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Modeling of two-phase anaerobic process treating traditional Chinese medicine wastewater with the IWA Anaerobic Digestion Model No. 1 Zhaobo Chen a,b,3 , Dongxue Hu b,1 , Zhenpeng Zhang c,2 , Nanqi Ren a, * , Haibo Zhu a,3 a State Key Laboratory of Urban Water Resource and Environment, Research Center of Environmental Biotechnology 2614#, School of Municipal and Environmental Engineering, Harbin Institute of Technology, 202 Haihe Road, Harbin 150090, China b School of Materials Science and Chemical Engineering, Harbin Engineering University, 145 Nantong Street, Harbin 150001, PR China c Beijing Enterprises Water Group, Beijing 100195, China article info Article history: Received 21 January 2009 Received in revised form 28 April 2009 Accepted 28 April 2009 Available online 23 May 2009 Keywords: Traditional Chinese medicine wastewater Two-phase anaerobic process ADM1 Dynamic modeling Simulation abstract The aim of the study was to implement a mathematical model to simulate two-phase anaerobic digestion (TPAD) process which consisted of an anaerobic continuous stirred tank reactor (CSTR) and an upflow anaerobic sludge blanket (UASB) reactor in series treating traditional Chinese medicine (TCM) wastewa- ter. A model was built on the basis of Anaerobic Digestion Model No. 1 (ADM1) while considering com- plete mixing model for the CSTR, and axial direction discrete model and mixed series connection model for the UASB. The mathematical model was implemented with the simulation software package MAT- LABTM/Simulinks. System performance, in terms of COD removal, volatile fatty acids (VFA) accumulation and pH fluctuation, was simulated and compared with the measured values. The simulation results indi- cated that the model built was able to well predict the COD removal rate (4.8–5.0%) and pH variation (2.9–1.4%) of the UASB reactor, while failed to simulate the CSTR performance. Comparing to the mea- sured results, the simulated acetic acid concentration of the CSTR effluent was underpredicted with a deviation ratios of 13.8–23.2%, resulting in an underprediction of total VFA and COD concentrations despite good estimation of propionic acid, butyric acid and valeric acid. It is presumed that ethanol pres- ent in the raw wastewater was converted into acetic acid during the acidification process, which was not considered by the model. Additionally, due to the underprediction of acetic acid the pH of CSTR effluent was overestimated. Ó 2009 Elsevier Ltd. All rights reserved. 1. Introduction Traditional Chinese medicine (TCM), a splendid culture of Chi- nese nation for thousands of years, occupies the very important status in the medicine domain due to its unique effectiveness. In recent years, TCM industry rapidly develops, which results in a cor- respondingly increase in TCM wastewater production. It is esti- mated that approximately half of the TCM wastewater was discarded directly without specific treatment (Shi et al., 2005). The composition of TCM wastewater was determined by raw mate- rials and production process, mainly coming from the washing water of raw material, the residual liquid of original medicine and floor washing water (Jiang et al., 2006; Zhu, 2007; Zhao et al., 2007). TCM wastewater contains a lot of various natural or- ganic pollutants, such as carbohydrates (mainly polysaccharides), alkaloids, phenols,alcohols, amino acid, lignin, proteins, pigments and their hydrolysates, in the form of water-soluble, colloidal, sus- pended particles (Li et al., 2006; Zhu, 2007). Therefore, TCM waste- water is a kind of high concentration organic wastewater with complicated composition, which will cause the serious pollution to the environment if emitted directly. Due to the high COD concentration in pharmaceutical wastewa- ters, attempts have already been made to work with anaerobic pro- cesses (Yang et al., 2003; Göblös et al., 2008). It has been noted that the acid inhibition usually occurs in the one-phase anaerobic di- gester because of the differences in the rates of acidogenesis and methanogenesis, which depresses the methane yield and the sta- bility, resulting thereby in low treatment efficiency. The different growth rates and pH optima for acidogenic and methanogeic organisms, and thus different requirements regarding reactors, have led to the development of two-phase anaerobic digestion (TPAD) processes (Liu, 1998). Currently, research on TPAD processes was concentrated on the evaluation of operating condi- tions, biological characteristics, and process performance, but not 0960-8524/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2009.04.066 * Corresponding author. Fax: +86 451 8628 2008. E-mail addresses: [email protected] (Z. Chen), [email protected] (D. Hu), [email protected] (Z. Zhang), [email protected] (N. Ren), zhuhaibo1616@163. com (H. Zhu). 1 Fax: +86 451 8628 2195. 2 Fax: +65 6792 1291. 3 Fax: +86 451 8628 2008. Bioresource Technology 100 (2009) 4623–4631 Contents lists available at ScienceDirect Bioresource Technology journal homepage: www.elsevier.com/locate/biortech

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Page 1: Modeling of two-phase anaerobic process treating traditional Chinese medicine wastewater with the IWA Anaerobic Digestion Model No. 1

Bioresource Technology 100 (2009) 4623–4631

Contents lists available at ScienceDirect

Bioresource Technology

journal homepage: www.elsevier .com/locate /bior tech

Modeling of two-phase anaerobic process treating traditional Chinesemedicine wastewater with the IWA Anaerobic Digestion Model No. 1

Zhaobo Chen a,b,3, Dongxue Hu b,1, Zhenpeng Zhang c,2, Nanqi Ren a,*, Haibo Zhu a,3

a State Key Laboratory of Urban Water Resource and Environment, Research Center of Environmental Biotechnology 2614#,School of Municipal and Environmental Engineering, Harbin Institute of Technology, 202 Haihe Road, Harbin 150090, Chinab School of Materials Science and Chemical Engineering, Harbin Engineering University, 145 Nantong Street, Harbin 150001, PR Chinac Beijing Enterprises Water Group, Beijing 100195, China

a r t i c l e i n f o

Article history:Received 21 January 2009Received in revised form 28 April 2009Accepted 28 April 2009Available online 23 May 2009

Keywords:Traditional Chinese medicine wastewaterTwo-phase anaerobic processADM1Dynamic modelingSimulation

0960-8524/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.biortech.2009.04.066

* Corresponding author. Fax: +86 451 8628 2008.E-mail addresses: [email protected] (Z. Chen), hudo

[email protected] (Z. Zhang), [email protected] (com (H. Zhu).

1 Fax: +86 451 8628 2195.2 Fax: +65 6792 1291.3 Fax: +86 451 8628 2008.

a b s t r a c t

The aim of the study was to implement a mathematical model to simulate two-phase anaerobic digestion(TPAD) process which consisted of an anaerobic continuous stirred tank reactor (CSTR) and an upflowanaerobic sludge blanket (UASB) reactor in series treating traditional Chinese medicine (TCM) wastewa-ter. A model was built on the basis of Anaerobic Digestion Model No. 1 (ADM1) while considering com-plete mixing model for the CSTR, and axial direction discrete model and mixed series connection modelfor the UASB. The mathematical model was implemented with the simulation software package MAT-LABTM/Simulinks. System performance, in terms of COD removal, volatile fatty acids (VFA) accumulationand pH fluctuation, was simulated and compared with the measured values. The simulation results indi-cated that the model built was able to well predict the COD removal rate (�4.8–5.0%) and pH variation(�2.9–1.4%) of the UASB reactor, while failed to simulate the CSTR performance. Comparing to the mea-sured results, the simulated acetic acid concentration of the CSTR effluent was underpredicted with adeviation ratios of 13.8–23.2%, resulting in an underprediction of total VFA and COD concentrationsdespite good estimation of propionic acid, butyric acid and valeric acid. It is presumed that ethanol pres-ent in the raw wastewater was converted into acetic acid during the acidification process, which was notconsidered by the model. Additionally, due to the underprediction of acetic acid the pH of CSTR effluentwas overestimated.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Traditional Chinese medicine (TCM), a splendid culture of Chi-nese nation for thousands of years, occupies the very importantstatus in the medicine domain due to its unique effectiveness. Inrecent years, TCM industry rapidly develops, which results in a cor-respondingly increase in TCM wastewater production. It is esti-mated that approximately half of the TCM wastewater wasdiscarded directly without specific treatment (Shi et al., 2005).The composition of TCM wastewater was determined by raw mate-rials and production process, mainly coming from the washingwater of raw material, the residual liquid of original medicineand floor washing water (Jiang et al., 2006; Zhu, 2007; Zhao

ll rights reserved.

[email protected] (D. Hu),N. Ren), zhuhaibo1616@163.

et al., 2007). TCM wastewater contains a lot of various natural or-ganic pollutants, such as carbohydrates (mainly polysaccharides),alkaloids, phenols,alcohols, amino acid, lignin, proteins, pigmentsand their hydrolysates, in the form of water-soluble, colloidal, sus-pended particles (Li et al., 2006; Zhu, 2007). Therefore, TCM waste-water is a kind of high concentration organic wastewater withcomplicated composition, which will cause the serious pollutionto the environment if emitted directly.

Due to the high COD concentration in pharmaceutical wastewa-ters, attempts have already been made to work with anaerobic pro-cesses (Yang et al., 2003; Göblös et al., 2008). It has been noted thatthe acid inhibition usually occurs in the one-phase anaerobic di-gester because of the differences in the rates of acidogenesis andmethanogenesis, which depresses the methane yield and the sta-bility, resulting thereby in low treatment efficiency. The differentgrowth rates and pH optima for acidogenic and methanogeicorganisms, and thus different requirements regarding reactors,have led to the development of two-phase anaerobic digestion(TPAD) processes (Liu, 1998). Currently, research on TPADprocesses was concentrated on the evaluation of operating condi-tions, biological characteristics, and process performance, but not

Page 2: Modeling of two-phase anaerobic process treating traditional Chinese medicine wastewater with the IWA Anaerobic Digestion Model No. 1

4624 Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631

the model development. Model study might be favorable to com-prehensively evaluate the impacts of all process variables on theperformance of TPAD processes.

Many models had been developed for anaerobic digestion pro-cesses, but with increasing complexity of the advanced digestiontechnologies, more complex models that can represent the impactsof changing environments on chemical and microbial species wererequired (Parker, 2005). Recently, there had been a move by theInternational Water Associations (IWA) Task Group for mathemat-ical modelling of anaerobic digestion processes to develop a com-mon model (Batstone et al., 2002a). ADM1 model has been usedas a common basis for further development or as a commonplatform for dynamic simulation of different anaerobic processes.Blumensaat and Keller (2005) used ADM1 model to implementthe anaerobic two-stage digestion of sewage sludge, a thermophilicpre-treatment followed by a mesophilic main treatment stage. Theaccuracy of the optimized parameter sets has been assessedagainst experimental data from pilot-scale experiments. Underthese conditions, the model predicted reasonably well the dynamicbehaviour of a two-stage digestion process in pilot-scale. Consider-ing microbial storage, Toshio et al. (2007) demonstrated that themodified model adequately described anaerobic sequencing batchreactor ASBR dynamics in the degradation of trehalose during a 24-h cycle. There were also many implementations of this powerfultool that have been tested and proved their success in simulatingthe anaerobic digestion of several organic wastes (Fuentes andNicolás, 2008; Fezzani and Ridha, 2008).

However, application of ADM1 model to the two-phase anaero-bic process, especially for the treatment of drugs manufacturingwith two-phase anaerobic process wastewater remained limited.The objective of this study was to make ADM1 model modifiedbased on the process configurations and then to simulate experi-mental results of two-phase anaerobic process treating TCMwastewater with the modified ADM1 model.

2. Methods

2.1. Pilot-scale setup

A schematic diagram of pilot-scale anaerobic process is shownin Fig. 1. The system consisted of two anaerobic reactors, that is,

Inflnent

Buffer tank

CSTR

Pump

BlenderFermented gas

Elevated water t

ORP/pH determin

Te

Temperature controller

Solenoid valve

Soleno

Solenoid valve

Fig. 1. Scheme of th

a continuous stirred tank reactor (CSTR) and an upflow anaerobicsludge blanket anaerobic reactor (UASB), working as acidogenicconditions and mehtanogenic conditions, respectively. The CSTRhad a working volume of approximately 12 m3 with an internaldiameter of 2.2 m and a height of 3.2 m. A blender was equippedfor culture mixing. The UASB was built cuboidally with a squaresection of 9 m2 and a height of 6.5 m, providing a working volumeof approximately 55 m3. The top half of the reactor was filled withwave-shaped epoxy glass cells (cell thickness, 1 mm; crest heightof wave, 45 mm; crest distance of wave 100 mm; slant distanceof wave, 130 mm; and specific surface area, 360 m2 m�3). Athree-phase separator was installed upside the reactor to preventbiomass washout. The HRTs of CSTR and UASB were maintainedconsistently at 12 h and 55 h, respectively. The reactor tempera-tures were maintained through auto-controlled heat exchangersto be 35 �C.

2.2. Wastewater

The wastewater, obtained from a local TCM company (Harbin,China) was used as the feedstock of the CSTR-UASB system. Thewastewater was generated from the processes of washing, distilla-tion, separation, concentration, purification washing, refining, tor-refaction, and moulding. Its biochemical oxygen demand (BOD5)was estimated to be around 4200 mg/L and COD around 17000mg/L, indicating a low biodegradability (BOD5/COD � 0.25). Vola-tile fatty acids (VFA), including acetic, propionic and butyric acids,and ethanol accounted for about 3.65% of total wastewater COD,with the averages as follows: acetic acid (241 mg/L), propionic acid(63 mg/L), butyric acid (68 mg/L) and ethanol (358 mg/L).

2.3. Analytical methods

The TCM wastewater and effluent of CSTR and UASB werecollected daily for the analysis of COD. Measurement of COD wasperformed directly on the sampled slurry according to StandardMethods (APHA, 1995). VFA and ethanol were determined by gaschromatography (GC) as described in the previous work (Renet al., 2006). The GC (GC-122, Shanghai Analytical ApparatusCorporation, China) was equipped with an injector (220 �C), ahydrogen flame ionization detector (220 �C) and a 2 m

Pump

UASB

Pump

Effluent

Pump

Methane

Heating

ank

ator

Electric cabinet

mperature controller

id valve

Solenoid valve

e TPAD system.

Page 3: Modeling of two-phase anaerobic process treating traditional Chinese medicine wastewater with the IWA Anaerobic Digestion Model No. 1

Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631 4625

(length) � 5 mm (ID) stainless steel column packed with supporterof GDX-103 (60–80 meshes). The operation of the stainless steelcolumn was amenable to a temperature programming processwithin 100–200 �C (N2 carrier at a flow rate of 50 mL/min, andcombustion gas H2 at 50 mL/min, combustion-supporting gas O2

at 500 mL/min). The retention times were of 0.474 min for ethanol,0.690 min for acetic acid, 1.412 min for propionic acid, 3.020 minfor butyric acid and 6.727 min valeric acid. Before measurement,the sample was filtrated through a 0.45 lm filter and acidified with6 M HCl. Two lL acidified sample was injected manually for anal-ysis. ORP and pH were monitored daily by ORP determinator (ORP-412, Cany Precision Instruments Co., Ltd, China).

3. Mathematical model

3.1. Substrate degradation and main hypotheses of modeling

It is recognized that a number of the conversion processes thatare active in anaerobic digestion can be inhibited by the accumula-tion of intermediate products such as molecular hydrogen, ammo-nia or by extremes of pH. In this study, it is assumed that allmicrobially mediated substrate conversion processes are onlyinhibited by extremes of pH. Fluid-gas exchange process is ne-glected. The microorganism content is neglected in influent andeffluent. These simplifying assumptions are introduced to makethe model workable, although they do not completely reflectreality.

3.2. Main mathematical model equations

In ADM1 model, cellular kinetics is described by three expres-sions: uptake, growth and decay. This model includes three overallbiochemical (cellular) steps, this is, acidogenesis (fermentation),acetogenesis (anaerobic oxidation of organic acids) and methano-genesis. In a two phase anaerobic process, acidogenic-phase diges-tion process includes acidogenesis and acetogenesis steps, whilemethanogenic-phase process involves methanogenesis.

Matrix form is adopted in ADM1 model to describe the bioki-netics model (Batstone et al., 2002a). An important advantage ofmatrix form is able to easily understand dynamic process of thevarious components used in the model. The matrix includes bio-chemical rate coefficients (v i;j) and kinetic rate equations (qj). Be-

Table 1Model component.

Component Name Value Unit

Ssu Monosaccharides Simulation kgCOD/m3

Saa Amino acid Simulation kgCOD/m3

Sfa Long chain fatty acid Simulation kgCOD/m3

Sva Valerate acid Simulation kgCOD/m3

Sbu Butyric acid Simulation kgCOD/m3

Spro Propionic acid Simulation kgCOD/m3

Sac Acetic acid Simulation kgCOD/m3

Sh2 Dissolved H2 Simulation kgCOD/m3

Sch4 Dissolved CH4 Simulation kgCOD/m3

SI Soluble inerts Simulation kgCOD/m3

Xc Composites Simulation kgCOD/m3

Xch Carbohydrates Simulation kgCOD/m3

Xpr Proteins Simulation kgCOD/m3

Xli Lipids Simulation kgCOD/m3

Xsu Sugar degraders Simulation kgCOD/m3

Xaa Amino acid degraders Simulation kgCOD/m3

Xfa LCFA degraders Simulation kgCOD/m3

Xc4 Valerate and butyrate degraders Simulation kgCOD/m3

Xpro Propionate degraders Simulation kgCOD/m3

Xac Acetate degraders Simulation kgCOD/m3

Xh2 Hydrogen degraders Simulation kgCOD/m3

Xi Particulate inerts Simulation kgCOD/m3

cause the microorganism content is neglected in influent andeffluent flows, the calculation of the state variables is shown inEqs. (1) and (2).

VdSi

dt¼ qinSi;in � qoutSi þ

Xj¼1�19

qjv i;j ð1Þ

VdXi

dt¼ �qwXi þ

Xj¼1�19

qjmi;j ð2Þ

WhereP

j¼1�19qjv i;j is the sum of the biochemical rate coefficientsðv i;jÞmultiplies kinetic rate equations ðqjÞ for process; V is the liquidreactor volume; qin is flow rate of influent; qout is flow rate of efflu-ent; qw is flow rate of sludge discharge; Si;in is the input concentra-tion of the soluble components; Si is the concentration of solublecomponents and Xi is the concentration of the particulate in thereactor. The components are shown in Table 1.

I1 ¼ I2 ¼ I3 ¼ exp �3pH � pHUL

pHUL � pHLL

� �2" #

ð3Þ

To reduce the complexity of the model, only pH inhibition mecha-nisms are considered, as shown in Eq. (3).

3.2.1. The balance equations of CSTRIn the present study, the CSTR is considered to be completely

mixed through continuous stirring, and the frequency and inten-sity of mixing are sufficient to prevent the formation of large sub-strate gradients in the reactor. Based on the rate equations matrixof ADM1, the following balance equations of CSTR can be derived,as shown in Eqs. (4–20) (Appendix A).

3.2.2. The balance equations of UASBAccording to the distribution of sludge concentration along the

reactor height, UASB reactor was divided into three internal sec-tions, i.e., bed section, blanket section and settler section. In orderto simple the flow model, the axial direction discrete model andmixed series connection model were introduced here. Discretemodel, one kind of revised ideal model, mainly describes non-idealreactors, particularly applicable to the non- back-mixing systems.This model has assumptions as follows: (1) each section verticalto flow direction has homogeneous radial concentration; (2) axialdirection discrete coefficient DL is constant, regardless of operatingtime and reactor position; (3) substrate concentration is continu-ous functions of flow position. Two equations [Eqs. (21) and (22)]representing inflow and outflow states can de derived.

inflow : qSþ DLddl

Sþ dSdl

� �� �A ð21Þ

outflow : q Sþ dSdl

� �þ DL

dSdl

� �A ð22Þ

Where A is the section area of reactor, l is the reactor height and V isthe reactor volume.As high order differential items are neglected, abalance equation [Eq. (23)] can be derived.

dSdt¼ DL

d2S

dl2 � qdSdl

ð23Þ

Boundary conditions for Eq. (23) are described as follows:

l ¼ 0; qSin ¼ qSþin � DLdSdl

� �þin

ð24Þ

l ¼ L;dSdl

� �L

¼ 0 ð25Þ

Page 4: Modeling of two-phase anaerobic process treating traditional Chinese medicine wastewater with the IWA Anaerobic Digestion Model No. 1

4626 Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631

According to the mixed series connection model, the actual reactoris divided into several equal volume ideal-mixing sectors. The massbalance of those sectors is expressed by Eqs. (26) and (27).

Vn

dSi

dt¼ qSi;in þ

Xj¼1�19

qjmi;j ð26Þ

Vn

dXi

dt¼ �qwXi þ

Xj¼1�19

qjmi;j ð27Þ

By combining the rate equations matrix of ADM1 and the flowmodel, the balance equations of UASB are derived, as shown inEqs. (28)–(38) (Appendix B). Boundary conditions for the abovematerial balance equations are given in following equations [Eqs.(24) and (25)]:

3.3. Model parameters estimation

In ADM1, there are three main parameters: stoichiometric coef-ficients, equilibrium coefficients, and kinetic parameters. Many ofthe stoichiometric coefficients, equilibrium coefficients as well askinetic parameters are assumed to be fixed due to their low vari-ability and sensitivity and taken from the scientific and technicalwork reported by the IWA task group and Chynoweth et al. (Chy-noweth et al., 1998a; Batstone et al., 2002b; Masse et al., 1996,1997; Masse and Droste, 1997, 2000; Stumm and Morgan, 1996).The reactor parameters were taken from the experimental set-ups for both datasets and applied to the model. The operating tem-peratures were 35 �C for CSTR and UASB. The HRTs of CSTR andUASB were maintained consistently at 12 and 55 h, respectively.All simulations were run to steady-state using a constant inputincluding a constant flow rate that was given through the initialconditions of the dynamic experiments, to avoid numerical prob-

Table 2Model parameters with low sensitivity and low variability from the literature.

Parameter Name Value Unit

A. Hydrolysis parameterkhyd-CH Hydrocarbon hydrolysis coefficient 8 d�1

khyd-PR Proteins hydrolysis coefficient 8 d�1

khyd-Li Lipid hydrolysis coefficient 8 d�1

B. Kinetic parameterskm-su Monod largest absorption rate of sugar 30 COD/(COD�d)KS-su Half saturated coefficient of sugar 0.5 kgCOD/m3

Ysu Yield of sugar 0.1 COD/CODkm-aa Monod largest absorption rate of amino

acid50 COD/(COD�d)

KS-aa Half saturated coefficient of amino acid 0.3 kgCOD/m3

Yaa Yield of amino acid 0.08 COD/CODkm-fa Monod largest absorption rate of long

chain fatty acid6 COD/(COD�d)

KS-fa Half saturated coefficient of long chainfatty acid

0.4 kgCOD/m3

Y fa Yield of long chain fatty acid 0.06 COD/CODkm-Cþ4

Monod largest absorption rate of butyricacid

20 COD/(COD�d)

KS-Cþ4Half saturated coefficient of butyric acid 0.3 kgCOD/m3

YCþ4Yield of butyric acid 0.06 COD/COD

Ypro Yield of propionic acid 0.04 COD/CODYac Yield of acetic acid 0.05 COD/CODkm-H2 Monod largest absorption rate of H2 35 COD/(COD�d)YH2 Yield of H2 0.06 COD/COD

C. Stoichiometric coefficientsfbu; su Butyric acid generated by sugar 0.59 –fac; su Acetic acid generated by sugar 0.45 –fpro; su Propionic acid generated by sugar 0.20 –fac; aa Acetic acid generated by amino acid 0.6 –fpro; aa Propionic acid generated by amino acid 0.02 –fbu; aa Butyric acid generated by amino acid 0.2 –fva; aa Valeric acid generated by amino acid 0.3 –

lems that could occur when starting a simulation with dynamic in-put (Pauss et al., 1990). The prior simulations were run over anintegration period of 120 days to find the effective steady state.Carbon contents were recalculated by implementing the balanceterms in the rate equation matrix. The mass balance has beenchecked for COD and carbon contents using the Excel spreadsheetdeveloped by Batstone et al. (2002a). These specific biochemicalprocess rates and coefficients are tabulated in Table 2.

Parameters with high sensitivity and high variability are esti-mated using experimental data of Masse et al., 1996, 1997; Masseand Droste, 2000; Siegrist et al., 2002; and the selected optimiza-tion methods (Masse et al., 1996, 1997; Masse and Droste, 1997,2000; Siegrist et al., 2002). The nonlinear constrained optimizationmethod was implemented using MATLAB 6.5 optimization toolbox.The two-parameter optimization around optimum using the secantmethod is implemented using Aquasim 2.1d version. Estimationprocedures were applied for the following parameters: the dissoci-ation coefficient, kdis; Monod largest absorption rate of acetic acidand propionic acid, km,ac, km,pro; Half saturated coefficient of aceticacid, propionic acid and hydrogen, Ks,ac, Ks, pro, Ks, H2. Estimation re-sults of the six parameters were shown in 4.1 sections.

3.4. Ordinary differential equation solutions

Anaerobic digestion processes was formed the work of whichled to the Anaerobic Digestion Model No. 1 (Batstone et al.,2002b). ADM1 is a highly complex model, characterized by 19 bio-chemical conversion processes and 24 dynamic state variables. Forthis paper calculations were executed with the software MATLABversion 6.5 (release 13) and Simulink 5.

MATLAB/Simulink system provides ordinary dynamic equationsolver systems (Copp et al., 2002; Rosen and Jeppson, 2002. Solverode23, a one-step solver, is an implementation of an explicit Run-ge–Kutta (2,3) pair of Bogacki and Shampine. It could be more effi-cient than ode45 in cases of crude tolerances and moderatestiffness. Solver ode45, a one-step solver, is based on an explicitRunge–Kutta (4,5) formula, the Dormand-Prince pair. It could beconsidered as the best function to apply as a ‘‘first try” for mostproblems. Solver ode15s, a multi-step solver, is a variable ordersolver based on the numerical differentiation formulas. Optionally,it uses the backward differentiation formulas (also known as Gear’smethod) that are usually less efficient. It is considered to be goodfor stiff problems while solving a differential-algebraic problem.Solver ode23s, a one-step solver is based on an extended Rosen-brock formula of order 2. It is could be more efficient than ode15sat crude tolerances and is used for the solution of specific type ofstiff problems where ode15s are not efficient.

4. Results and discussion

4.1. Parameter estimation results

The results of the parameter estimation indicated a good fit be-tween the model and the measured data. Parameters with low sen-sitivity and low variability were not further optimized aftercarrying out sensitivity analysis using Aquasim 2.1d software andcomparing with previous results of similar cases. Previous studyindicated that the effect of these parameters on the model out-comes is quite limited (Batstone et al., 2002a). Optimization ofthe low sensitivity parameters was not carried out since furthertuning of these parameters requires highly accurate experimentaldata, and changes in the model outputs would be relatively smallas compared to errors in the data. Those typical parameters wereselected for the optimization in this work (i.e., similar to the onesgenerally selected for the optimization).

Page 5: Modeling of two-phase anaerobic process treating traditional Chinese medicine wastewater with the IWA Anaerobic Digestion Model No. 1

Table 3Model parameter estimation results with high sensitivity and high variability.

Parameter Name Value Unit

kdis Dissociation coefficient 0.75 d�1

km-pro Monod largest absorption rate ofpropionic acid

13 COD/(COD�d)

KS-pro Half saturated coefficient of propionic acid 0.2 kgCOD/m3

km-ac Monod largest absorption rate of aceticacid

18 COD/(COD�d)

KS-ac Half saturated coefficient of acetic acid 0.2 kgCOD/m3

KS-H2 Half saturated coefficient of H2 5 � 10�5 kgCOD/m3

Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631 4627

In the estimation procedure, the disintegration constant wasfirst estimated by matching the model outputs with the measuredoutputs. Then the model outputs for concentrations of aceticacid and propionic acid were changed by changing the half satura-tion constants and maximum uptake rates. Parameters km, Ks foracetic acid and propionic acid were optimized together since theyexhibit the lowest correlation and the highest relevance (Batstoneet al., 2003). The optimum values for the parameters are given inTable 3.

4000

5000

6000

7000

8000

9000

10000

11000

12000

0 10 20 30 40 50 60

Time (d

CO

D (

mg/

L)

COD simulated COD

Fig. 2. Measured COD value of CSTR com

1300

1400

1500

1600

1700

1800

CO

D (

mg/

L)

COD simulated COD m

0 10 20 30 40 50 60

Time (da

Fig. 3. Measured COD value of UASB com

4.2. The simulation result of COD

The raw wastewater COD had a concentration of 15000–20000 mg/L, corresponding to an OLR of 30–40 kgCOD/m3 d. Anaverage COD removal rate of 41.7% was achieved by the CSTR,while simulated COD removal rates averaged at around 30.0%,showing a deviation ratio of 10.1–24.6% between the measuredand predicted values (Fig. 2). Comparing to the COD removal rates(20–30%) in acidogenic phase reported by Li et al. (2006), the pres-ent CSTR system exhibited a superior performance in COD removal.The inconsistency is likely owing to the difference of wastewatercomposition as TCM wastewater was used in the present study.The deviation ratios between the measured and predicted valuesindicate that the coefficients and parameters obtained by previouswork might not be suitable for the acidogenic phase simulation ofTCM with ADM1 model.

In the two-phase anaerobic process, wastewater COD is mainlyremoved in the methanogenic phase. The effluent of UASB stabi-lized at around 1400 mg/L, corresponding to a COD removal rateof 86.7%. Only a COD removal efficiency of 65% was achieved in asimilar, but one-phase UASB reactor while treating chemical syn-thesis based pharmaceutical wastewater (Ince et al., 2002). Results

70 80 90 100 110 120

ays)

5

10

15

20

25

30

35

40

45

50

devi

atio

n ra

tio

meatured deviation ratio

parison with model simulated value.

-30

-25

-20

-15

-10

-5

0

5

10

devi

atio

n ra

tio

eatured deviation ratio

70 80 90 100 110 120

ys)

parison with model simulated value.

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4628 Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631

of this study show that the TPAD system is much superior com-pared to one-phase processes. It is probably owing to the fact thatcomplicated organics were first broken down and converted toeasily biodegradable components in the CSTR. The experimentalresults did indicate a substantially increase in ethanol concentra-tion of CSTR effluent. As shown in Fig. 3, the model was able to pre-dict the COD of UASB effluent with a considerable accuracy. It wasfound that the deviation ratio of the measured and simulated val-ues fluctuated between �4.8% and 5.0%.

4.3. The simulation result of VFA

In the two-phase anaerobic process, the function of acidogenicphase reactor is mainly to enhance wastewater biodegradabilitythrough decomposing large molecular compounds into smallmolecular products. In the present study, VFA and ethanol concen-trations of CSTR effluent increased substantially, suggesting theacidification occurring in the CSTR. It was found an averageincrease in acetic acid from 241 ± 1.6 mg/L to 873 ± 2.8 mg/L, inpropionic acid from 63 ± 1.2 mg/L to 174 ± 1.7 mg/L, in butyric acidfrom 68 ± 0.9 mg/L to 340 ± 2.5 mg/L, valeric acid from null to150 ± 2.06 mg/L, and in ethanol from 358 ± 2.1 mg/L to 767 ± 3.2mg/L. As a result, the proportions of wastewater VFA and ethanolincreased from 3.65% to 23.09% after the TCM wastewater reactedin the CSTR. Fig. 4 shows the measured and simulated results ofVFA and ethanol in the CSTR. It can be seen that propionic acid, bu-tyric acid and valeric acid were well predicted with respectivedeviation ratios of �6.5–7.1%, �2.1–5.7%, and �4.4–4.3%. However,

300

400

500

600

700

800

900

1000

acet

icac

id (

mg/

L)

acetic acid simulated acetic

150

165

180

195

210

225

prop

ioni

c ac

id (

mg/

L)

propionic acid simulated propio

280

300

320

340

360

380

buty

ric

acid

(m

g/L

) butyric acid simulated butyri

130

140

150

160

170

180

190

200

vale

ric

acid

(m

g/L

)

valeric acid simulated valeri

0 10 20 30 40 50 60

Time (da

Fig. 4. Measured VAF value of CSTR com

acetic acid ethanol was much underpredicted. Compared to themeasured values, there was a deviation ratio of 13.8–23.2% in thesimulated results. As a consequence, the VFA content was under-predicted in this model. It is likely that ethanol present in theraw wastewater was converted into acetic acid during the acidifi-cation process, which was not considered by the model. It wasnoted that Parker (2005) showed that the concentrations of VFAswere consistently over-predicted in digesters at short solidretention times. These results seems to confirm the previous obser-vation that it was difficult to accurately predict the VFA concentra-tion with ADM1 model due to complicated behavior ofacidification process (Blumensaat and Keller, 2005).

4.4. The simulation result of pH

Previous studies indicated that the major products ofcarbohydrates acidogenesis are acetate, butyrate, propionate, andethanol, whose proportion is strongly dependent on culture pH(Fang and Liu, 2002). The effluent pH of CSTR was in a range of4.8–5.2, and the simulated pH was higher than the measured val-ues (deviation ratio, �5.8–�14.5%), as shown in Fig. 5. It is proba-bly due to the underpredicted VFA concentrations with the ADM1model.

The effluent pH of UASB fluctuated in a narrow range of 6.5–7.0since the acidified intermediates were fermented in the UASB(Fig. 6). The model well predicted the effluent pH of UASB with adeviation ratio of �2.9–1.4%, indicating that methanogenic bacte-ria grew well.

10

15

20

25

30

35

devi

atio

n ra

tiode

viat

ion

ratio

devi

atio

n ra

tiode

viat

ion

ratio

deviation ratio

deviation ratio

deviation ratio

deviation ratio acid measured

-80

-60

-40

-20

0

20

40

nic acid meatured

-30

-20

-10

0

10c acid meatured

-60

-40

-20

0

20c acid meatrued

70 80 90 100 110 120

ys)

parison with model simulated value.

Page 7: Modeling of two-phase anaerobic process treating traditional Chinese medicine wastewater with the IWA Anaerobic Digestion Model No. 1

3.5

4

4.5

5

5.5

pH

-15

-10

-5

0

5

devi

atio

n ra

tio

pH simulated pH meatured deviation ratio

0 10 20 30 40 50 60 70 80 90 100 110 120

Time (days)

Fig. 5. Measured pH of CSTR comparison with model simulated value.

6.4

6.5

6.6

6.7

6.8

6.9

7

7.1

7.2

pH

-5

-3

-1

1

3

5

7

9

devi

atio

n ra

tio

pH simulated pH meatured deviation ratio

0 10 20 30 40 50 60 70 80 90 100 110 120

Time (days)

Fig. 6. Measured pH of UASB comparison with model simulated value.

Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631 4629

5. Conclusions

ADM1 model was applied to the TCM wastewater treatmentwith TPAD process while considering complete mixing model forthe CSTR, and axial direction discrete model and mixed series con-nection model for the UASB. The simulation results indicated thatthe model built was able to well predict the COD removal rate(�4.8–5.0%) and pH variation (�2.9–1.4%) of the UASB reactor,while failed to simulate the CSTR performance. Comparing to themeasured results, the simulated acetic acid concentration of theCSTR effluent was underpredicted with a deviation ratios of13.8–23.2%, resulting in an underprediction of total VFA and CODconcentrations despite good estimation of propionic acid (devia-tion ratio, �6.5–7.1%), butyric acid (deviation ratio, �2.1–5.7%)and valeric acid (deviation ratio, �4.4–4.3%). It is presumed thatethanol present in the raw wastewater was converted into aceticacid during the acidification process, which was not consideredby the model. Additionally, due to the underprediction of aceticacid the pH of CSTR effluent was overestimated.

Acknowledgements

The authors are grateful to Research Center of EnvironmentalBiotechnology in Harbin Institute of Technology for their technical

and logistical assistance during this work which was supported byState Key Laboratory of Urban Water Resource and Environment(HIT-QAK200808) and China National ‘‘863” Hi-Tech R & D Pro-gram (Grant No. 2007AA06Z348).

Appendix A

VdSsu

dt¼ qSsu;in � qSsu þ khyd;chXch þ ð1� ffa;liÞkhyd;liXli

� km;suSsu

KS�su þ SsuXsu exp �3

pH � 5:51:5

� �2" #

ð4Þ

VdSaa

dt¼ qSaa;in � qSaa þ khyd;prXpr

� km;aaSaa

KS�aa þ SaaXaa exp �3

pH � 5:51:5

� �2" #

ð5Þ

VdSfa

dt¼ qSfa;in � qSfa þ ffa;likhyd;liXli

� km;faSfa

KS�fa þ SfaXfa exp �3

pH � 5:51:5

� �2" #

ð6Þ

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4630 Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631

VdSva

dt¼ qSva;in � qSva þ ð1� YaaÞfva;aakm;aa

Saa

KS�aa þ SaaXaa

� exp �3pH � 5:5

1:5

� �2" #

ð7Þ

VdSbu

dt¼ qSbu;in � qSbu þ ð1� YsuÞfbu;sukm;su

Ssu

KS�su þ SsuXsu

� exp �3pH � 5:5

1:5

� �2" #

�þð1� YaaÞfbu;aakm;aaSaa

KS�aa þ SaaXaa exp �3

pH � 5:51:5

� �2" #

ð8Þ

VdSpro

dt¼ qSpro;in � qSpro þ ð1� YsuÞfpro;sukm;su

Ssu

KS�su þ SsuXsu

� exp �3pH � 5:5

1:5

� �2" #

þ ð1� YaaÞfpro;aakm;aa

� Saa

KS�aa þ SaaXaa exp �3

pH � 5:51:5

� �2" #

ð9Þ

VdSac

dt¼ qinSac;in � qoutSac þ ð1� YsuÞfac;sukm;su

Ssu

KS�su þ SsuXsu

� exp �3pH � 5:5

1:5

� �2" #

þ ð1� YaaÞfac;aakm;aaSaa

KS�aa þ SaaXaa exp �3

pH � 5:51:5

� �2" #

þ 0:7ð1� YfaÞkm;faSfa

KS�fa þ SfaXfa exp �3

pH � 5:51:5

� �2" #

ð10Þ

VdSh2

dt¼ ð1� YsuÞfh2;sukm;su

Ssu

KS�su þ SsuXsu

� exp �3pH � 5:5

1:5

� �2" #

ð1� YaaÞfh2 ;aakm;aaSaa

KS�aa þ SaaXaa

� exp �3ðpH � 5:51:5

Þ2� �

þ 0:3ð1� YfaÞkm;faSfa

KS�fa þ SfaXfa

� exp �3ðpH � 5:51:5

Þ2� �

ð11Þ

VdSI

dt¼ �qwSI þ fsI;xckdisXc ð12Þ

VdXc

dt¼ �qwXc � kdisXc þ kdec;Xsu Xsu þ kdec;Xaa Xaa þ kdec;Xfa

Xfa ð13Þ

VdXch

dt¼ �qwXch þ fch;xckdis;chXc � khyd;chXch ð14Þ

VdXpr

dt¼ �qwXpr þ fpr;xckdisXc � khyd;prXpr ð15Þ

VdXli

dt¼ �qwXli þ fli;xckdisXc � khkd;liXli ð16Þ

VdXsu

dt¼ �qwXsu þ Ysukm;su

Ssu

KS�su þ SsuXsu

� exp �3pH � 5:5

1:5

� �2" #

� kdec;Xsu Xsu ð17Þ

VdXaa

dt¼ �qwXaa þ Yaakm;aa

Saa

KS�aa þ SaaXaa

� exp �3ðpH � 5:51:5

Þ2� �

� kdec;Xaaa Xaa ð18Þ

VdXfa

dt¼ �qwXfa þ Yfakm;fa

Sfa

KS�fa þ SfaXfa

� exp �3pH � 5:5

1:5

� �2" #

� kdec;XfaXfa ð19Þ

VdXi

dt¼ �qwXi þ fxl;xckdisXc ð20Þ

Appendix B

Vn

DLd2Sva

dl2 � qdSva

dl

!

¼ qSva;in � qSva � km;c4Sva

KS�Cþ4þ Sva

Xc41

1þ Sbu=Sva

� exp �3pH � 5:5

1:5

� �2" #

ð28Þ

Vn

DLd2Sbu

dl2 � qdSbu

dl

!

¼ qSbu;in � qSbu � km;c4Sbu

KS�Cþ4þ Sbu

Xc41

1þ Sva=Sbu

� exp �3pH � 5:5

1:5

� �2" #

ð29Þ

Vn

DLd2Spro

dl2 � qdSpro

dl

!

¼ qSpro;in � qSpro � km;prSpro

KS�pro þ SproXpro exp �3

pH� 5:51:5

� �2" #

þ 0:54ð1� Yc4Þkm;c4Sva

KS�Cþ4þ Sva

Xc41

1þ Sbu=Svaexp �3

pH� 5:51:5

� �2" #

ð30Þ

VnðDL

d2Sac

dl2 � qdSac

dlÞ

¼ qSac;in � qSac � km;acSac

KS�ac þ SacXac exp½�3ðpH�7Þ2�

þ 0:31ð1�Yc4Þkm;c4Sva

KS�Cþ4þ Sva

Xc41

1þ Sbu=Svaexp½�3ðpH� 5:5

1:5Þ2�

þ 0:8ð1�Yc4Þkm;c4Sbu

KS�Cþ4þ Sbu

Xc41

1þ Sva=Sbuexp½�3ðpH�5:5

1:5Þ2�

þ 0:57ð1�YproÞkm;prSpro

KS�pro þ SproXpro exp½�3ðpH�5:5

1:5Þ2� ð31Þ

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Z. Chen et al. / Bioresource Technology 100 (2009) 4623–4631 4631

Vn

DLd2Sh2

dl2 � qdSh2

dl

!

¼ 0:15ð1�Yc4Þkm;c4Sva

KS�Cþ4þ Sva

Xc41

1þ Sbu=Svaexp½�3ðpH�5:5

1:5Þ2�

þ0:2ð1�Yc4Þkm;c4Sbu

KS�Cþ4þ Sbu

Xc41

1þ Sva=Sbuexp½�3ðpH�5:5

1:5Þ2�

þ0:43ð1�YproÞkm;prSpro

KS�proþ SproXpro exp½�3ðpH�5:5

1:5Þ2�

� km;h2Sh2

KSþ Sh2Xh2 exp½�3ðpH�6Þ2� ð32Þ

Vn

DLd2Sch4

dl2 � qdSch4

dl

!¼ ð1� YacÞkm;ac

Sac

KS þ SacXac exp½�3ðpH�6Þ2�

þ ð1� Yh2Þkm;h2Sh2

KS þ Sh2Xh2

� exp½�3ðpH�5:51:5

Þ2� ð33Þ

Vn

DLd2Xc

dl2 � qdXc

dl

!¼ �qwXc þ kdec;Xc4

Xc4 þ kdec;Xpro Xpro

þ kdec;Xac Xac þ kdec;Xh2Xh2 ð34Þ

Vn

DLd2Xc4

dl2 � qdXc4

dl

!

¼�qwXc4 þ Yc4km;c4Sva

KS�Cþ4þ Sva

Xc41

1þ Sbu=Svaexp½�3ðpH�5:5

1:5Þ2�

þ Yc4km;c4Sbu

KS�Cþ4þ Sbu

Xc41

1þ Sva=Sbuexp½�3ðpH�5:5

1:5Þ2�

� kdec;Xc4 Xc4 ð35Þ

Vn

DLd2Xpro

dl2 � qdXpro

dl

!

¼ �qwXpro � kdec;Xac Xac þ Yprokm;prSpro

KS�pro þ SproXpro

� exp½�3ðpH � 5:51:5

Þ2� ð36Þ

Vn

DLd2Xac

dl2 � qdXac

dl

!

¼ �qwXac þ Yackm;acSac

KS�ac þ SacXac exp½�3ðpH � 7Þ2� � kdec;Xac Xac

ð37Þ

Vn

DLd2Xh2

dl2 � qdXh2

dl

!

¼ �qwXh2 þ Yh2km;h2Sh2

KS�H2 þ Sh2Xh2 exp½�3ðpH� 6Þ2� � kdec;Xh2

Xh2

ð38Þ

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