Dynamic Modeling the Anaerobic Reactor Startup Process - Industrial

Jul 7, 2010 - The startup of anaerobic reactors is a very important issue for their application in the treatment of wastewaters and solid wastes. Howe...
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Ind. Eng. Chem. Res. 2010, 49, 7193–7200

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Dynamic Modeling the Anaerobic Reactor Startup Process Bai-Hang Zhao,† Yang Mu,*,‡ Fang Dong,† Bing-Jie Ni,† Jin-Bao Zhao,† Guo-Ping Sheng,† Han-Qing Yu,* Yu-You Li,§ and Hideki Harada§ Department of Chemistry, UniVersity of Science & Technology of China, Hefei 230026, China, UniVersity of Queensland, AdVanced Water Management Centre, QLD 4072, Australia, Department of CiVil Engineering, Tohoku UniVersity, Sendai 980-8579, Japan

The startup of anaerobic reactors is a very important issue for their application in the treatment of wastewaters and solid wastes. However, very limited information is available about the quantitative and dynamic modeling of anaerobic reactor startup in the literature. In this work, by introducing a linear phenomenological equation into the IWA Anaerobic Digestion Model No. 1 (ADM1), we developed a model to describe the dynamic behavior of the startup of anaerobic reactors. The model was calibrated and verified using the experimental results of two anaerobic reactors: an upflow anaerobic sludge blanket (UASB) reactor fed with soybean processing wastewater and a continuously stirred tank reactor (CSTR) system with simulated municipal solid wastes as the substrate. The simulation results matched with the experimental data well, and the model could capture the variation trends of the components. This agreement demonstrates the rationality and validity of the established model for describing the startup of anaerobic systems. 1. Introduction During the past three decades, different anaerobic technologies have been applied to treat various types of wastewater, because of the lower costs of construction, installation, and operation; lower production of excess sludge; and production of energy gas of such systems compared to conventional activated sludge processes.1-4 Reactor startup is the most critical step in the operation of anaerobic systems,5 and inadequate startup can cause poor subsequent treatment and, thus, require expensive system maintenance and effluent post-treatment.6 In addition, restartup of anaerobic bioreactors is often encountered, because many types of wastewaters are seasonally unstable. However, the startup or restartup of an anaerobic reactor is cumbersome and can last for several months, because anaerobic bacteria, especially methanogenic bacteria, are slow-growing microorganisms and are sensitive to changes in operating conditions.1,7 The long startup period is the major restriction for the application of anaerobic systems.6 Much attention has been paid to the startup processes of different anaerobic reactors. For example, various operating strategies for both reactor startup and granulation enhancement have been proposed and evaluated for upflow anaerobic sludge blanket (UASB) reactors, and the organic loading rate is considered as the important parameter during the startup of UASB reactors.6,8-10 At low organic loading rates (e.g., 0.5 gCOD per liter per day, where COD is chemical oxygen demand), the startup of a UASB reactor could be enhanced by mixing through biogas recirculation.11 At high organic loading rates, volatile fatty acids (VFAs) can significantly accumulate in the reactor, especially during the initial startup stage. This will cause a severe pH drop in the reactor, if there is no pH control or insufficient buffering capacity, and could result in the failure of reactor startup.5 However, most of the previous studies in this area focused on only the process description and main * To whom correspondence should be addressed. Fax: +61 07 33654726 (Y.M.), +86 551 3601592 (H.-Q.Y.). E-mail: yangmu@ awmc.uq.edu.au (Y.M.), [email protected] (H.-Q.Y.). † University of Science & Technology of China. ‡ Advanced Water Management Centre. § Tohoku University.

operating parameters to govern the startup process, and very limited dynamics information is available in the literature for the startup of anaerobic reactors. The International Water Association (IWA) Anaerobic Digestion Model No. 1 (ADM1) has been used widely in modeling, further researching, and designing anaerobic digestion systems.12-14 In ADM1, a first-order kinetic equation is used to describe hydrolysis and microbial decay, and the Monod equation is adopted to interpret the uptake of soluble components and the production of gas. Three main components in ADM1, including kinetic parameters, equilibrium coefficients, and stoichiometric coefficients, are assumed to be constant during the simulation process.14-16 It has been found that the specific methanogenic activity (SMA), representing the metabolic activity of microorganisms in methane production systems, increases with time during the startup period.5,7,17 This means that the kinetic parameters might not remain constant during reactor startup, as these parameters are directly proportional to the metabolic activities of the microorganisms.18 As a result, ADM1 is not appropriate for directly describing the dynamic behavior of the startup of anaerobic reactors. To address this problem, linear nonequilibrium thermodynamics could be an appropriate option. Its core concept is the linear phenomenological relationship, described by the linear phenomenological equation (LPE).19 The LPE shows that a flux term and a driving force term for transport phenomena are linearly related under unsteady state.19 Originally, this equation was devised to describe transport phenomena such as electrical conductivity, thermal conductivity, and atomic diffusion.19,20 Now, it has also been successfully applied to describe chemical and biochemical processes under unsteady state. For example, the LPE has been used to establish a linear phenomenological relationship between the rate of microbial growth and its driving force during the aerobic granulation process, that is, the startup of an anaerobic reactor.21 Therefore, the main objective of this study was to develop a mathematical model to describe the dynamic behavior of the anaerobic reactor startup process. This model was formulated after integration of the LPE into ADM1 to explain the variation of the microbial activities in reactor startup. The model was

10.1021/ie1001857  2010 American Chemical Society Published on Web 07/07/2010

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evaluated using the experimental results of the startup of two anaerobic digestion systems: one UASB reactor at our laboratory and one continuously stirred tank reactor (CSTR) reported by Griffin et al.5 It is expected that this model is useful for better understanding biological reactors and the kinetic behavior in the startup process of anaerobic digestion systems.

Usually, the influent biomass concentrations of anaerobic reactors are neglected, which means that Xi,in in eq 4 can be set tz ero. Thus, eq 5 is used to calculate the mass balance of microorganisms in the anaerobic reactor Vliq

dXi ) -qoutXi,out + dt

2. Model Development In ADM1, the hydrolysis of organic particulates is described by a first-order equation, namely, dX/dt ) kX, whereas the uptake of soluble substances such as sugars and amino acids is described by a Monod-like equation, namely, dS/dt ) kS/Ks + SX. The hydrolysis rate coefficients or specific uptake rate coefficients, k, are fixed as constant in ADM1. However, these rate coefficients could be significantly affected by the microorganism activity in addition to environmental factors.18 During the anaerobic reactor startup process, the specific methanogenic activity (SMA) usually increases with time,6,22 indicating that the startup of an anaerobic reactor involves the increasing or progressive restoration of microbial activity. This implies that the kinetic parameters, including the hydrolysis rate coefficients and specific uptake rate coefficients k, could also increase with time during reactor startup. 2.1. LPE Equation. In this study, we assume that the increase of each rate coefficient and the driving force for this increase have a linear relationship, because this linear assumption has been universally recognized as the basis of nonequilibrium thermodynamics. Therefore, the variation of each rate coefficient can be described using the LPE as dk ) k'(keq - k) dt

(1)

where k is the hydrolysis rate coefficient or the specific uptake rate coefficient in a biochemical process at time t; keq is the hydrolysis rate coefficient or specific uptake rate coefficient at equilibrium; and k′ is the acceleration coefficient of the hydrolysis or uptake process, which means the restoring or increasing rate of bioactivity. Equation 1 can be rearranged and solved to give k ) (keq - ko)[1 - e-k'(t-to)] + ko

(2)

where to is the time of the lag phase and ko is the hydrolysis rate coefficient or specific uptake rate coefficient at the end of the lag phase. 2.2. Mass Balances. The mass balance equations for the liquid- and solid-phase components in anaerobic reactors are given by Vliq

Vliq

dSi ) qinSi,in - qoutSi,out + dt

j)1-15

dXi ) qinXi,in - qoutXi,out + dt

j)1-15

∑

FjVi,j

(3)

∑

FjVi,j

(4)

where Vliq is the liquid reactor volume; Si is the concentration of soluble component i in the reactor; Si,in and Si,out are the input and output concentrations, respectively, of soluble component i; Xi is the concentration of solid i in reactor; Xi,in is the input concentration of solid i; qin is the influent flow rate; qout is the effluent flow rate, and ∑j)1-15 FjVi,j is the sum over all processes j of the biochemical rate coefficient for component i (Vi,j) times the kinetic rate equation (Fj) for each process j.

∑

FjVi,j

(5)

j)1-15

The gas-phase component balance is described by the equations Vgas

dSgas,i ) -qgasSgas,j + VliqFT,i dt

(6)

FT,i ) kL,a(Sliq,i - AiKH,ipgas,i) qgas )

Patm

(

(7)

)

FT,CH4 FT,H2 RT + + FT,CO2 Vliq - Pgas,H2O 16 64

(8)

where Vgas is the volume beyond the liquid volume in the reactor; qgas is the volumetric flow rate of biogas; kL,a is volume-specific liquid-gas transfer coefficient; Sgas,i, FT,i, and KH,i are the concentration, transformation rate, and Henry’s law constant, respectively, of the ith biogas compound; Ai is the conversion factor between the units of COD (chemical oxygen demand) and moles for the ith biogas compound; Patm is the total gas pressure; pgas,i and pgas,H2O are the partial pressures of the ith biogas compound and water vapor, respectively; T is the operating temperature; and R is the gas constant. 2.3. Biodegradation Kinetics. Kinetic dependency describes the biotransformation of organic matter and the microbial growth. It is assumed that the hydrolysis of organic particulates produces soluble substances, such as sugars and amino acids, which are further fermented to generate propionate, butyrate, valerate, acetate, and hydrogen. Propionate, butyrate, and valerate are further degraded to acetate and hydrogen. Methane is produced through both the degradation of acetate and the reduction of carbon dioxide by hydrogen. The kinetic model describes the relationships among 3 species (substrate, products, and microorganisms), 15 bioprocesses, and 6 types of microorganisms. Except for CO2, the units for all species, such as solid substrate, various microorganisms, soluble substances, and gases, in the model are grams of COD per liter (gCOD/L), which is directly proportional to electron equivalents (8 g of O2 per electron equivalent).15 The units for CO2 are moles per liter (mol/L). Related stoichiometry and process kinetics for the reactor startup are presented as a matrix in Tables 1-3, in order to highlight the interactions among the model components and processes. 2.4. Model Simulation and Calibration. A computer program, AQUASIM 2.0,23 was used to model the biological processes and to estimate the coefficients and parameters involved in this model. 3. Materials and Methods 3.1. UASB Reactor, Seed Sludge, and Substrate. The experiments were conducted in a bench-scale Plexiglas UASB reactor that consisted of a 2.0-L reaction portion and a gas-solid separator portion. It was operated at 35 ( 1 °C with a heating jacket. The seed sludge was taken from an anaerobic digester in a wastewater treatment plant in Hefei, China. The pH and volatile

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a

Table 1. Stoichiometry for COD Conservation for Degradation Process in the Established Model j

process

XCA XPR Saa Ssu

hydrolysis of CA -1 hydrolysis of PR uptake of amino acid uptake of sugar uptake of valerate uptake of butyrate uptake of propionate uptake of acetate uptake of hydrogen

1 2 3 4 5 6 7 8 9

-1

Sva

Sbu

Spr

Sac

SH2

SIC

SIN

SCH4

1 1 -1 -1

fva,aa(1 - Yaa) fbu,aa(1 - Yaa) fpr,aa(1 - Yaa) fbu,su(1 - Ysu) fpr,su(1 - Ysu) -1 0.7(1 - YC4) -1 -1

fac,aa(1 - Yaa) fac,su(1 - Ysu) 0.3(1 - YC4) 0.8(1 - YC4) 0.57(1 - Ypr) -1

fH2,aa(1 - Yaa) -∑c(Ci)Vi,3 Naa - Yaac(Nbio) fH2,su(1 - Ysu) -∑c(Ci)Vi,4 -Ysuc(Nbio) 0.2(1 - YC4) 0.43(1 - Ypr) -1

-∑c(Ci)Vi,7 -∑c(Ci)Vi,8 -∑c(Ci)Vi,9

1 - Yac 1 - YH2

a aa, amino acids; ac, acetate; bu, butyrate; C4, valerate and butyrate; CA, carbohydrates; c(Ci), the concentration of carbon in substance i; c(Nbio), the concentration of nitrogen in microorganism; f, yield coefficient of product; IC, inorganic carbon; IN, inorganic nitrogen; Naa, nitrogen concentration in amino acids; PR, proteins; pr, propionate; S, soluble substance or gas concentration; su, sugar; va, valerate; X, solid substance concentration; Y, microorganism yield.

Table 2. Stoichiometry for COD Conservation for Decay Processes in the Established Model j 10 11 12 13 14 15

process decay decay decay decay decay decay

of of of of of of

Xaa Xsu X C4 Xpr Xac XH2

Xaa

Xsu

XC4

Xpr

Xac

XH2

Xi

-1

1 1 1 1 1 1

-1 -1 -1 -1 -1

suspended solids (VSS) content of the seed sludge were 7.10 and 40.1 g/L, respectively. Soybean processing wastewater, obtained from a local soybean processing plant, was fed into the reactor as substrate after being diluted three times. In this substrate, the concentrations of COD, suspended solids (SS), proteins, and carbohydrates were 8.98-10.25, 0.15-0.21, 2.44-2.76, and 5.61-5.78 g/L, respectively. In addition, this wastewater had sufficient nitrogen (approximately 0.21 g/L) for the growth of anaerobic microorganisms. 3.1. Experimental Procedure. After 6 months of stable operation with soybean processing wastewater as the substrate, the UASB reactor was suspended without feeding for 4 months and then was restarted. During the restartup process, the substrate concentration was kept around 10 gCOD/L. To maintain the reactor pH at 7, bicarbonate buffer was dosed into the reactor. Initially, the sodium bicarbonate dosage was 1.50 g/L, and subsequently, it was decreased to 0.50 g/L on day 10 and to 0 on day 15 by which point the SMA had recovered to 80% of its typical level. 3.2. Analysis. Samples were taken and analyzed daily from the reactor influent and effluent. The volume of biogas produced was measured daily using a gas meter by the water displacement method. The content of CH4 in biogas was analyzed with a gas chromatograph (Lunan, model SP-6800A) equipped with a thermal conductivity detector and a 1.5-m stainless steel column packed with 5-Å molecular sieve. The temperatures of the injector, detector, and column were kept at 100, 105, and 60 °C, respectively. Argon was used as the carrier gas at a flow rate of 30 mL/min. The concentrations of ethanol and volatile fatty acids (VFsA), including acetate, propionate, butyrate, i-butyrate, valerate, and caproate, in the effluent were determined with another gas chromatograph (Agilent, model 6890NT) equipped with a flame ionization detector and a 30 m × 0.25 mm ×0.25 µm fused-silica capillary column (DB-FFAP). The liquor samples were first centrifuged at 12000 rpm for 5 min and were then acidified with formic acid, filtered through a 0.2µm membrane, and finally measured for free acids. The temperatures of the injector and detector were 250 and 300 °C, respectively. The temperature of the oven was initially held at 70 °C for 3 min, then ramped at 20 °C/min for 5.5 min, and

finally held at 180 °C for 3 min. Nitrogen was used as the carrier gas with a flow rate of 2.6 mL/min. Carbohydrates were determined as glucose equivalents using the anthrone-sulfuric acid method,,24 and proteins were measured as bovine albumin equivalents using the Lowry method.25 Measurements of COD, pH, NH3-N, and VSS were performed according to the standard methods.26 4. Results and Discussion 4.1. UASB Startup Performance. As shown in Figure 1, during the initial UASB startup period, the substrate removal efficiency was low, with an accumulation of a high level of VFAs. On day 10, the total COD removal efficiency was only 28%, and 4.6 gCOD/L of total VFAs was present in the reactor. These conditions can be attributed to the weak microbial activity during the initial startup step. As the startup time increased, the substrate removal efficiency and the methane percentage in the gas increased, whereas the VFA accumulation simultaneously decreased. This indicates the progressive restoration of microbial activity and an increase in the removal capacity of the microorganisms. After about 35 days of operation, the UASB reactor had been successfully started up, as evidenced by a substrate removal efficiency of over 90%, stable gas production, a stable methane percentage, and no significant VFA accumulation in the reactor (Figure 1). 4.2. Sensitivity Analysis. Sensitivity analysis is a useful tool for assessing whether the values of theoretically identifiable parameters can be reliably obtained from experimental data.23,27 Compared to ADM1, the established model has a more complex dynamics, and its response might be influenced by the values of the new parameters. Prior to model calibration, a sensitivity analysis should be conducted to determine the most important parameters. In the sensitivity analysis, the behavior of the model was evaluated as a function of variations in input parameters, using a one-variable-at-a-time approach. The sensitivity coefficient represents the change in the output variable resulting from a change in the input variable. The sensitivity analysis was conducted using the curve profiles of output (methane production rate) with input variables (acceleration coefficients). Only one parameter value was changed each time, while the other parameters were kept constant, as shown in Table 4. All acceleration coefficients were selected to perform the sensitivity analysis. In the simulation process, model coefficients were changed one by one in an appropriate range, and then the sensitivities of the methane production to the changes of their acceleration coefficients were determined. The output results for methane production were almost unchanged with changes in the values of k′PR, k′CA, k′su, and

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Table 3. Kinetic Rate Equation for Each Process in the Established Model j

process

Fj [gCOD/(L day)]

1

hydrolysis of carbohydrate

{(khyd,CA,eq - khyd,CA,o)[1 - e-k'CA(t-to)] + khyd,CA,o}XCA

2

hydrolysis of protein

{(khyd,PR,eq - khyd,PR,o)[1 - e-k'PR(t-to)] + khyd,PR,o}XPR

3

uptake of amino acid

4

uptake of sugar

5

uptake of valerate

{(km,C4,eq - km,C4,o)[1 - e-k'C4(t-to)] + km,C4,o}

Sva X Ks,C4 + Sva C4

6

uptake of butyrate

{(km,C4,eq - km,C4,o)[1 - e-k'C4(t-to)] + km,C4,o}

Sbu X Ks,C4 + Sbu C4

7

uptake of propionate

{(km,pr,eq - km,pr,o)[1 - e-k'pr(t-to)] + km,pr,o}

Spr X Ks,pr + Spr pr

8

uptake of acetate

{(km,ac,eq - km,ac,o)[1 - e-k'ac(t-to)] + km,ac,o}

Sac X Ks,ac + Sac ac

9

uptake of hydrogen

10

decay of Xaa

kdecXaa

11

decay of Xsu

kdecXsu

12

decay of XC4

kdecXC4

13

decay of Xpr

kdecXpr

14

decay of Xac

kdecXac

15

decay of XH2

kdecXH2

{(km,aa,eq - km,aa,o)[1 - e-k'aa(t-to)] + km,aa,o}

{(km,su,eq - km,su,o)[1 - e-k'su(t-to)] + km,su,o}

Saa X Ks + Saa aa

Ssu X Ks,su + Ssu su

{(km,H2,eq - km,H2,o)[1 - e-k'H2(t-to)] + km,H2,o}

SH 2 Ks,H2 + SH2

XH2

k′C4 (data not shown). The effects of the other acceleration coefficients on methane production are shown in Figure 2. It was found that increasing k′aa by a factor of 5 resulted in a 10% increase in methane production (Figure 2A). Figure 2B shows that an 81-times change in k′pr resulted in a 6% fluctuation of methane production. Similarly, factor-of-4 increase in k′ac brought about a 22% fluctuation in methane production (Figure 2C), and factor-of-10 change in k′H2 induced a 17% variation in methane production (Figure 2D).

Figure 1. Simulation results and experimental data for the startup of the UASB reactor in this study (lines for simulation results, dots for experimental data).

These results suggest that the acceleration coefficients for acetate uptake was the most sensitive, with the sensitivities of the other parameters following the order k′H2 > k′aa > k′pr > (k′PR, k′CA, k′su, and k′C4). k′ac and k′H2 represent the rate of restoration of methanogenic bioactivity. This implies that increasing the methanogenic bioactivity is essential for a successful startup of an anaerobic reactor. This result is in agreement with previous reports in which a long startup period to obtain high and steady methane production is the major restriction on the application of anaerobic reactors.6

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Table 4. Kinetic Parameters Used for Model Calibration and Verification symbol

definition

units

value for UASB

value for CSTR

khyd,PR,eq khyd,CA,eq km,su,eq km,aa,eq km,C4,eq km,pr,eq km,ac,eq km,H2,eq Ks,su Ks,aa Ks,C4 Ks,pr Ks,ac Ks,H2 khyd,PR,o khyd,CA,o km,su,o km,aa,o km,C4,o km,pr,o km,ac,o km,H2,o k′PR k′CA k′su k′aa k′C4 k′pr k′ac k′H2 kdec,su kdec,aa kdec,C4 kdec,pr kdec,ac kdec,H2

hydrolysis rate constant of protein at equilibrium hydrolysis rate constant of carbohydrate at equilibrium maximum specific uptake rate of sugar at equilibrium maximum specific uptake rate of aminophenol at equilibrium maximum specific uptake rate of C4 at equilibrium maximum specific uptake rate of propionate at equilibrium maximum specific uptake rate of acetate at equilibrium maximum specific uptake rate of H2 at equilibrium half-saturation coefficient of sugars half-saturation coefficient of aminophenol half-saturation coefficient of C4 half-saturation coefficient of propionate half-saturation coefficient of acetate half-saturation coefficient of H2 initial hydrolysis rate constant of protein initial hydrolysis rate constant of carbohydrate initial maximum specific uptake rate of sugar initial maximum specific uptake rate of aminophenol initial maximum specific uptake rate of C4 initial maximum specific uptake rate of propionate initial maximum specific uptake rate of acetate initial maximum specific uptake rate of H2 acceleration coefficient of hydrolysis of protein acceleration coefficient of hydrolysis of carbbohydrate acceleration coefficient of uptake of sugar acceleration coefficient of uptake of aminophenol acceleration coefficient of uptake of C4 acceleration coefficient of uptake of propionate acceleration coefficient of uptake of acetate acceleration coefficient of uptake of H2 decay coefficient for sugar degraders decay coefficient for aminophenol degraders decay coefficient for C4 degraders decay coefficient for propionate degraders decay coefficient for acetate degraders decay coefficient for H2 degraders

day-1 day-1 day-1 day-1 day-1 day-1 day-1 day-1 gCOD/L gCOD/L gCOD/L gCOD/L gCOD/L gCOD/L day-1 day-1 day-1 day-1 day-1 day-1 day-1 day-1 day-2 day-2 day-2 day-2 day-2 day-2 day-2 day-2 day-1 day-1 day-1 day-1 day-1 day-1

6.56 30 50 20 13 8 35 0.369 5.422 0.239 0.300 0.004 2.5 × 10-5 0 0 0 0 0 0 0 0 0.039 / 0.088 0.025 0.069 0.009 0.063 0.098 0.02 0.02 0.02 0.02 0.02 0.02

0.75 2.50 5 70 42 20 24 37 0.369 5.422 2.846 0.300 0.165 0.1 × 10-3 0 0 0 0 0 0 0 0 0.006 0.004 0.089 0.025 0.004 0.620 0.075 0.027 0.02 0.02 0.02 0.02 0.02 0.02

The sensitivity analysis results thus provided useful information for the subsequent model calibration. 4.3. Model Calibration. The model calibration procedure was a process of adjusting the coefficient values in the model, and AQUASIM 2.0 was used to perform the model calibration in this study. The parameter values were estimated by minimizing the sum of the squares of the deviations between the

Figure 2. Sensitivity analysis of acceleration coefficients.

measured data and the simulation results. The objective function to be minimized in the parameter estimation was thus n

χ2(p) )

∑ i)1

(

ymeas,i - yi(p) σmeas,i

)

2

(9)

where ymeas,i is the ith measurement, σmeas,i is its standard deviation, yi(p) is the calculated value of the model variable corresponding to the ith measurement evaluated at the time and location of this measurement, p ) (p1, ..., pm) are the model parameters, and n is the number of data points. Our model was calibrated using the experimental results from the UASB reactor startup. To simplify the calibration process and reduce the simulation workload, the strategy employed for model calibration was to change as few coefficients as possible. The rate coefficients at equilibrium in this model came from the default values in ADM1, because ADM1 is a steady structured model and the corresponding rate coefficient is appropriate for steady-state process. In the case of a mismatch, the calibration procedure was repeated until the predicted results matched well with the measured results. The simulated results and measured data for the UASB reactor are shown in Figure 1, and a good agreement can be observed. The corresponding kinetic parameters are summarized in Table 4. 4.4. Model Verification. Model verification was performed using experimental data from Griffin et al.,5 who fed a laboratory-scale CSTR with simulated municipal solid waste as the substrate. Their experiments were conducted in a 5-L Microferm benchtop fermentor with a 3-L working volume at

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Table 5. Chemical Characteristics of Feedstock in the Study of Griffin et al. (1998)5 parameter b

TS VSc cellulose hemicelulose lignin acetate propionate butyrate total VFAsd bicarb.alkalinity pH sulfate nitrogen

mean ( SDa 7.0 ( 0.6 88.0 ( 0.9 55.5 ( 4.4 10.7 ( 1.1 8.9 ( 0.4 372 ( 66 136 ( 36 47 ( 29 587 ( 91 158 ( 26 7.1 ( 0.2 53 ( 14 1.4 ( 0.3

units % % TS % TS % TS % TS mg/L as mg/L as mg/L as mg/L as mg/L as

acetic acid acetic acid acetic acid acetic acid CaCO3

Figure 4. Variation of hydrolysis rate coefficients and specific uptake rate coefficients during the startup of the UASB reactor.

mg/L as sulfate % TS

a Mean values and standard deviations (SDs) were obtained by performing analyses for seven batches of feedstock. b TS ) total solid. c VS ) volatile solid. d Total VFAs equals the sum of the concentrations of acetate, propionate, butyrate, valerate, iso-valerate, and iso-butyrate as measured by gas chromatography.

Figure 3. Simulation results and experimental data for CSTR system startup from Griffin et al. (1998)5 (lines for simulation results, dots for experimental data).

55 °C. The solid waste was prepared by collecting different paper fractions and food wastes from several local restaurants and grocery stores. These components were combined in proportions reflecting their presence in actual municipal solid waste. Chemical characteristics of the feedstock are listed in Table 5. The design organic loading rate was 3.1 gVS/(L day) with a retention time of 20 days. The measured and simulated results for VFA production and consumption and methane percentage are shown in Figure 3. Acetate and butyrate were accumulated on the fifth day after the reactor startup. This phenomenon was similar to that in our experiments for the startup of a UASB reactor (Figure 1). However, no propionate accumulation was observed in their CSTR. This difference might be attributed to the fact that the activity of the microorganisms for propionate uptake increased rapidly (k′pr ) 0.620 day-2, shown in Table 4). The deviation between the simulated results and the measured data was less than 10%, indicating that our model could capture the variation trends of all of these components and provide a reasonable description of both reactor startup and the increase in microbial activity. 4.5. Model Simulation. The variation trends of the hydrolysis rate and uptake rate coefficients in the reactor startup process could be simulated using our model, as shown in Figure 4 for the UASB reactor as an example. In the simulation process,

Figure 5. Startup process of the UASB reactor at various OLRs.

the influent carbohydrate and protein concentrations of UASB system were set at 6 and 4 gCOD/L, respectively. As shown in Figure 4, both the hydrolysis rate coefficients and the uptake rate coefficients increased during reactor startup period, implying that the microbial activity of sludge was restored step by step with time. At day 40, the rate coefficients of different uptake processes reached their maximum values. Then, the activities of various microorganisms were close to their maximum activities. Accordingly, high levels of COD removal efficiency and gas production were obtained. As a result, a successful startup of the anaerobic reactor was achieved. Many efforts have been made regarding the startup process of anaerobic reactors. Several operating parameters have been identified as significantly affecting the startup of anaerobic reactors, such as inoculation, temperature, pH, organic loading rate, and C/N ratio.28-30 Therefore, an appropriate strategy should be employed to accelerate startup (i.e., to reduce the startup time of anaerobic reactors). As the organic loading rate (OLR) is the most important parameter for the startup of anaerobic systems,1,11 its effect on the UASB startup was simulated based on the parameters for the model calibration (Table 4), and the simulation results are shown in Figure 5. With an increase in OLR, the time for the successful reactor startup was prolonged, which might be attributed to the initial accumulation of VFAs, especially propionate, at higher OLRs, such as 0.56 and 2.24 gCOD/(L day). These simulation results are similar to the experimental observations of Speece et al.31

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and further demonstrate that it is critical to employ an appropriate OLR to start up anaerobic reactors successfully. Transition and startup times can be dramatically reduced by using dynamic optimal startup procedures. For example, a twophase anaerobic reactor was started up quickly when the organic loading rate was slowly increased from 1 to 10 g/(L day).28 A feeding strategy consisting of maintaining a low nitrogen concentration in the influent during the initial two weeks followed by a nitrogen-balanced feed was proposed to accelerate the startup of an anaerobic filter reactor.29 In addition, cationic polymers can be used to accelerate startup and enhance granulation in UASB reactors.30 4.6. Parameter Comparison. As shown in Table 4, the acceleration coefficients of the hydrolysis process (0.039 day-2) were comparatively lower than those of the other processes in the UASB system. Similar results were also obtained for the CSTR reactor, with lower values of 0.004 and 0.005 day-2 for the hydrolyses of proteins and carbohydrates, respectively. This might be due to the fact that the hydrolysis process is the ratelimiting step in anaerobic systems with solid wastes as substrates.14,32 In addition, the hydrolysis rate coefficients of proteins in equilibrium (6.56 day-1 for the UASB, 0.75 day-1 for the CSTR) and carbohydrates (2.50 day-1 for the CSTR) were lower than the default value in ADM1 (10 day-1 for all solid hydrolyses). However, these values were higher than those reported by Siegrist et al.,33 who investigated anaerobic digestion of sewage sludge under mesophilic (0.25 day-1) and thermopholic (0.40 day-1) conditions. This discrepancy might be associated with the difference in wastewater characteristics, reactor operating conditions, and microbial communities.34 In addition, the higher protein hydrolysis rate in the UASB system compared to the CSTR reactor could be due to the microbial diversity and the relatively simple biodegradable component in the UASB influent. 4.7. Significance of This Work. The model developed in this study can be used to predict the startup process of anaerobic systems, such as the variation trends of substrate, intermediates, and end products, which should be very useful for the design, development, and application of anaerobic systems for the treatment of various wastewaters. However, in the present form, our model is not able to describe the startup of anaerobic systems to treat some specific pollutants, such as toxic substances, sulfur compounds, and metallurgical wastewaters. This is because the microbial community could change significantly with the progressive bioactivity restoration of microorganisms in the anaerobic reactors treating these specific substrates.35-37 5. Conclusions A mathematical model was developed to describe the startup process of anaerobic systems by integrating a linear phenomenological equation into the IWA Anaerobic Digestion Model No. 1 (ADM1). The model was calibrated and verified using two anaerobic reactors, namely, a UASB and CSTR, and it captures the variation trends of components in both reactors well, demonstrating its rationality and validity for describing the anaerobic reactor startup process. The acceleration coefficient of uptake of acetate (k′ac) was identified as the most important parameter in the anaerobic reactor startup. Acknowledgment The authors thank the NSFC (50625825, 50708106, and 50738006), the NSFC-JST Joint Project (21021140001), and

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the Ministry of Science & Technology of China (2008BADC4B18) for partial support of this study. Literature Cited (1) Alvarez, J. A.; Ruiz, I.; Gomez, M.; Presas, J.; Soto, M. Start-up Alternatives and Performance of an UASB Pilot Plant Treating Diluted Municipal Wastewater at Low Temperature. Bioresour. Technol. 2006, 97, 1640–1649. (2) Angenent, L. T.; Sung, S. W.; Raskin, L. Methanogenic Population Dynamics during Startup of a Full-Scale Anaerobic Sequencing Batch Reactor Treating Swine Waste. Water Res. 2002, 36, 4648–4654. (3) Foresti, E. Anaerobic Treatment of Domestic Sewage: Established Technologies and Perspectives. Water Sci. Technol. 2002, 45, 181–186. (4) Karim, K.; Hoffmann, R.; Klasson, T.; Al-Dahhan, M. H. Anaerobic Digestion of Animal Waste: Waste Strength versus Impact of Mixing. Bioresour. Technol. 2005, 96, 1771–1781. (5) Griffin, M. E.; McMahon, K. D.; Mackie, R. I.; Raskin, L. Methanogenic Population Dynamics during Start-up of Anaerobic Digesters Treating Municipal Solid Waste and Biosolids. Biotechnol. Bioeng. 1998, 57, 342–355. (6) Show, K. Y.; Wang, Y.; Foong, S. F.; Tay, J. H. Accelerated Startup and Enhanced Granulation in Upflow Anaerobic Sludge Blanket Reactors. Water Res. 2004, 38, 2293–2304. (7) Fang, H. H. P.; Lau, I. W. C. Startup of Thermophilic (55 °C) UASB Reactors Using Different Mesophilic Seed Sludges. Water Sci. Technol. 1996, 34, 445–452. (8) Francese, A.; Cordoba, P.; Duran, J.; Sineriz, F. High Upflow Velocity and Organic Loading Rate Improve Granulation in Upflow Anaerobic Sludge Blanket Reactors. World J. Microb. Biotechnol. 1998, 14, 337–341. (9) Ghangrekar, M. M.; Asolekar, S. R.; Joshi, S. G. Characteristics of Sludge Developed under Different Loading Conditions during UASB Reactor Start-up and Granulation. Water Res. 2005, 39, 1123–1133. (10) Show, K. Y.; Tay, J. H.; Yang, L. M.; Wang, Y.; Lua, C. H. Effects of Stressed Loading on Startup and Granulation in Upflow Anaerobic Sludge Blanket Reactors. J. EnViron. Eng. ASCE 2004, 130, 743–750. (11) Jayantha, K. S.; Ramanujan, T. K. Start-up Criteria for a Upflow Anaerobic Sludge Blanket (UASB) Reactor. Bioprocess Eng. 1995, 13, 307– 310. (12) Batstone, D. J.; Keller, J.; Steyer, J. P. A Review of ADM1 Extensions, Applications, and Analysis: 2002-2005. Water Sci. Technol. 2006, 54, 1–10. (13) Johnson, B. R.; Shang, Y. Applications and Limitations of ADM1 in Municipal Wastewater Solids Treatment. Water Sci. Technol. 2006, 54, 77–82. (14) Yasui, H.; Goel, R.; Li, Y. Y.; Noike, T. Modified ADM1 Structure for Modelling Municipal Primary Sludge Hydrolysis. Water Res. 2008, 42, 249–259. (15) Batstone, D. J.; Keller, J.; Angelidaki, I.; Kalyuzhnyi, S. V.; Pavlostathis, S. G.; Rozzi, A.; Sanders, W. T. M.; Siegrist, H.; Vavilin, V. A. The IWA Anaerobic Digestion Model No. 1 (ADM1). Water Sci. Technol. 2002, 45, 65–73. (16) Masse, D. I.; Droste, R. L. Comprehensive Model of Anaerobic Digestion of Swine Manure Slurry in a Sequencing Batch Reactor. Water Res. 2000, 34, 3087–3106. (17) Shi, R. J.; Zhang, Y.; Xu, H.; Zhang, Z.; Zhang, C. G. Pretreatment of Distillery Wastewater from Vitamin C Synthesis Industry by Upflow Anaerobic Sludge Blanket (UASB) Reactor. EnViron. Eng. Sci. 2007, 24, 1333–1337. (18) Vavilin, V. A.; Lokshina, L. Y. Modeling of Volatile Fatty Acids Degradation Kinetics and Evaluation of Microorganism Activity. Bioresour. Technol. 1996, 57, 69–80. (19) Fitts, D. Nonequilibrium Thermodynamics; McGraw-Hill: New York,1962. (20) Prigogine, I. Introduction to Thermodynamics of IrreVersible Processes; Wiley-Interscience: New York, 1967. (21) Heijnen, J. J.; Vandijken, J. P. In Search of a Thermodynamic Description of Biomass Yields for the Chemotropic Growth of Microorganisms. Biotechnol. Bioeng. 1992, 39, 833–858. (22) Dong, F.; Zhao, Q. B.; Zhao, J. B.; Sheng, G. P.; Tang, Y.; Tong, Z. H.; Yu, H. Q.; Li, Y. Y.; Harada, H. Monitoring the Restart-up of an Upflow Anaerobic Sludge Blanket (UASB) Reactor for the Treatment of a Soybean Processing Wastewater. Bioresour. Technol. 2009, 101, 1722– 1726. (23) Reichert, P. Aquasim 2.0 User Manual; Swiss Federal Institute for Environmental Science and Technology (EAWAG): Dubendorf, Switzerland, 1998.

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ReceiVed for reView January 26, 2010 ReVised manuscript receiVed May 21, 2010 Accepted June 18, 2010 IE1001857