
−1 𝑌𝑀
SO 1 − 𝑌𝐻 − 𝑌𝐻 1 − 𝑌𝐻,𝑀 − 𝑌𝐻,𝑀
1 1
1- 𝑓𝑝
8
X B,M
1
1- 𝑓𝑝
7
X B,H 1
1- 𝑓𝑝
6
10
SM
-1 -1 -1
𝑓𝑝 𝑓𝑝
4.57 − 𝑌𝐴 − 𝑌𝐴
S NO
S NH
S ND
X ND
− 𝑖𝑋𝐵 1 − 𝑌𝐻 2.86 𝑌𝐻 1 − 𝑌𝑀 − 2.86 𝑌𝑀 1 𝑌𝐴 −
𝑓𝑝
− 𝑖𝑋𝐵 − 𝑖𝑋𝐵 − 𝑖𝑋𝐵
-𝑖𝑋𝐵 -
1
𝑌𝐴
(1- 𝑓𝑝 ) 𝑖𝑋𝐵 (1- 𝑓𝑝 ) 𝑖𝑋𝐵
1
-1
(1- 𝑓𝑝 ) 𝑖𝑋𝐵
1
-1
-1
11
Copyright ©2014 Water Environment Federation 4560
WEFTEC 2014
Results and Discussion BIOEST, uses Metropolise-Hastings (MH) algorithm to draw samples from the posterior distribution (Sharifi et al. 2014). The prior distributions of the parameters are assumed to follow a log-normal distribution as suggested by Cox, (2004) with the 95% brackets being the lower and upper ranges provided in table 2. The given range for parameters obtained from various sources (Hauduc et al. 2011, Dold et al. 2008, Cox 2004, Henze et al. 2000). Table 2- Bayesian inverse modeling summary results of M-ASM1 Kinetic and stoichiometric parameters Given Range
Parameter
Heterotrophs maximum growth rate, µ H (d-1)
Substrate half saturation for Heterotrophs, K S (g COD.m-3) Methanol half saturation for Heterotrophs, K M,H (g O 2 half saturation for Heterotrophs, K O,H (g
COD.m-3)
O 2 .m-3)
NOx half saturation for Heterotrophs, K NO,H (g
N.m-3)
Anoxic growth reduction for Heterotrophs, η g (-)
Aerobic decay rate coefficient for Heterotrophs, b H (d-1)
Low
High
1
10
2
0.234
0.1
0.5
0.27
0.065
0.01
0.5
0.02 0.4 0.4
0.1 0.8 1
0.1
Methanol half saturation coefficient, K M,M (g COD.m-3)
0.01
0.1
N.m-3)
0.01
0.1
O 2 half saturation for Methylotrophs, K O,M (g
NOx half saturation for Methylotrophs, K NO,M (g
Aerobic decay rate coefficient for Methylotrophs, Maximum specific growth rate of Autotrophs,
b M (d-1)
µ A (d-1)
Ammonia half saturation for Autotrophs, K NH,A (g N.m-3)
0.8
0.01 0.04 0.7 0.5
3
0.1 0.1 1.2 2
NOx half saturation for Autotrophs, K NO,A (g N.m-3)
0.01
0.2
Aerobic decay rate coefficient for Autotrophs, b A (d-1)
0.15
0.25
1
3
Oxygen half saturation for Autotrophs, K O,A (g O 2 .m-3) Anoxic growth reduction for decay, Hydrolysis rate coefficient,
K h (d-1)
η h (d-1)
0.1 0.3
0.4 0.7
Hydrolysis half saturation coefficient, KX (-)
0.01
0.1
Aerobic yield of Heterotrophs on substrate, Y H (-)
0.5
0.75
0.15
0.3
Ammonification rate coefficient,
K a (d-1)
Aerobic yield of Heterotrophs on methanol, Y HM (-) Autotroph yield, Y A (-)
Methylotroph yield, Y M (-)
Endogenous fraction (death-regeneration), f P (-) Nitrogen fraction in biomass, i XB (g N.g
COD-1)
σ
2.40
0.01
O 2 .m-3)
μ
10
NHx half saturation for Heterotroh/Methylotroph, K NH (g N.m-3) Maximum specific growth rate of Methylotroph, µ M (d-1)
Posterior Distribution
0.01 0.3 0.3
0.08 0.05
Copyright ©2014 Water Environment Federation 4561
0.1 0.5 0.5
0.08 0.1
7.53 0.06 0.31 0.52 0.84 0.04 2.59 0.01 0.04 0.05 0.07 1.07 0.74 0.11 0.18 0.18 0.55 1.85 0.05 0.03 0.61 0.39 0.22 0.43 -
0.06
1.114 0.013 0.080 0.048 0.070 0.012 0.223 0.002 0.013 0.017 0.008 0.061 0.104 0.032 0.039 0.012 0.061 0.288 0.014 0.011 0.028 0.031 0.025 0.020 -
0.006
WEFTEC 2014
Multiple MCMC chains can be generated using the algorithm that helps exploring the plausible parameters spaces more effectively. For faster convergence of MH algorithm and decreasing the burn-in period samples, the deterministic result obtained from the hybrid Genetic algorithm (with 200 generations and a population of 100 in each generation) was used as the starting parameter set for the chains. Ten chains are considered each with the length of 150,000 samples. After MCMC termination, 10% of the samples were discarded as burn-in period from each chain. The mean (µ) and standard deviation (σ) of each parameter are reported in the Table 2. This 95% interval range of each parameter distribution can be used as an updated parameter range for the nitrification-denitrification system at Blue Plains. a)
b) sCOD
VSS
80
3200 Observed 2.5% Parameter Uncertainty 97.5% Parameter Uncertainty 2.5% Model Structural Error 97.5% Model Structural Error
60
2800
mg / L
mg N / L
2400 40
2000 1600
20
1200 0 3/3/10
4/12/10
5/22/10
800 3/3/10
7/1/10
Date
4/12/10
5/22/10
7/1/10
Date
c) NOx x 15
mg N / L
10
5
0 4/22/10
5/2/10
5/12/10
5/22/10
6/1/10
6/11/10
6/21/10
7/1/10
Date
Fig. 3. 95% credible intervals of a) soluble COD , b) VSS, and, c) NOx in the effluent representing uncertainty associated with parameters (solid line) and model structural error (dashed line). Dots are showing observed measured data.
Copyright ©2014 Water Environment Federation 4562
WEFTEC 2014
A Monte Carlo simulation approach is the utilized to take a random samples from the MCMC results. Overall, 5000 samples were taken from the MCMC results. Sampling from the posterior distribution preserves the correlation between the parameters in the sampled subset. The 95% credible intervals (from 2.5% to 97.5%) in the effluent for sCOD, VSS and NO x are obtained in each time and are shown in Figure 3. In Figure 3 the solid lines represent the 95% range of predictions as a result of solely model parameter uncertainty while the dashed lines is the 95% range representing the possible range of observed constituent concentrations when model structural and measurement errors are also considered. Scenario Analysis As an application of Bayesian parameter estimation, a scenario analysis was performed to evaluate the efficacy of an improved dosage scheme of methanol loading. The joint distribution of the parameters obtained from the parameter estimation was used to do this based on the frequency of nitrogen effluent concentration (in the form of nitrite-nitrate, NOx) violation from some presumed threshold values. Two threshold concentrations of 5 and 6 mg N/L in the effluent are considered. The operation scheme scenarios include: Scenario 1 (baseline operation): Methanol loading is determined to be proportional to the inflow rate. This scheme is currently being used at the Blue Plains WWTP. In this scenario the ratio of methanol loading to inflow rate is constant and equal to 45L/(1000 m3 inflow). The daily methanol loading rate based on scenario number 1 is shown by red line in the Figure 4.
8000
Methanol Loading (kg/day)
Scenario 1: 1: Flow-Based Flow-Based Scenario 2: 2: TN-Based TN-Based
7000
6000
5000
4000
3000
2000 1/22/10
3/3/10
4/12/10
5/22/10
Scenario 2: Methanol loading is considered to be proportional to the total nitrogen to be removed. In this scenario the methanol loading is considered proportional to the total nitrogen entering the nitrification-denitrification system, (i.e. TN= NH3+NOx+Snd+Xnd) minus 6.1 mg/L. The subtraction of 6.1 mg/L is used because it is assumed that the NOx concentration in the effluent can be between 56 mg N/L and the value is obtained such that the amount of total methanol during the simulation (150 days) is equal to the baseline scenario. Based on stoichiometery of methylotrphic reaction presented in the Table 1 we have:
7/1/10
Date
Fig. 4. Methanol loading into the denitrification stages in two scenarios: 1) based on input flow (red line) and, 2) based on input nitrogen (TN-6.1 mg N/L) (blue line).
𝑀𝑒𝑡ℎ𝑦𝑙𝑜𝑡𝑟𝑜𝑝ℎ𝑠 1 − 𝑌𝑀 � 𝑁𝑂−3 + … �⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯� 𝑌𝑀 𝑋𝐵,𝑀 2.85 +⋯
𝐶𝐻3 𝑂𝐻 + �
Copyright ©2014 Water Environment Federation 4563
WEFTEC 2014
If Y m is assumed to have a value of 0.4 (Table 2) then the ratio of methanol needed to remove NO 3 is: 4.76 mg COD methanol/ mg N. By knowing the total amount of nitrogen (TN) in the influent, and assuming that all the TN is converted to the form of nitrite-nitrate in the nitrification stage, the methanol loading rate can be obtained based on this ratio (4.76 mg COD methanol / mg TN). The calculated daily methanol loading based on this scenario is shown in the Figure 4 by the blue curve. a)
b) 1
1
Relative Frequency
0.6
0.4
0.6
0.4
0.2
0.2
0
0 0
0.1
0.2
0.3
0.4
0
0.5
c)
0.1
0.2
0.3
0.4
0.5
Effluent NOx Violation (Fraction of time)
Effluent NOx Violation (Fraction of time)
d) 1
1 1
NOx Violation > 5 mg/L Scenario 2
0.6
0.4
NOx Violation > 6 mg/L Scenario 2
0.8
Relative Frequency
0.8
Relative Frequency
NOx Violation > 6 mg/L Scenario 1
0.8
Relative Frequency
NOx Violation > 5 mg/L Scenario 1
0.8
0.6
0.4
0.2
0.2
0
0 0
0.1
0.2
0.3
0.4
0.5
Effluent NOx Violation (Fraction of time)
0
0.1
0.2
0.3
0.4
0.5
Effluent NOx Violation (Fraction of time)
Fig. 5. NOx violation in the effluent: a) methanol loading with scenario 1, and effluent limit of 5 mg/L, b) methanol loading with scenario 1, and effluent limitation of 6 mg/L, c) methanol loading with scenario 2, and effluent limit of 5 mg/L, and, d) methanol loading with scenario 2, and effluent limit of 6 mg/L. (The bin on the left side of zero on the x axis represent zero incidents of threshold violation).
Copyright ©2014 Water Environment Federation 4564
WEFTEC 2014
Comparing two scenarios shows that the total used methanol during the simulation period (150 days) in the both scenarios are equal. 5000 realizations of effluent total nitrogen is generated through random sampling of the parameters from the posterior distributions obtained from the Bayesian parameter estimation. For each realization the fraction of time that the concentration exceeds the assumed threshold of 5 mg/L and 6mg/L is calculated both for the baseline scenario as well the TN-based methanol loading scenario. The histograms fraction of violation frequency for each scenario for an assumed effluent concentration threshold of 5mg/L and 6mg/L is provided in Fig. 5. As it is illustrated in Figure 5, based on scenario 1, all realizations violate the 5 mg N/L at least once (Fig. 5a) and on average roughly 20% of the time the concentrations are above the threshold, and on average violate the 6 mg/L threshold 5% of the time while using the TN-based approach significantly reduces the frequency of violation. In the TN-based approach the maximum possible violation of the 5 mg/L threshold is around 5% and for the 6 mg/L limit is much less than 5% with the majority of the realization resulting in no period of violation. This shows that the determining the methanol loading rate based on influent flow rate without taking into account the concentration of nitrogen components in the inflow is less effective in terms of reducing the violation frequency in the effluent. On the other hand, by a simple change in the methanol loading strategy based on total nitrogen load in the influent which can be done using a probe and feedback system, the quality of effluent is significantly improved without any increase in total methanol loading compared to the baseline scenario. As it is shown in the Figure 5c and 5d, the violation of NOx respectively from 5 and 6 mg N/L in the effluent is decreased substantially. The scenario analysis performed in this study is simple. A formal optimization can be done by using an optimization algorithm to discover better operational schemes. It should be also noted that the uncertainty in the ASM’s output is not limited to just parameter uncertainty. Municipal wastewater stream inherently has regular and non-regular fluctuation affecting the effluent characteristic and the future temporal fluctuations are not necessarily similar to the past flow patterns. Furthermore, the simplifying assumptions and the idealization of the mathematical model itself can be considered as a source of uncertainty which is partly captured in the parameter uncertainty but is not explicitly considered.
Conclusion ASM parameter uncertainties was quantified by applying the previously developed Bayesian parameter estimation framework, BIOEST described in detail by Sharifi et al. (2014). The resulting probability distribution of parameters can then be used along with a Monte Carlo scheme to quantify the uncertainty of model predictions. This joint probability distribution of ASM parameters was then used to evaluate two different schemes for methanol addition to the nitrification-denitrification process at the Blue Plains wastewater treatment plant. In the first scenario which is based on the practice currently in use, the methanol loading is determined based on the flow rate. In the proposed scenario, it is assumed that the methanol loading is determined as a function of total nitrogen entering the reactor. The efficiency of nitrogen removal was assessed based on the total duration of time when the effluent NOx (nitrite+nitrate) concentration exceeds certain thresholds. Results show that determining the methanol addition based on total nitrogen can improve the efficiency or reduce methanol consumption substantially.
Copyright ©2014 Water Environment Federation 4565
WEFTEC 2014
References Albrecht, J. (2013) Estimating reaction model parameter uncertainty with Markov Chain Monte Carlo. Computers & Chemical Engineering 48(0), 14-28. Benedetti, L., De Baets, B., Nopens, I. and Vanrolleghem, P.A. (2010) Multi-criteria analysis of wastewater treatment plant design and control scenarios under uncertainty. Environmental Modelling & Software 25(5), 616-621. Bixio, D., Parmentier, G., Rousseau, D., Verdonck, F., Meirlaen, J., Vanrolleghem, P.A. and Thoeye, C. (2002) A quantitative risk analysis tool for design/simulation of wastewater treatment plants. Water Sci Technol 46(4-5), 301-307. Bullock, C.M., Bicho, P.A., Zhang, Y. and Saddler, J.N. (1996) A solid chemical oxygen demand (COD) method for determining biomass in waste waters. Water Research 30(5), 12801284. Busch, J., Elixmann, D., Kühl, P., Gerkens, C., Schlöder, J.P., Bock, H.G. and Marquardt, W. (2013) State estimation for large-scale wastewater treatment plants. Water Research 47(13), 4774-4787. Cox, C.D. (2004) Statistical distributions of uncertainty and variability in activated sludge model parameters. Water Environ Res 76(7), 2672-2685. Dold, P., Takacs, I., Mokhayeri, Y., Nichols, A., Hinojosa, J., Riffat, R., Bott, C., Bailey, W. and Murthy, S. (2008) Denitrification with carbon addition--kinetic considerations. Water Environ Res 80(5), 417-427. Flores-Alsina, X., Rodríguez-Roda, I., Sin, G. and Gernaey, K.V. (2008) Multi-criteria evaluation of wastewater treatment plant control strategies under uncertainty. Water Research 42(17), 4485-4497. Gamerman, D. and Lopes, H.F. (2006) Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition, Taylor & Francis. Han, H.-G., Qian, H.-H. and Qiao, J.-F. (2014) Nonlinear multiobjective model-predictive control scheme for wastewater treatment process. Journal of Process Control 24(3), 47-59. Hauduc, H., Rieger, L., Ohtsuki, T., Shaw, A., Takacs, I., Winkler, S., Heduit, A., Vanrolleghem, P.A. and Gillot, S. (2011) Activated sludge modelling: development and potential use of a practical applications database. Water Sci Technol 63(10), 2164-2182. Henze, M., Gujer, W., Mino, T. and van Loosdrecht, M. (2000) Activated sludge models ASM1, ASM2, ASM2d and ASM3, IWA Publishing London. Henze, M., Leslie Grady Jr, C.P., Gujer, W., Marais, G.V.R., Matsuo, T., Henze, M., Leslie Grady Jr, C.P., Gujer, W., Marais, G.V.R. and Matsuo, T. (1987) A general model for singlesludge wastewater treatment systems. Water Research 21(5), 505-515.
Copyright ©2014 Water Environment Federation 4566
WEFTEC 2014
Kaipio, J. and Somersale, E. (2004) Statistical and Computational Inverse Problems (Applied Mathematical Sciences) (v. 160), Springer. Keskitalo, J. and Leiviskä, K. (2012) Application of evolutionary optimisers in data-based calibration of Activated Sludge Models. Expert Systems with Applications 39(7), 6609-6617. Mannina, G., Cosenza, A. and Viviani, G. (2012) Uncertainty assessment of a model for biological nitrogen and phosphorus removal: Application to a large wastewater treatment plant. Physics and Chemistry of the Earth, Parts A/B/C 42–44(0), 61-69. Sharifi, S., Murthy, S., Takács, I. and Massoudieh, A. (2014) Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo. Water Research 50(0), 254-266. Sin, G., Gernaey, K.V., Neumann, M.B., van Loosdrecht, M.C.M. and Gujer, W. (2009) Uncertainty analysis in WWTP model applications: A critical discussion using an example from design. Water Research 43(11), 2894-2906. Sin, G., Gernaey, K.V., Neumann, M.B., van Loosdrecht, M.C.M. and Gujer, W. (2011) Global sensitivity analysis in wastewater treatment plant model applications: Prioritizing sources of uncertainty. Water Research 45(2), 639-651. Yang, T., Qiu, W., Ma, Y., Chadli, M. and Zhang, L. (2014) Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes. Neurocomputing 136(0), 88-95.
Copyright ©2014 Water Environment Federation 4567
