International Journal of Environment Science and Technology Center for Environment and Energy Research and Studies (CEERS) ISSN: 1735-1472 EISSN: 1735-2630 Vol. 6, Num. 1, 2009, pp. 69-76

International Journal of Enviornmental Science and Technology, Vol. 6, No. 1, Winter, 2009, pp. 69-76

Environmental biological model based on optimization of activated sludge process

¹ *X. X. Zhang; ¹D. Y. Zhao; ¹Z. X. Wang; ¹B. Wu; ^{1, 2} W. X. Li; ¹ S. P. Cheng

¹ State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210093, China
² Nanjing Institute of Environmental Sciences, State Environmental Protection Administration of China, Nanjing, 210042, China
*Corresponding Author Email: zhangxx@nju.edu.cn Tel.: +8625 8359 5995; Fax: +8625 8359 5995

Received 27 May 2008; revised 14 August 2008; accepted 25 November 2008

Code Number: st09007

ABSTRACT

A simplified environmental biological model has been developed based on biodegradation kinetics correlation to regulate and optimize wastewater treatment system of activated sludge process. All parameters included in the model are calibrated in accordance with reference data and experimental results and good agreements are achieved between calculated results and reference data or experimental results. The minimum bioreactor volume is used as objective function in the model and errors between optimal minimum volume of the model and each reported result of three references are found to be no more than 8.63 % after validation. Comparisons between optimal results and experimental data demonstrate that the deviations are negligible. The optimal minimum volume is 9.21 m³ with the error of 6.40 % to the practical bioreactor volume of a pilot treatment system. The environmental biological model has been applied to economically evaluate a former treatment system with native bacterium YZ1 and four operation periods of the pilot system with functional strain Fhhh compared with YZ1, Fhhh possesses higher biodegradation ability in purified terephthalic acid wastewater and a broader economic potential in the field of wastewater treatment.

Keywords: Cost evaluation, functional strain, mathematical model, wastewater treatment, biodegradation kinetics

INTRODUCTION

With rapid development of informatics technology, many advanced informatics methods have been introduced to the field of environmental pollution control (Cheng et al., 2002; Glover et al., 2006). In order to solve environmental problems more effectively, researchers are focusing on design and optimization of the control processes for environmental pollution through imitation and simulation of these problems (Zeidan et al., 2003; Templeton et al., 2006; Li et al., 2008; Rashidinejad et al., 2008). Development of informatics techniques, including mathematical modeling (Panjeshahi and Ataei, 2008), artificial neural network (Choi and Park 2001), fuzzy control (Chen and Chang, 2008), database construction (Okubo et al., 1994) and expert system (Ribas et al., 2008) has been one of the most exciting progresses in wastewater treatment technology in recent years.

International wastewater association (IWA) Task Group proposed activated sludge model No. 1 (ASM1) in 1987, activated sludge model No. 2 (ASM2) in 1994 and activated sludge model No. 3 (ASM3) in 1999 (IWA Task Group, 2000) which has made major contributions to promote numerical analyses in wastewater treatment. The methods of the parameter estimation and calibration procedures have been presented for the ASMs (Von Sperling, 1994; Reichert et al., 1995; Mino et al., 1997). Optimal operating modes for biofilm process and sequencing batch reactors were also determined via model based optimization (Suzuki et al., 1999; Souza et al., 2008). Some models have been developed for optimization of removing nitrogen or other toxic chemicals in activated sludge reactors (Oda et al., 2006; Hu et al., 2007; Rivas et al., 2008).

In those models, main attention was paid to seek reasonable models to simulate activated sludge process by mathematical or informatics techniques, but microbial effects were neglected (Gujer, 2006). Lack of recognition of microbial crucial roles in bioreactors makes the models less reliable, even when heterogenous or functional microorganisms are introduced into bioreactors to improve operation efficiency. However, the problem can be solved with microbial degradation kinetics which deals with the relationship between microbial growth rate and pollutant degradation rate, as well as the effects of environmental factors on the microbial growth and pollutant removal (Hosseini et al., 2007; Mohanan et al., 2007).

Different from the above methods, in this paper an environmental biological model (EBM) is developed with the special attention paid to biodegradation kinetics parameters which can reflect the biodegradation and adaptation abilities of various microorganisms in different wastewaters. The aims of this work are:

1. Regulate and optimize wastewater treatment system of activated sludge process with the simplified model based on biodegradation kinetics correlation;
2. Comparatively assess the treatment system of purified terephthalic acid (PTA) wastewater with functional strains cultivated in the bioreactor which was operated in Nanjing Yangtze wastewater treatment plant (Nanjing, China) from 2002 to 2004.

MATERIALS AND METHODS

Mathematical equations and parameters

EBM optimization and evaluation function is achieved based on activated sludge process. The flow sketch and related parameters are shown in Fig. 1. The validity of mathematical equations depends on its reasonability.

EBM is developed based on biodegradation kinetics and mass balance theory and the methods are described in detail in Table 1. Total twenty eight mathematical equations (divided into four groups) are employed to develop EBM and the equations are set up with the following suppositions:

1. EBM is proposed with V_min as objective function and V_min errors between EBM computed results and reference or experimental data are considered as the main evaluation factors for the model validity;
2. The mathematical equations of wastewater treatment process and the relationship between objective function and process parameters are achieved with Q_r, S_e and X_e as recycled variables according to mass balance theory and Monod equations (Qin, 1989);
3. It is thought that there is no effective biomass in the bioreactor influent wastewater based on the facts of high toxicity induced by PTA wastewater and very few microorganisms living in the wastewater (Zhang et al., 2005).
4. According to reported data from references, some coefficients of the mathematical equations such as ?₁, ?₂, γ₁ and γ₂ have been determined for cost evaluation (Middleton and Lawrence, 1974; Gu, 1993).

Forty eight variables used in EBM equations are divided into eight groups:

1. Optimization computation objective function V_min;
2. Degradation kinetics parameters, including q_max,μ_max, K_sq, K_sμ, K_d and Y_t;
3. Natural parameters of water quality, including Q_o, S_o and X_o;
4. Wastewater treatment process parameters, including HRT, M_t, Q_e, q, Q_s, SRT, SVI, V, X, X_r, X_s, Y_obs and μ;
5. Recycled variables, including Q_r, S_e and X_e;
6. Control parameters of water quality, including S_ei and X_ei;
7. Conventional experienced coefficient, including C_sm(20), E_A, K_La₍₂₀₎,α, β, λ₁ , λ₂, γ₁, γ₂ and
8. Cost evaluation parameters, including A_T, C, E_e, E_f , G_s, R, T_d, T_s, V_d, V_s and ZSV.

Operation strategy and programming

EBM optimization computation is achieved through recycle operation of computer. The model has been programmed with Visual Basic 7.0 language (Microsoft Co., USA). EBM operation strategies are described as following:

1. After input of the values of ten parameters (q_max, K_sq, μ_max,K_sμ, Y_t, SVI, Q_o, S_o, S_ei and X_ei) into EBM, corresponding calculation of μ, q, q_c, K_d and Y_obs will be carried out with S_e as recycled variable;
2. The produced data arrays must be verified with Eq. 20 on account of microbial respiration and death which are inevitable during the growth. With S_e, X_e and Q_o as recycled variables, the eligible data arrays can be used to calculate the matrix including, X, X_s, X_r, θ, V, M_t, Q_o and Q_e;
3. The results in the matrix must be screened with Eq. 21 for mass balance verification. If no data arrays in the matrix can satisfy the condition, it demonstrates that no optimal result is produced by EBM in the wastewater treatment system;
4. However, in the case of more than one eligible data array , the array with the minimum volume of bioreactor can be assigned to be the optimal result (V_min). The corresponding recycled variables (S_e, X_e and Q_o) are also obtained in the optimal data array. Original and final values and steps of the three variables are shown in Table 2;
5. Subsequently, equations in group (C) can be employed to calculate related parameters of settlers based on the values of Q_o, X, X_r and SVI in the optimal data array and those in group (D) are used for determination of R and G_s based on V_min value;
6. After the values G_s, A_T and V_min are obtained, calculation for E_f and E_e can be performed with equations in group (E) to evaluate the wastewater treatment systems economically.

RESULTS AND DISCUSSION

Calibration with reference data

Comparisons between EBM optimal results and the reference data have been performed. The reference data of twenty two operational parameters was obtained from different wastewater treatment systems with activated sludge process (Lee and Lin, 1999; Qin, 1989; Woodard, 2001). Ten reference data (q_max, K_sq, μ_max, K_sμ, Y_t, SVI, Q_o, S_o, S_ei and X_ei) have been input into EBM on the interface (Table 3) and the optimal values of other sixteen parameters (S_e, X_e, X, X_s, X_r, HRT, V_min, M_t, Q_r, Q_s, Q_e, μ, q, SRT, K_d and Y_obs) are produced on output interface of the software (Table 3). After comparisons between EBM optimal results and reference data, the differences of most parameters, including V_min, HRT, SRT and Q_r between the two groups are not significant and can be negligible. The V_min errors between the EBM optimal results and the reported data from the three references are 4.44 %, 8.63 % and 5.93 %, respectively. It is found that EBM exhibits a high validity when it is used for optimization computation of wastewater treatment system.

Pollutant removal efficiency of activated sludge process depends on wastewater components (various pollutants and different concentrations) and species or characteristics of microorganisms in bioreactor (Gujer et al., 1999). The different wastewater conditions and structural variations of microbial communities make the kinetic parameters (q_max, K_sq, μ_max, K_sμ, Y_t and K_d) vary greatly in the three examples (Table 3). EBM is a mathematical model developed according to the principle that operational parameters must be adjusted with the changes of wastewater characteristics and biodegradation kinetic parameters which makes a great contribution to EBM validity.

Calibration with experimental results

To calibrate the EBM with experimental results, PTA wastewater was subject to biodegradation kinetics test to quantify the six parameters, including q_max, K_sq, μ_max, K_sμ, Y_t and K_d. Degradation kinetic experiments were carried out according to Cheng et al. (2003). PTA wastewater was treated with activated sludge process (Fig. 1). Functional strain Fhhh constructed through protoplast fusion with the three parental strain of Phanerochaete chrysosporium, Saccharomyces cerevisiae and native bacterium Bacillus YZ1 at laboratory, was cultivated in bioreactor at Nanjing Yangtze wastewater treatment plant (Nanjing, China) to improve the removal efficiency of pollutants in PTA wastewater (Hao et al., 2003; Sun et al., 2005; Zhang et al., 2006a). The treatment system with bioreactor effective volume of 9.84 m³ was operated continuously and stably for more than 180 days in the plant. According to regulation strategies (Table 4), the treatment system was regulated and optimized in terms of process parameters, metallic ions and nutrition factors and also the whole operation time was divided into four periods. The experimental results of biodegradation kinetic parameters and operational parameters in the pilot treatment system have been shown in Table 5. EBM optimal results are achieved after the input of the 10 parameters (q_max, K_sq, μ_max, K_sμ, Y_t, SVI, Q_o, S_o, S_ei and X_ei) into the model (Table 5). After comparisons between the two groups, it is found that among the 16 pairs of parameters, the errors of 11 pairs are less than 10 %, including μ, q, HRT, SRT, Y_obs, X, X_s, Q_r, S_e, Q_e and V_minand those of 4 pairs range from 10 % to 17 %, including K_d, M_t, Q_s and S_e. Only one parameter error (X_e) is more than 17 % which probably results from the EBM hypothesis that there is no biomass in the bioreactor influent. EBM optimal V_min is obtained to be 9.21 m³ and V_min error between optimization value and experimental result is only 6.40 %. Dependent on the waste amounts and microbial characteristics, the bioreactor volume is the most crucial parameters for the design of wastewater treatment system which exerts a great influence on other process parameters, including hydraulic retention time, recycled sludge rate and air supplies (Gu, 1993; Rivas et al., 2008). A good V_min agreement results in small errors of other variables between calculated results and experimental results. Therefore, EBM can be used to forecast operational state of activated sludge process accurately and to regulate and optimize the treatment system successfully.

Cost evaluation of wastewater treatment system

EBM has been applied to economically evaluate the former treatment system (with bacterium YZ1 cultivated in the bioreactor to treat PTA wastewater) and the four operation periods of pilot treatment system (with functional strain Fhhh introduced into the bioreactor to improve degradation efficiency). As shown in Table 6, when influent flow arrives at 10,000 m³/d, EBM optimal V_min, A_T, E_e and E_f in the pilot treatment system are achieved to be 6,529 m³, 553 m², 0.88 CNY/m³ and 6.19 million-CNY, respectively. However, the four parameters of former treatment system are obtained to be 86,383 m³, 3,401 m², 11.70 CNY/m³ and 46.4 million-CNY. EBM optimal V_min, A_T, E_e and E_f in the pilot treatment system are 7.56 %, 16.26 %, 7.52 % and 13.35 % in former treatment system and 19.36 %, 6.24 %, 19.26 % and 16.40 % in period (A), respectively in which the system has not been regulated by metallic ions and nutrition factors. The results demonstrate that Fhhh presents a more economical form than YZ1 in PTA wastewater treatment and metallic and nutritional factors play important roles in biodegradation of the pollutants by Fhhh (Zhang et al., 2006a). Biodegradabilities of pollutants by microorganisms vary greatly (Zhang et al., 2006b), Thus, the biodegradation kinetics parameters must be taken into account for both design and optimization of activated sludge process.

ACKNOWLEDGMENTS

This research was financially supported by International Foundation for Science (No. W/4215-1) and Nanjing University Innovative Foundation (2006071009). The authors also would like to thank Nanjing Yangtze wastewater treatment plant (Nanjing, China) for the support of the pilot experiment.

A_T , m² Area of secondary settler
BOD₅ Biochemical oxygen demands for 5 days
C, mg/L Oxygen concentration in wastewater
C_sm(20), mg/L Oxygen solubility in distilled water at 20 ^oC
E_A, 100 % Oxygen absorptivity
E_e, CNY/m³ Electricity costs of wastewater treatment
E_f, million-CNY Equipment costs of reactor and settler
G_s, m³/y Air supplies in bioreactor
HRT, d Hydraulic retention time
K_d, L/d Cell decay coefficient
K_La₍₂₀₎, 1/h Oxygen total transfer coefficient at 20 ^oC
K_sq, g/L Substrate concentration at one-half q_max
K_sμ, g/L Substrate concentration at one-half μ_max
M_t, kg/d Total mass of sludge production
q, L/d Specific degradation rate
q_max, L/d Maximum specific degradation rate
Q_e, m³/d Effluent flow
Q_o, m³/d Influent flow
Q_r, m³/d Return sludge flow
Q_s, m³/d Waste sludge flow
R, kg/h Oxygen absorbency in bioreactor
S, g/L BOD₅ concentration in bioreactor
S_e, g/L Effluent soluble BOD₅ concentration
S_ei, g/L EBM input data of S_e
S_o, g/L Influent BOD₅ concentration
SRT, d Sludge retention time
SS, g/L Suspended solids
SVI, mL/g Sludge volumetric index
t, h Microbial growth or degradation time
T_d, h Sludge deposition time
T_s, h Sludge settling time
V, m³ Bioreactor volume
V_d, m³ Deposition volume of settler
V_min, m³ Minimum volume of bioreactor
V_s, m³ Total volume of settler
X, g/L SS concentration in bioreactor
X_e, g/L Effluent suspend solids
X_ei, g/L EBM input data of X_e
X_o, g/L Influent suspend solids
X_r, g/L Return SS concentration
X_s, g/L Waste SS concentration
Y_obs Observed yield coefficient
Y_t Theoretical yield coefficient
ZSV, m/min Zone sedimentation velocity
αK_La ratio
βOxygen solubility ratio
λ₁ Coefficient of settler E_f
λ₂ Coefficient of bioreactor E_f
γ₁ Exponential coefficient of settler E_f
γ₁ Exponentialcoefficientof bioreactor E_f
μ, L/d Specific growth rate
μ_max, L/d Maximum specific growth rate

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