starch-based solid carbon and its influence on biodenitrification

Journal Pre-proof Carbon release behaviour of Polylactic acid/Starch-based solid carbon and its influence on biodenitrification Min Yang (Data curation) (Writing - original draft), Xiaona Wang (Supervision), Shu Liu (Conceptualization) (Methodology), Chuanfu WuModeling), Qunhui Wang (Writing - review and editing)

PII:

S1369-703X(19)30407-3

DOI:

https://doi.org/10.1016/j.bej.2019.107468

Reference:

BEJ 107468

To appear in:

Biochemical Engineering Journal

Received Date:

20 April 2019

Revised Date:

10 December 2019

Accepted Date:

12 December 2019

Please cite this article as: Yang M, Wang X, Liu S, Wu C, Wang Q, Carbon release behaviour of Polylactic acid/Starch-based solid carbon and its influence on biodenitrification, Biochemical Engineering Journal (2019), doi: https://doi.org/10.1016/j.bej.2019.107468

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Carbon release behaviour of Polylactic acid/Starch-based solid carbon and its influence on biodenitrification

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Min Yang a, Xiaona Wang a, Shu Liu b*, Chuanfu Wu a,c**, Qunhui Wang a,c

Department of Environmental Engineering, School of Energy and Environmental

Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian

b

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District, Beijing 100083, China

Department of Environmental Science and Engineering, School of Space and

c

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Environment, Beihang University, Beijing 10191, China

Beijing Key Laboratory on Resource-oriented Treatment of Industrial Pollutants,

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University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District,

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Beijing 10083, China

* Correspondence to: Shu Liu, Department of Environmental Science and Engineering,

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School of Space and Environment, Beihang University, Beijing 10191, China,

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TEL/FAX: +86-(010)-6171-6810, Email: [email protected] ** Correspondence to: Chuanfu Wu, Department of Environmental Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing

100083,

China,

TEL/FAX:

+86-(010)-6233-2778,

Email:

[email protected]

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Graphical abstract

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Highlights:

Carbon release behavior of PLA/starch-based solid carbon was investigated.

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Mass ratio of the blends is a key parameter governing the TOC release rate.



Hydrolysis contributed more to TOC released than the physicochemical

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

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leaching. 

Little TOC will accumulates in the effluent during the SPD process.

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The PLA/starch blend is an ideal solid-carbon source for nitrate removal.

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Abstract The carbon release behaviour of polylactic acid (PLA)/starch blends and the correlation between solid-phase denitrification (SPD) performance and solid-carbon decomposition were investigated by both batch experiments and numerical simulations. The results showed that the mass ratio of PLA to starch is a key parameter in

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determining the total organic carbon (TOC) release rate of the blends. Hydrolysis of the blends contributed more markedly to the fraction of TOC released during denitrification

compared with physicochemical leaching. The maximum denitrification rate of the

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blend-supported SPD system (PLA/starch mass ratio = 5 : 5) was 0.008 kg-N/(m3·h). Simulation results suggested that the denitrification performance of the SPD system was

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strongly affected by the fraction of its bioavailable solid carbon and also suggested that

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little TOC (mean value of 0.003 kg/m3) would be accumulated in the effluent. Therefore, the PLA/starch blend is a suitable solid-carbon source for nitrate removal because of its adaptability to environmental changes, stability of effluent quality and

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renewability.

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Keywords: Solid carbon, Polylactic acid, Starch, Denitrification, Numerical simulation.

Abbreviations C/N

Carbon to nitrogen

DOC

Dissolved organic matter

HRT

Hydraulic retention time 3

Mixed liquor suspended solids

PLA

Polylactic acid

SBR

Sequencing batch reactor

SPD

Solid-phase denitrification

TN

Total nitrogen

TOC

Total organic carbon

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MLSS

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1. Introduction

Excessive nitrogen discharge into surface water is a critical issue imperilling water

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ecosystems and drinking water security [1]. In the recently enforced provincial standard

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for municipal sewage discharge in Beijing, China, the TN concentration is limited to less than 10.0 mg/L (< 15.0 mg/L for China) in regions with sensitive aquatic ecology [2]. Similarly, the maximum admissible concentration of nitrate for drinking water is

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also 10.0 mg/L [3]. Therefore, to cope with the increasingly stringent emission

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standards, intensified nitrogen removal from wastewater is vital. Nitrogenous pollution in the effluents of traditional biological nitrogen removal

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processes and agricultural runoff is mainly in the form of nitrate, which is due to the deficiency of an available carbon source for denitrification [4-9]. Thus, external liquid-carbon sources, such as methanol, glucose and ethanol, are added in traditional post-biodenitrification treatment facilities to remove excess nitrate [4,10,11]. However, the risk of insufficient dosing or overdosing entails a deterioration of the effluent 4

quality, and therefore sophisticated and costly process control and continuous monitoring are required [6,12]. Currently, solid-phase denitrification (SPD), which uses water-insoluble solid materials (including natural organic materials, such as wood chips and maize cobs, and synthetic biodegradable polymers, such as polycaprolactone, 3-hydroxybutyrate-3-hydroxyvalerate,

polyhydroxyalkanoate

and

polylactic

acid

(PLA)) as carbon sources for denitrifiers and biofilm carriers, has been proven to be a

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promising alternative for removing nitrates from water with a low C/N ratio (e.g. groundwater and drinking water recirculated aquaculture systems) because of its advantages, including avoiding the risks of overdosing/insufficient dosing, simple

process control, constant supply of reducing power and less organic pollution in the

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form of biopolymers [6,13-17].

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Developing favourable solid carbons with low cost and high bioavailability and without deteriorating effluent quality is an enormous challenge for SPD. Compared with

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biodegradable polymers, natural organic materials are cheap and widely available, but they tend to result in excess dissolved organic matter (DOC) in the effluent, especially

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during the start-up period [6,18]. On the other hand, blending synthetic biodegradable polymers with cheap organic matter (e.g. starch and bamboo powder) is a potential way

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to reduce the overall cost of the solid-carbon source, thereby making SPD a

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commercially competitive and practical technology for tertiary denitrification [19-20]. Moreover, blending biopolymers with starch may dramatically stimulate the biodegradability of the blends, resulting in a higher denitrification rate, longer life span and shorter start-up periods [21-26]. However, inappropriate mass ratios of biopolymers to starch for specific wastewaters may lead to an increase of DOC in the effluent, thus deteriorating the quality of the effluent [23-24]. 5

Despite massive researches have been performed to exploit the denitrification potentialities of various biopolymers, there are relatively few commercial nitrogen contaminated wastewater treatment systems using solid-carbon as electron donor have been constructed. The added complexity and expense of monitoring and operating commercial systems have so far counteracted its advantages (e.g. low price, little organic pollution, etc.). To reduce the added complexities of the SPD systems,

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comprehensively evaluating the correlation between the organic carbon release behaviours and the properties of the solid-carbon is crucial as the organic carbon release rate of the solid carbons are inextricably linked to the denitrification performance of the

systems. According to the documented literatures, in SPD systems, the solid-carbon

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source is firstly hydrolysed by extracellular enzymes excreted by the microbes in the

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attached biofilm into soluble small-molecule substrates, which in turn are utilised by the denitrifiers [6]. Therefore, the DOC levels of the aqueous phase are regulated by the

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processes of physicochemical dissolution (i.e. leaching) and solid-carbon hydrolysis, as well as DOC consumption by the microbes (denitrifiers, methanogens, aerobic bacteria

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and fungi, etc.). However, a good understanding and comprehensive quantification of DOC dissolution and consumption in SPD systems is lacking in the literature. In

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addition, due to the dynamic changes of solid carbon upon decomposition, the formulation of biodenitrification performance and effluent qualities with the

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characteristics of the solid carbon has yet to be sufficiently constructed. An in-depth understanding of the influence of organic carbon release behaviours of a

specific solid-carbon on the biodenitrification performance is fundamentally important for the prevention of overdosing/insufficient dosing of solid carbon in SPD systems and consequently minimize the cost for its operating and monitoring. In this study, the 6

organic carbon release behaviours (i.e. including the physicochemical dissolution, hydrolysis and DOC consumption behaviours) of solid carbons comprising PLA ( a biodegradable and non-toxic material) and starch (a cheap, widely available and easily degradable material) is a potential way to reduce the overall cost of the solid-carbon source) with different mass ratios were firstly investigated by laboratory batch scale experiments. In addition, a mechanistic model was developed and the correlations

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between the denitrification performance and PLA/starch blend decomposition in the batch scale experiments were further evaluated by numerical simulations. The results

obtained and the model developed here may lay the foundation for the optimal design of

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SPD systems and provide a scientific basis for further studies.

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2.1. Preparation of the Blends

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2. Materials and Methods

The solid carbon (i.e. PLA/starch blends) used in this study was synthesised by biodegradable polymer PLA and starch. The molecular weight of the PLA was 8,000

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Da. It was purchased from Shenzhen Guanghua Co., Ltd. (China). The densities of PLA and starch (Beijing Weimeifa Food Co., Ltd., Beijing, China) are ca. 1,200 and 1,500

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kg/m3, respectively. For specific blend preparation, certain amounts of PLA and starch

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were added into a high-speed grinder (HY-04B, Beijing Huanya Tianyuan Co., Ltd., Beijing, China) for comminution and mixing. The homogenous mixture was added into a the heated barrel of a plastic extruder (JP6C-9, Beijing Yingte Plastic Factory, Beijing, China). The temperature of the preheated section was ca. 130°C. The mixture was moved forward melting section by the rotation of the screw at rpm/min. the temperature 7

of the melting section was ca. 160°C. A solidified cylinder with a diameter of 0.3 cm could be obtained in the cooling section (ca. 130°C). The blends were then cut into 2 cm lengths after completely cooling to room temperature before use. 2.2. Experimental Setup 2.2.1. Organic Carbon Leaching of the Blends

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Ten grams of the blends with different mass ratios of PLA to starch (i.e. P : S = 4 : 6, 5 : 5 and 6 : 4) was added to 500 mL Erlenmeyer flasks, followed by the addition of 400 mL of deionised water. The carbon release rate of the blends with other mass ratios of

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PLA to starch (i.e. P : S = 8 : 2, 7 : 3 and 3 : 7) have been proved to be inappropriate in

our previous study [20]. The pH values of the solutions were adjusted to given values

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(i.e. pH = 6.5, 7.5 and 8.5) by adding a 1 M NaOH/HCl solution. The flasks were then sealed and placed in a thermostatic shaking incubator operating at 70 rpm and 25 ± 1°C.

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The liquid samples were sampled at different time points (2.5 mL per sampling) and the total organic carbon (TOC) content was analysed to investigate the carbon-releasing

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behaviour of the blends.

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2.2.2. Hydrolysis of the Blends

Ten grams of the blends with different mass ratios of PLA to starch (produced

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according to the methodology described in Section 2.1) was added to 500 mL Erlenmeyer flasks, followed by the addition of 350 mL of synthetic wastewater and 50 mL of aged activated sludge. The activated sludge was collected from a local municipal wastewater treatment plant and had an MLSS concentration of ~800 mg/L. The activated sludge was accommodated in a 10 L bucket for more than three months by 8

adding blends of PLA/starch (5 : 5), NO3−-N (0.2 kg/m3), PO43−-P (0.01 kg/m3), NH4+-N (0.002 kg/m3) and trace element solution. The HRT of the accommodation system were 3 days. The MLSS concentration of the accommodated activated sludge was 2,000– 3,000 mg/L. Before adding to the flasks as mentioned above, the seeded sludge was concentrated to gain an MLSS concentration of 4,500–5,500 mg/L. The concentrations of PO43−-P and NH4+-N of the synthetic wastewater were controlled to be 0.01 kg-P/m3

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and 0.002 kg-N/m3 by adding KH2PO4 and NH4Cl, respectively. The pH values of the solutions were adjusted to given values (i.e. pH = 6.5, 7.5 and 8.5) by adding a 1 M

NaOH/HCl solution. The flasks were filled with nitrogen gas to achieve anaerobic conditions. Each flask was then sealed and placed in a thermostatic shaking incubator

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operating at 70 rpm and 25 ± 1°C. The liquid samples were sampled at different time

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hydrolysis behaviour of the blends.

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points (2.5 mL per sampling) and the TOC content was analysed to unveil the

2.2.3. Denitrification Performance of the Blends

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The experimental protocol was the same with the solid-carbon hydrolysis experiment mentioned above, except that the concentration of NO3−-N in the synthetic wastewater

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was controlled to predesignated values (i.e. 0.05, 0.15, 0.25 and 0.35 kg/m3) by adding NaNO3, and the liquid samples were sampled at different time points (2.5 mL per

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sampling) and analysed for NH4+-N, NO2−-N, NO3−-N, TOC and pH. 2.3. Analytical Methods The liquid samples were filtered through a 0.45 μm thick membrane before analysis. The NH4+-N, NO2−-N and NO3−-N in the filtered liquid were assayed in accordance with 9

the Water and Wastewater Monitoring Analysis Method [27]. The TOC contents of the liquid were measured using a TOC analyser (Vario TOC, Elementar, Germany). The pH value was determined using a pH meter (pHs-3C, Shanghai DaPuYiQi Co., Ltd., Shanghai, China). All experiments were performed in triplicate, and averages of the three experiments are reported. 2.4. Mechanistic model Development

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2.4.1. Model Theoretical Framework

In this study, a mechanistic model was developed to investigate the correlation

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between solid-carbon decomposition and the denitrification performance. The model

integrates multiple biochemical processes (a schematic representation of all the

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processes can be found in Fig. 1), including physicochemical dissolution, solid-carbon hydrolysis and DOC consumption by the denitrifiers. In this model, the SPD system is

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fractionated into three phases: a hardly degradable solid-carbon phase, an easily degradable solid-carbon phase and a liquid phase. The hardly degradable and easily

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degradable solid carbon is firstly decomposed into DOC in the liquid phase by anaerobic hydrolysis bacteria, and the DOC is in turn decomposed into N2 and CO2 by

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Fig. 1

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the denitrifiers in the liquid phase.

Several important assumptions were made during the construction of this model,

which are as follows: (1) the volumetric ratio of each phase is constant, even if the organic matter in the hardly and easily degradable solid-carbon phases has been decomposed; (2) decayed bacterial mass is immediately transformed into the form of 10

hardly degradable solid carbon and it does not wash out during the drainage operation; (3) in batch scale experiment, we assumed that the shaking incubator operating at 70 rpm could homogenize the TOC once it was released from the blends through dissolution and hydrolysis. Therefore, convection and diffusion terms were ignored during the numerical simulations; (4) the DOC can only be used by the denitrifiers, considering that the methanogenic bacteria cannot compete with the denitrifiers for

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carbon sources when the level of nitrate is sufficiently high [28]; (5) the amount of active bacteria per unit area of solid carbon is constant (i.e. considered as their

saturation concentrations); the concentration of bacteria may increase in each batch experiment, but it will decrease to the set concentration during drainage (i.e.

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deactivation); conversely, when the concentration of bacteria is lower than the set

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concentration in each batch experiment, the drainage operation will not affect its

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concentration; (6) the nitrate conservation coefficient for the denitrifiers is negligible. 2.4.2. Kinetic Model of the Process

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In this model, the specific growth rates of different bacteria are expressed by the Monod

equations

CC ,Shar

KC , Shar  CC , Shar CC , Seas

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R Shar  max, Shar

KC , Seas  CC ,Seas

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R Seas  max,Seas

R NO3  max, NO3

CNO3 ,l

as

follows:

X Shar ,l ,

(1)

X Seas ,l ,

(2)

CC ,l

K NO3  CNO3 ,l KC , NO3  CC ,l

X NO3,l .

(3)

Here, Ri (kg-cell/m3/h) is the specific growth rate of bacteria i (i.e. XShar, XSeas and XNO3); μmax,i is the maximum growth rate of bacteria i (i.e. XShar, XSeas and XNO3); CC,Shar 11

(kg-C/m3) and CC,Seas (kg-C/m3) are the concentrations of organic carbon in hardly and easily degradable solid-carbon phases, respectively; CC,l (kg-C/m3) and CNO3,l (kg-N/m3) are the concentrations of organic carbon and nitrate in the liquid phase, respectively; Kj,i (kg-substrate/m3) is the half-saturated coefficient of each substrate j (j = C and NO3) for bacteria i (i.e. XShar, XSeas and XNO3); Xi (kg-cell/m3) is the concentration of bacteria i in the liquid phase (i.e. XShar, XSeas and XNO3).

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The mass balance equations for different bacteria and substrates in solid and liquid phases are expressed as follows: X i  Ri  K d ,i X i , t

CC , Seas t CC ,l t



 Shar YC , X , Shar

X Shar ,1 

l

 Shar

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l 

 Seas

1 1    YC ,Seas YC , X ,Seas

  R Seas , 

(5)

(6)

 Shar  C , Shar CC , Shar (CCeq ,l  CC ,l )  Seas C ,Seas CC ,Seas (CCeq ,l  CC ,l ) l l 

R Seas

 1 1     R NO3, Y  C , NO3 YC , X , NO3 

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R Shar

YC , Shar



1

YNO3

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t

K d , Shar

  C , Seas CC , Seas (CCeq ,l  CC ,l ) 



CNO3 ,l

l

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

l 

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t

1 1    R  Shar  YC , Shar YC , X , Shar  Shar K d , Seas  K d , NO3 X Seas ,1  l X , YC , X , Seas  Shar YC , X , NO3 NO3,1

 C , Shar CC , Shar (CCeq ,l  CC ,l ) 

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CC , Shar

(4)

YC , Seas

R NO3 .

(7)

(8)

Here, Kd,i (1/h) is the specific decay rate of bacteria i (i.e. XShar, XSeas and XNO3);

βC,Shar (m3/kg/h) and βC,Seas (m3/kg/h) are the physiochemical dissolution coefficients for hardly and easily degradable solid carbon, respectively; CCeq,l (kg-C/m3) is the 12

equilibrium concentration of organic carbon in the liquid phase; εl (–), εShar (–) and εSeas (–) are the volumetric ratios of the liquid phase, hardly degradable solid-carbon phase and easily degradable solid-carbon phase, respectively; Yj,i (kg-cell/kg-substrate) and Yj,X,i (kg-cell/kg-substrate) are the transformation and conservation coefficients of different substrates (j = C and NO3) for bacteria i (i.e. XShar, XSeas and XNO3). Mathematical modelling was set up on the basis of the aforementioned equations.

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The numerical solution to this model is obtained using a finite difference approach, which comprises a set of nonlinear discrete balance equations, coupled with the conservation equations, etc.

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2.4.3. Model Inputs

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There are two categories of data in this model that must be clarified: initial values and kinetic parameters. According to experimental measurements, the volumes of water,

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easily degradable solid carbon and hardly degradable solid carbon were 400, 4.2 and 3.3 mL, respectively. Therefore, the volumetric ratios of different phases can be

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determined, which are listed in Table 1. The concentrations of different bacteria were estimated by measuring the MLSS of the seeded activated sludge and assuming that the

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concentration of total living bacteria was half the MLSS. Detailed information on the

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initial values of all the related components can be found in Table 1. Table 1

3. Results and Discussion

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3.1. Experimental Study of the Carbon Release Behaviour of PLA/Starch Blends and Its Influence on the Denitrification Performance 3.1.1. Leaching Behaviour of the PLA/Starch Blends Figure 2(a) presents the physicochemical TOC dissolution of the PLA/starch blends with different mass ratios at various pH values. The investigated solution pH values did not cause significantly different impacts (p > 0.05) on TOC leaching from the

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PLA/starch blends. The amounts of TOC released by the blends after 312 h of leaching

were on average 230, 75.2 and 70.4 mg for P : S ratios of 4 : 6, 5 : 5 and 6 : 4, respectively, indicating that a high PLA fraction corresponds to a low amount of

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released carbon. The organic carbon release rate of the blends was high in the first 100 h

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but dropped drastically to a lower and more stable level from then on. The leaching behaviour of the blends is similar to the observation by [31]. The average TOC release

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rates in the rapid (0–100 h) and stable (100–312 h) states were 0.17 and 0.03 mg/(g·h) for the blends of P : S = 4 : 6, 0.05 and 0.01 mg/(g·h) for the blends of P : S = 5 : 5 and

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0.06 and 0.005 mg/(g·h) for the blends of P : S = 6 : 4, respectively. These results indicated that TOC leaching from the blends was a relatively slow and stable process

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and that the mass ratio of PLA to starch was a key parameter in determining the TOC release rate of the blends. The rapid release of carbon from the blends at the very

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beginning of the leaching experiment is also noteworthy and can probably be attributed to the dissolution of unpolymerised small organic molecules on the surface of the blends.

3.1.2. Hydrolysis Behaviour of the PLA/Starch Blends

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Figure 2(b) presents the hydrolysis behaviour of the PLA/starch blends with different mass ratios at various pH values. The investigated pH values of the solutions did not cause significantly different impacts (p > 0.05) on the hydrolysis behaviour of the PLA/starch blends. The TOC release amounts of the blends after 286 h of the hydrolysis experiment were on average 905.7, 612.0 and 523.2 mg for P : S ratios of 4 : 6, 5 : 5 and 6 : 4, respectively, indicating that a high starch content promoted TOC release by

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hydrolysis. The organic carbon release rate of the blends was high in the first 76 h but dropped drastically to a lower and more stable level from then on. The average TOC

release rates in the rapid (0–76 h) and stable (76–286 h) states were 0.76 and 0.15

mg/(g·h) for the blends of P : S = 4 : 6, 0.38 and 0.15 mg/(g·h) for the blends of P : S =

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5 : 5 and 0.30 and 0.14 mg/(g·h) for the blends of P : S = 6 : 4, respectively. Therefore,

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the TOC release rates, induced only by the hydrolysis effect, of the rapid and stable states were 0.59 and 0.13 mg/(g·h) for the blends of P : S = 4 : 6, 0.33 and 0.14 mg/(g·h)

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for the blends of P : S = 5 : 5 and 0.24 and 0.13 mg/(g·h) for the blends of P : S = 6 : 4, respectively. These results suggested that the TOC release rates induced by hydrolysis

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in the stable state were 4.4, 12.0 and 28.8 times higher than those induced by physicochemical dissolution for the blends with P : S ratios of 4 : 6, 5 : 5 and 6 : 4,

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respectively.

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3.1.3. Denitrification Performance of the PLA/Starch Blends The influences of pH and the mass ratio of the PLA/starch blends on the

denitrification performance of the SPD system were investigated at a nitrate concentration of 0.25 kg-N/m3. As shown in Fig. 2(c), the investigated pH values of the solutions did not cause significantly different impacts (p > 0.05) on the denitrification 15

rates of the blend of P : S = 4 : 6, indicating that the negative impact of the pH on the denitrification performance can be balanced by the promotion of the available organic carbon. Conversely, the pH values of the solutions significantly (p < 0.05) affected the denitrification rates in SPD systems with the blends of P : S = 5 : 5 and 6 : 4. Generally, suitable pH values for SPD are reported to range from 6.5 to 8.5, with the optimum value lying between 7.0 and 8.0 [6,32]. Our results are basically consistent with this

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observation, and we further found that the alkaline conditions can promote the denitrification performance of SPD systems, especially under cases of limited accessible DOC. This is probably attributed to the stimulation of the denitrifier activity under alkaline conditions. In the case of nearly 100% nitrate removal conditions, the

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denitrification rate of the SPD system with P : S of 4 : 6 was 0.013 kg-N/(m3·h) over the

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whole investigated pH range. On the other hand, the denitrification rates of the SPD system with P : S of 5 : 5 were 0.005, 0.005 and 0.008 kg-N/(m3·h) for pH of 6.5, 7.5

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and 8.5, respectively, which were similar to the denitrification rates with P : S of 6 : 4, except for the case of a pH of 6.5. In this study, the PLA/starch blend-supported SPD

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systems were comparable with the results found in other SPD systems using different synthetic biodegradable polymers as carbon sources (as shown in Table 2) in terms of

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the denitrification rates values. Notably, the denitrification rates observed in the continuous flow SPD systems were higher than in batch test, suggesting that the

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denigrification potentialities of the blends created in this study could be further improved. Regardless, the denigrification performance of the blends we created was better than the blends with similar carbon source (i.e. PLA and starch) in batch test [20]. In addition, the NO2−-N of the effluent was always below 0.0005 kg-N/m3 and no accumulation of NH4+-N was observed (data not shown), indicating that the route 16

involving dissimilatory nitrate reduction to ammonium did not occur in our SPD systems. Figure 2(c) also presents the TOC evolution during the SPD process under various conditions. Although the denitrification rate for the blend of P : S = 4 : 6 was high, the TOC concentration in the effluent was markedly higher than that with other treatments, thus deteriorating the quality of the effluent. Conversely, the denitrification rate of SPD

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systems with the blend of P : S = 6 : 4 might be insufficient for this specific wastewater. Therefore, the blend of P : S = 5 : 5 is probably a more suitable solid-carbon source for denitrification, in terms of its denitrification rate and the quality of the effluent. This

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conclusion is similar to that of our previous study, in which the blend of P : S = 5 : 5 was applied for nitrate-contaminated (NO3−-N = 0.05 kg-N/m3) groundwater remediation

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[20].

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Fig. 2 Table 2

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blends in water.

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3.2. Contribution of different biochemical processes on carbon release amount of the

In SPD systems, the release of DOC from biodegradable polymers is necessary for

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microbial growth and denitrification. However, the DOC accumulates once the release of DOC exceeds the amount required by denitrification. We expected that the release of DOC from biodegradable polymers could be regulated by the response of bacteria to nitrate levels in the liquid phase and therefore the risk of overdosing could be prevented. Technically, there are two types of denitrifiers: one is the biodegradable 17

polymer-degrading type of denitrifier and the other can only perform denitrification, and the compositions of the denitrifiers might be distinct, arising from different carbon substrates [6,36]. Therefore, clarifying the contributions of leaching, hydrolysis and denitrification processes to the carbon release amount from biodegradable polymers is essential for optimising the design of SPD systems. Figure 3 presents the contributions of different processes to the carbon release amount of the blends at a pH of 7.5. The TOC release amounts were 0.22 g in the first 20 h of the denitrification experiment for

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the blend of P : S = 4 : 6 (i.e. the nitrate removal rate reached 100% at this time point, as

shown in Fig. 2(c)) and 0.17 and 0.14 g for the blends of P : S = 5 : 5 and 6 : 4 in the 50 h denitrification experiment, respectively. The TOC release rates of the blends of P : S =

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4 : 6, 5 : 5 and 6 : 5 under SPD conditions were 1.1, 0.76 and 0.6 mg/(g·h), respectively.

re

Notably, physicochemical dissolution and denitrification contributed markedly to the TOC release amount of the blend of P : S = 4 : 6, whereas hydrolysis contributed a large

lP

fraction to the TOC release amounts of the blends of P : S = 5 : 5 and 6 : 4, indicating that the carbon released by the blends from less easily degradable solid-carbon fractions

na

might be induced by denitrifiers that are capable of both degrading polymers and denitrification. Another reasonable explanation is that the hydrolysis step might be the

ur

rate-limiting step since the denitrification effect contributed only a small fraction to the TOC from the blends with less easily degradable solid-carbon fractions (i.e. P : S = 5 : 5

Jo

and 6 : 4). On the other hand, in the SPD system with a large and easily degradable solid-carbon fraction, the leached amount of TOC is sufficient for the bacteria in the liquid phase, which may hinder the hydrolysis of solid carbon, and polymer-degrading denitrifiers may exist in this SPD system. Regardless, the amount of TOC accumulated in the effluent increases with increasing starch fraction of the blends. 18

Fig. 3 3.3. Numerical simulation of the correlation between PLA/starch blend decomposition and its denitrification performance Given that the solid substrate is degraded as a carbon source, the amount of usable organic compounds in the solid substrate will decrease with operation time, thus degrading the denitrification performance. Therefore, studying the mass reduction rate

ro of

of solid-carbon materials and their denitrification performance to determine their

renewal period is necessary for the continuation of SPD [6]. In this study, a mechanistic model was developed and used to simulate the correlation between the PLA/starch

-p

blend decomposition and its denitrification performance.

re

The developed mechanistic model was sequentially calibrated by the experimental leaching data, hydrolysis and batch denitrification experiments (see Fig. S1 of the

lP

Supporting Information). The obtained results indicated that the mechanistic model could basically simulate the PLA/starch blend-supported SPD process. To investigate

na

the denitrification performance of the blend of P : S = 5 : 5 and examine the accuracy of the model, batch experiments were performed under various nitrate concentrations. As

ur

shown in Fig. 4, the required time for full nitrate removal in the influent with NO3−-N concentrations of 0.05, 0.15, 0.25 and 0.35 kg-N/m3 was 10, 20, 45 and 70 h,

Jo

respectively. Notably, in the batch experiments, a high initial nitrate concentration (e.g. NO3−-N = 0.25 and 0.35 kg-N/m3) tended to increase the TOC concentrations of the effluent, especially when the NO3−-N concentration was reduced to a relatively low level, indicating that denitrification might be nitrate-limited when the nitrate concentration is low [37-38]. However, this issue could be properly handled in continual 19

experiments by controlling the superficial hydraulic velocity of the wastewater. Furthermore, the accuracy of the calibrated mechanistic model was examined using these experimental data, and the results demonstrated that the developed model could give a comparatively accurate prediction of nitrate removal in SPD systems for nitrate concentrations below 0.35 kg-N/m3 (see Fig. S2 of the Supporting Information). However, notably, it is difficult to fit simulation results to experimental data using only

ro of

the parameter values in the literature. Therefore, the values of some parameters were obtained after a literature review, which were used to select values that were physically

and biologically reasonable within the framework of the model. The remaining parameter values were obtained by curve fitting. In this study, most of the

-p

transformation coefficients, especially for unionised substances, were assumed since

re

they have not been reported in the literature with similar model construction assumptions. Therefore, quantification and verification of the coefficients with

lP

uncertain values in the model are necessary in future studies. The default kinetic

na

parameter values used in the model are listed in Table 1.

ur

Fig. 4

Sequencing batch reactor (SBR) technology has been widely used for wastewater

Jo

treatment. In this study, the calibrated model was used to evaluate the influence of the decomposition of blends on their denitrification performance in an SBR scenario (see Fig. S3 of the Supporting Information). Figure 5 presents the simulation results of the correlation

between

SPD

performance

and

solid-carbon

decomposition

in

semicontinuous experiments. The results show that the denitrification performance of 20

the SPD system was strongly affected by the fraction of its bioavailable solid carbon. The required HRT for denitrification increased as the concentration of easily degradable solid carbon decreased when the target NO3−-N in the effluent settled. The SPD system could treat 32, 11 and 7 batches of wastewater with influent NO3−-N concentrations of 0.05, 0.15 and 0.25 kg-N/m3 (the targeted nitrate concentration in the effluent was 0.01 kg-N/m3). According to the simulations, the problem with the longevity of the blends

ro of

treating polluted wastewater with different nitrate concentrations can be solved by considering the comprehensive analysis of the required HRT and the cost of

replacement of the blends. For the specific experiment scenario developed in this study,

the highest required HRT for NO3−-N concentrations of 0.05, 0.15 and 0.25 kg-N/m3 in

-p

the first 60 days of the experiments were 97, 306 and 602 h, respectively. Additionally,

re

the maximum TOC found in the effluent during the start-up period was ~0.025 kg-C/m3, and this only slightly increased as the experiment proceeded. Moreover, the PLA/starch

lP

blends would not collapse even when the starch in the blends was exhausted (i.e. the PLA served as the skeleton of the blends; see Fig. S4 of the Supporting Information),

na

which is beneficial for the attachment of the denitrifiers. Furthermore, the PLA skeleton can be reused as raw material for regeneration of new blends through the method

ur

described in Section 2.1. Therefore, the PLA/starch blend is a suitable solid-carbon source and biofilm carrier for nitrate removal due to its adaptability to environmental

Jo

changes, less deterioration of effluent quality and renewability. Fig. 5

4. Conclusions

21

The carbon release behaviour of PLA/starch blends and the correlation between SPD performance and solid-carbon decomposition were investigated by both batch experiments and numerical simulations. The mass ratio of PLA to starch was a key parameter in determining the TOC release rates of the blends. Hydrolysis of the blends contributed more markedly to the fraction of TOC produced during denitrification than physicochemical leaching. Simulation results suggested that the denitrification performance of the SPD system was strongly affected by the fraction of its bioavailable

ro of

solid carbon and also suggested that little TOC (mean value of 0.003 kg/m3) would be accumulated in the effluent.

-p

Author contribution

Yang Min: Data curation, Writing- Original draft preparation. Wang Xiaona:

re

Supervision. Liu Shu: Conceptualization, Methodology. Wu Chuanfu: Modeling. Wang

na

Declaration of interests

lP

Qunhui: Writing- Reviewing and Editing.

The authors declare that they have no known competing financial interests or personal

Jo

ur

relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

22

The authors wish to acknowledge the financial support provided by the National Key Research and Development Program of China (2018YFC1900103) and the National

Jo

ur

na

lP

re

-p

ro of

Natural Science Foundation of China (grant number 41505124).

23

References

Jo

ur

na

lP

re

-p

ro of

[1] Ryther, J.H., Dunstan, W.M., 1971. Nitrogen, phosphorus, and eutrophication in the coastal marine environment. Science 171, 1008–1013. [2] BMEPB (Beijing Municipal Environmental Protection Bureau), BMAQTS (Beijing Municipal Administration of Quality and Technology Supervision), 2012. Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant. Beijing, China. [3] National Standards of the People’s Republic of China, 2006. Standards for Drinking Water Quality (GB 5749-2006), People’s Republic of China, Beijing, China. [4] Shi, Y., Wu, G., Wei, N., Hu, H., 2015. Denitrification and biofilm growth in a pilot-scale biofilter packed with suspended carriers for biological nitrogen removal from secondary effluent. J. Environ. Sci. China 32, 35–41. [5] Li, P., Zuo, J., Wang, Y., Zhao, J., Tang, L., Li, Z., 2016. Tertiary nitrogen removal for municipal wastewater using a solid-phase denitrifying biofilter with polycaprolactone as the carbon source and filtration medium. Water Res. 93, 74–83. [6] Wang, J., Chu, L., 2016. Biological nitrate removal from water and wastewater by solid-phase denitrification process. Biotechnol. Adv. 34, 1103–1112. [7] Lu, Y., Zhang, X., Feng, L., Yang, G., Zheng, Z., Liu, J., Mu, J., 2017. Optimization of continuous-flow solid-phase denitrification via coupling carriers in enhancing simultaneous removal of nitrogen and organics for agricultural runoff purification. Biodegrad. 28, 275–285. [8] Feng, L., Chen, K., Han, D., Zhao, J., Lu, Y., Yang, G., Mu, J., Zhao, X., 2017. Comparison of nitrogen removal and microbial properties in solid-phase denitrification systems for water purification with various pretreated lignocellulosic carriers. Bioresour. Technol. 224, 236–245. [9] Xu, Z., Chai, X., 2017. Effect of weight ratios of PHBV/PLA polymer blends on nitrate removal efficiency and microbial community during solid-phase denitrification. Int. Biodeterior. Biodegrad.116, 175–183. [10] Modin, O., Fukushi, K., Yamamoto, K., 2007. Denitrification with methane as external carbon source. Water Res. 41, 2726–2738. [11] Chen, H.B., Wang, D.B., Li, X.M., Yang, Q., Zeng, G.M., 2015. Enhancement of post-anoxic denitrification for biological nutrient removal: effect of different carbon sources. Environ. Sci. Pollut. Res. 22, 5887–5894. [12] Shen, Z.Q., Yin, Y., Wang, J.L., 2016. Biological denitrification using poly (butanediol succinate) as electron donor. Appl. Microbiol. Biotechnol.100, 6074– 6053. [13] Gutierrez-Wing, M.T., Malone, R.F., Rusch, K.A., 2012. Evaluation of polyhydroxybutyrate as a carbon source for recirculating aquaculture water denitrification. Aquac. Eng. 51, 36–43. [14] Yang, X., Jiang, Q., Song, H., Gu, T., Xia, M., 2015. Selection and application of agricultural wastes as solid carbon sources and biofilm carriers in MBR. J. Hazard. Mater. 283, 186–192. 24

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[15] Chu, L.B., Wang, J.L., 2016. Denitrification of groundwater using PHBV blends in packed bed reactors and the microbial diversity. Chemosphere 155, 463–470. [16] Xu, Z., Dai X., Chai X., 2018. Effect of influent pH on biological denitrification using biodegradable PHBV/PLA blends as electron donor. Biochem. Eng. J. 131, 24-30 [17] Xu, Z., Dai X., Chai X., 2019. Biological denitrification using PHBV polymer as solid carbon source and biofilm carrier. Biochem. Eng. J. 146, 186-193 [18] Cameron, S.G., Schipper, L.A., 2012. Hydraulic properties, hydraulic efficiency and nitrate removal of organic carbon media for use in denitrification beds. Ecol. Eng. 41, 1–7. [19] Tokiwa, Y., Calabia, B., Ugwu, C., Aiba, S., 2009. Biodegradability of plastics. Int. J. Mol. Sci. 10, 3722–3742. [20] Wu, C., Tang, D., Wang, Q., Wang, J., Liu, J., Guo, Y., Liu, S., 2015. Comparison of denitrification performances using PLA/starch with different mass ratios as carbon source. Water Sci. Technol. 71, 1019–1025. [21] Hiraishi, A., Khan, S.T., 2003. Application of polyhydroxyalkanoates for denitrification in water and wastewater treatment. Appl. Microbiol. Biotechnol. 61,103–109. [22] Zhang, J., Feng, C., Hong, S., Hao, H., Yang, Y., 2012. Behavior of solid carbon sources for biological denitrification in groundwater remediation. Water Sci. Technol. 65, 1696–1704. [23] Shen, Z.Q., Zhou, Y.X., Wang, J.L., 2013. Comparison of denitrification performance and microbial diversity using starch/polylactic acid blends and ethanol as electron donor for nitrate removal. Bioresour. Technol. 131, 33–39. [24] Shen, Z.Q., Zhou, Y.X., Hu, J., Wang, J.L., 2013. Denitrification performance and microbial diversity in a packed-bed bioreactor using biodegradable polymer as carbon source and biofilm support. J. Hazard Mater. 250,431–438 [25] Shen, Z.Q., Hu, J., Wang, J.L., Zhou, Y.X., 2015. Biological denitrification using starch/polycaprolactone blends as carbon source and biofilm support. Desalin. Water Treat. 54, 609–615. [26] Shen, Z.Q., Hu, J., Wang, J.L., Zhou, Y.X., 2015. Comparison of polycaprolactone and starch/polycaprolactone blends as carbon source for biological denitrification. Int. J. Environ. Sci. Technol. 12, 1235–1242. [27] SEPA., 2002. Water and Wastewater Monitoring Analysis Method, 4th edn. China Environmental Science Press, Beijing, China. [28] Schipper, L.A., Cameron, S.C., Warneke, S., 2010. Nitrate removal from three different effluents using large-scale denitrification beds. Ecol. Eng. 36, 1552–1557. [29] Wiesmann, U., 1994. Biological nitrogen removal from wastewater. Adv. Biochem. Engin. Biotechnol. 51, 113–154. [30] Pollard, P.C., Greenfield, P.F., 1997. Measuring in situ bacterial specific growth rates and population dynamics in wastewater. Water Res. 31, 1074–1082. [31] Zhang, Q., Ji, F., Xu, X., 2016. Effects of physicochemical properties of poly-e-caprolactone on nitrate removal efficiency during solid-phase denitrification. Chem. Eng. J. 283, 604–613. [32] Hiscock, K.M., Lloyd, J.W., Lerner, D.N., 1991. Review of natural and artificial denitrification of groundwater. Water Res. 25, 1099–1111. [33] Li, H., Zhang, J., Zhou, Z., Liu, Q., Dong, H., Duan, Y., Li, C., 2017. A green porous solid carbon source supports denitrification in low C/N salinity wastewater.

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RSC Adv. 7, 18305–18310. [34] Zhu, S.M., Deng, Y.L., Ruan, Y.J., Guo, X.S., Shi, M.M., Shen, J.Z., 2015. Biological denitrification using poly (butylene succinate) as carbon source and biofilm carrier for recirculating aquaculture system effluent treatment. Bioresour. Technol. 192, 603–610. [35] Shen, Z.Q., Wang, J.L., 2011. Biological denitrification using cross-linked starch/PCL blends as solid carbon source and biofilm carrier. Bioresour. Technol. 102, 8835–8838. [36] Mergaert, J., Boley, A., Cnockaert, M.C., Muller, W.R., Swings, J., 2001. Identity and potential functions of heterotrophic bacterial isolates from a continuous-upflow fixed-bed reactor for denitrification of drinking water with bacterial polyester as source of carbon and electron donor. Syst. Appl. Microbiol. 24, 303–310. [37] Schipper, L.A., Robertson, W.D., Gold, A.J., Jaynes, D.B., Cameron, S.C., 2010. Denitrifying bioreactors-an approach for reducing nitrate loads to receiving waters. Ecol. Eng. 36, 1532–1543. [38] Ashok, V., Hait, S., 2015. Remediation of nitrate-contaminated water by solid-phase denitrification process - a review. Environ. Sci. Pollut. Res. 22, 8075– 8093.

26

ro of

Fig. 1. Schematic representation of the processes considered in the developed

mechanistic model. XShar represents the anaerobic hydrolysis bacteria that transform hardly degradable organic solid carbon into DOC; XSeas represents the anaerobic

-p

hydrolysis bacteria that transform easily degradable organic solid carbon into DOC;

re

XNO3 represents the denitrifiers, which convert DOC and NO3−-N into N2 and CO2.

0.8 P:S=4:6

P:S=5:5

0.4

0.2

lP

3

TOC (kg-C/m )

0.6

pH=6.5 pH=7.5 pH=8.5

0.0 100

200 Time (h)

300

0

ur

Jo

3

TOC (kg-C/m )

P:S = 4:6

2

100

na

0

3

P:S=6:4

200 Time (h)

300

0

100

200 Time (h)

300

(a)

P:S = 6:4

P:S = 5:5

1

pH=6.5 pH=7.5 pH=8.5

0 0

50

100

150 200 Time (h)

250

300 0

50

100

150 200 Time (h)

250

300 0

50

100

150 200 Time (h)

250

300

(b) 27

0.3

2.0 pH=6.5, TOC pH=7.5, TOC pH=8.5, TOC

-

0.2

pH=7.5, NO3 -N -

1.5

3

3

NO3 -N (kg-N/m )

-

pH=6.5, NO3 -N pH=8.5, NO3 -N

-

1.0

0.1 0.5

0.0

0.0 10

20 30 Time (h)

40

50

0

10

20 30 Time (h)

(c)

40

50

0

10

20 30 Time (h)

40

ro of

0

Fig. 2. The influences of pH and the mass ratio of the PLA/starch blends on

-p

physicochemical dissolution and hydrolysis of the blends, and its effect on the

denitrification performance. (a) Physicochemical dissolution of the blends with different

re

mass ratios at various pH values; (b) Hydrolysis of the blends with different mass ratios at various pH values; (c) Denitrification performance of the blends different mass ratios

lP

at various pH values.

na

0.25

0.086

0.10

0.138

ur

0.15

Jo

Total carbon release (g-C)

0.20

TOC accumulated in effluent TOC used for denitrification Denitrification effect Hydrolysis effect Dissolution effect 0.005

0.042 0.011

0.002

0.141 0.099

0.128

0.006

0.128

0.128

0.05

0.070 0.025

0.00 P:S = 4:6

P:S = 5:5

0.038

P:S = 6:4

28

TOC (kg-C/m )

P:S = 6:4

P:S = 5:5

P:S = 4:6

50

Fig. 3. Contributions of dissolution, hydrolysis and denitrification processes to the amounts of carbon released by the blends at a pH of 7.5. The “TOC accumulation amount in the effluent” can be obtained through measurement. The “TOC used for denitrification” was calculated by using the concentration difference between the influent and effluent and multiply the stoichiometric coefficient of nitrate denitrification. The “Dissolution effect” was obtained by measurement the TOC concentrations in leaching experiment (Section 3.1.1). The TOC release amount by hydrolysis (i.e. “hydrolysis effect”) was calculated by the concentration difference between the leaching

ro of

experiments and the hydrolysis experiments (Section 3.1.2). The “Denitrification effect” was defined as “TOC accumulated in effluent” + “TOC used for denitrification” “Dissolution effect” - “Hydrolysis effect”. 0.4

0.6

-

3

-

3

-

3

-

3

NO3 -N=0.15 kg-N/m 0.2

NO3 -N=0.25 kg-N/m NO3 -N=0.35 kg-N/m

0.4

0.2

re

0.1

0

20

40 Time (h)

lP

0.0 60

80

0

20

3

-

NO3 -N=0.05 kg-N/m

TOC (kg-C/m )

TOC

-p

0.3

-

3

NO3 -N (kg-N/m )

NO3 -N

0.0 40 Time (h)

60

80

Fig. 4. Denitrification performance of the blend with a PLA-to-starch ratio of 5 : 5 for

3

-

3

-

NO3 -N: 0.15 kg-N/m

-

Required HRT

-

CC,Seas

NO3 -N loading NO3 -N removal rate

Jo

0.004

-

NO3 -N: 0.05 kg-N/m

750

3

NO3 -N: 0.25 kg-N/m

600 450 300

0.002

150

0.000

0

5

10 15 20 25 Batch number (-)

0 30

35

0

3

6 9 Batch number (-)

12

0

2

4 6 Batch number (-)

8

Fig. 5. Simulation results showing the correlation between SPD performance and solid-carbon decomposition in semi-continuous experiments lasting 60 days. During the 29

Required HRT (h) and Organic carbon concentration 3 (kg-N/m )

0.006

ur

0.008

3

Nitrate loading (kg-N/m /h)

na

different nitrate concentrations (pH = 7.5).

simulation, nitrate concentrations of 0.05 kg-N/m3, 0.15 kg-N/m3 and 0.25 kg-N/m3 in the influent were examined, and the targeted nitrate concentration in the effluent was 0.01 kg-N/m3 (P : S = 5 : 5 and pH = 7.5).

ro of

Table 1 Values of the model parameters. Definition

Units

Initial values of related components

CC,l

Source

kg-C/m3

600

Measured

kg-C/m3

666.7

Measured

kg-C/m3

VDT a

Measured

kg-N/m3

VDT a

Measured

kg-Cell/m3

0.08 b

Measured

re

Concentrations of organic carbon in easily degradable solid phase

Concentrations of organic carbon in liquid phase

Concentrations of nitrate in liquid

ur

CNO3,l

in hardly degradable solid phase

lP

CC,Seas

Concentrations of organic carbon

na

CC,Shar

Values

-p

Parameters

phase

Jo

Concentrations of anaerobic

XShar,1

hydrolysis bacteria in liquid phase (for hardly degradable organic matters)

30

Concentrations of anaerobic

XSeas,1

hydrolysis bacteria in liquid phase (for easily degradable organic

kg-Cell/m3

0.2 b

Measured

matters)

εl

Concentrations of denitrifier in liquid phase Volumetric ratio of liquid phase

εSeas

Volumetric ratio of hardly

Volumetric ratio of easily

Organic carbon transformation coefficient for bacteria XSeas,1

Organic carbon transformation coefficient for bacteria XNO3,1 Organic carbon conservation coefficient for XShar,1

Jo

YC,X,Shar

YC,X,Sear

YC,X,NO3

0.982

Measured

Measured

Measured

0.008

Measured

kg-Cell/kg-C

0.5

Fitting

kg-Cell/kg-C

0.26

Fitting

kg-Cell/kg-C

1.0 c

(1)

kg-Cell/kg-C

0.3

Fitting

kg-Cell/kg-C

0.11

Fitting

kg-Cell/kg-C

1.0 c

(1)

re

lP

coefficient for bacteria XShar,1

ur

YC,NO3

Organic carbon transformation

na

YC,Sear

b

0.010

-

degradable solid waste

Kinetic parameters

YC,Shar

-

-

degradable solid waste

0.08-0.1

-p

εShar

kg-Cell/m3

ro of

XNO3,1

Organic carbon conservation coefficient for XSeas,1 Organic carbon conservation coefficient for XNO3,1

31

Kd,Shar

Kd,Seas

Kd,NO3

Half saturation coefficient of organic carbon for bacteria XSeas,1 Half saturation coefficient of organic carbon for bacteria XNO3,1 Half saturation coefficient of nitrate for bacteria XNO3,1 Decay coefficient for bacteria

XShar,1 Decay coefficient for bacteria

XSeas,1

Decay coefficient for bacteria

XNO3,1

Fitting

kg-C/m3

1100.0

Fitting

kg-C/m3

1100.0

Fitting

kg-C/m3

0.022 c

(1)

ro of

KNO3

organic carbon for bacteria XShar,1

0.85

kg-N/m3

0.014

Fitting

0.003

Fitting

1/h

0.006

Fitting

1/h

0.00426

(1)

-p

KC,NO3

Half saturation coefficient of

kg-Cell/kg-N

1/h

re

KC,Seas

for bacteria XNO3,1

lP

KC,Shar

Nitrate transformation coefficient

na

YNO3

Maximum growth rate of XShar,1

1/h

0.008

(2)

μmax,Seas

Maximum growth rate of XSeas,1

1/h

0.03

Fitting

1/h

0.108

(1)

kg-C/m3

0.3

Fitting

m3/kg-C/h

2×10-5

Fitting

ur

μmax,Shar

Maximum growth rate of XNO3,1 Equilibrium concentration of

Jo

μmax,NO3 CCeq,l

organic carbon in liquid phase Physiochemical dissolusion

βC,Shar

coefficient for organi carbon in hardly degradable solid phase

32

Physiochemical dissolusion

βC,Seas

coefficient for organi carbon in

m3/kg-C/h

9.7×10-4

Fitting

easily degradable solid phase Source: (1) [29]; (2) [30]. Vary with different treatments.

b

Initial bacteria concentrations.

c

Calculation refers to the value of the literature. During the calculation, the conversion

ro of

a

coefficient of DOC into COD was 1kg DOC equals to 2.6 kg COD. The coefficient was

-p

measured by the experiment data.

Table 2

re

Denitrification performance of solid phase denitrification systems using different

lP

synthetic biodegradable polymers as carbon sources.

Organic carbon conservation coefficient for X2

kg-Cell/kg-C

1.67

YC,X,3

Organic carbon conservation coefficient for X3

kg-Cell/kg-C

1.67

YC,X,4

Organic carbon conservation coefficient for X4

kg-Cell/kg-C

1.67

YC,X,5

Organic carbon conservation coefficient for X5

kg-Cell/kg-C

1.67

ur

na

YC,X,2

Organic carbon conservation coefficient for X8

kg-Cell/kg-C

1.67

YN,1

Organic nitrogen yield coefficient for bacteria X1

kg-Cell/kg-N

0.04

YON,2

Organic nitrogen yield coefficient for bacteria X2

kg-Cell/kg-N

0.1

YON,3

Organic nitrogen yield coefficient for bacteria X3

kg-Cell/kg-N

0.4

YNH4,6

Ammonium yield coefficient for bacteria X6

kg-Cell/kg-N

0.4

YNO2,7

Nitrite yield coefficient for bacteria X7

kg-Cell/kg-N

0.2

Jo

YC,X,8

33

Nitrate yield coefficient for bacteria X8

kg-Cell/kg-N

0.01

YN2,8

Nitrogen gas yield coefficient for bacteria X8

kg-Cell/kg-N

0.01

YN2O,6

Nitrous oxide yield coefficient for bacteria X6

kg-Cell/kg-N

400

YN2O,8

Nitrous oxide yield coefficient for bacteria X8

kg-Cell/kg-N

10

YN,X,1

Organic nitrogen conservation coefficient for bacteria X1

kg-Cell/kg-N

2.5

YON,X,2

Organic nitrogen conservation coefficient for bacteria X2

kg-Cell/kg-N

2.5

YON,X,3

Organic nitrogen conservation coefficient for bacteria X3

kg-Cell/kg-N

2.5

YNH4,X,6

Ammonium conservation coefficient for bacteria X6

kg-Cell/kg-N

2.5

YNO2,X,7

Nitrite conservation coefficient for bacteria X7

kg-Cell/kg-N

2.5

YNO3,X,8

Nitrate conservation coefficient for bacteria X8

kg-Cell/kg-N

2.5

YO2,3

Oxygen yield coefficient for bacteria X3

kg-Cell/kg-O

0.38

YO2,5

Oxygen yield coefficient for bacteria X5

kg-Cell/kg-O

0.66

YO2,6

Oxygen yield coefficient for bacteria X6

kg-Cell/kg-O

0.65

YO2,7

Oxygen yield coefficient for bacteria X7

kg-Cell/kg-O

0.54

A B Ea1,1

Frequency factor Dimensionless factor in Arrhenius expression Activation enthalpy for bacteria X1

1/h kJ/mol

5.69E+14 1.38E+48 98

Ea1,2

Activation enthalpy for bacteria X2

kJ/mol

95.9

ur

na

lP

re

-p

ro of

YNO3,8

Activation enthalpy for bacteria X3

kJ/mol

90

Ea1,4

Activation enthalpy for bacteria X4

kJ/mol

93.4

Ea1,5

Activation enthalpy for bacteria X5

kJ/mol

88.6

Jo

Ea1,3

Ea1,6

Activation enthalpy for bacteria X6

kJ/mol

93.9

Ea1,7

Activation enthalpy for bacteria X7

kJ/mol

92.8

Ea1,8

Activation enthalpy for bacteria X8

kJ/mol

92.5

Ea2,1

Inactivation enthalpy for bacteria X1

kJ/mol

288.1

Ea2,2

Inactivation enthalpy for bacteria X2

kJ/mol

287.8

Ea2,3

Inactivation enthalpy for bacteria X3

kJ/mol

273

Ea2,4

Inactivation enthalpy for bacteria X4

kJ/mol

286.8 34

Inactivation enthalpy for bacteria X5

kJ/mol

273.2

Ea2,6

Inactivation enthalpy for bacteria X6

kJ/mol

277.4

Ea2,7

Inactivation enthalpy for bacteria X7

kJ/mol

275.6

Ea2,8

Inactivation enthalpy for bacteria X8

kJ/mol

284.4

KC,1

Half saturation coefficient of organic carbon for bacteria X1

kg-C/m3

600

KC,2

Half saturation coefficient of organic carbon for bacteria X2

kg-C/m3

25

KC,3

Half saturation coefficient of organic carbon for bacteria X3

kg-C/m3

20

KC,4

Half saturation coefficient of organic carbon for bacteria X4

kg-C/m3

5

KC,5

Half saturation coefficient of organic carbon for bacteria X5

KC,8

Half saturation coefficient of organic carbon for bacteria X8

KN,1

Half saturation coefficient of organic nitrogen for bacteria X1

KON,2

Half saturation coefficient of organic nitrogen for bacteria X2

kg-N/m3

0.25

kg-N/m3

0.2

kg-N/m3

0.15

kg-N/m3

0.1

kg-N/m3

1

kg-O/m3

0.00015

kg-O/m3

0.005

kg-O/m3

0.001

kg-O/m3

0.006

Jo

kg-O/m3

0.002

kg-O/m3

0.0015

kg-O/m3

0.001

Kd,1

Half saturation coefficient of organic nitrogen for bacteria X3 Half saturation coefficient of ammonium for bacteria X6 Half saturation coefficient of nitrite for bacteria X7 Half saturation coefficient of nitrate for bacteria X8 Half saturation coefficient of oxygen for bacteria X2 Half saturation coefficient of oxygen for bacteria X3 Half saturation coefficient of oxygen for bacteria X4 Half saturation coefficient of oxygen for bacteria X5 Half saturation coefficient of oxygen for bacteria X6 Half saturation coefficient of oxygen for bacteria X7 Half saturation coefficient of oxygen for bacteria X8 Decay coefficient for bacteria X1

1/h

0.0004

Kd,2

Decay coefficient for bacteria X2

1/h

0.0004

Kd,3

Decay coefficient for bacteria X3

1/h

0.0004

KNO2,7 KNO3,8 KO2,2 KO2,3

KO2,5 KO2,6 KO2,7 KO2,8

kg-C/m3

13

kg-C/m3

0.02

kg-N/m3

250

-p

re

ur

KO2,4

lP

KNH4,6

na

KON,3

ro of

Ea2,5

35

Decay coefficient for bacteria X4

1/h

0.002

Kd,5

Decay coefficient for bacteria X5

1/h

0.0007

Kd,6

Decay coefficient for bacteria X6

1/h

0.0002

Kd,7

Decay coefficient for bacteria X7

1/h

0.0008

Kd,8

Decay coefficient for bacteria X8

1/h

0.001

CCeq,l

Equilibrium concentration of organic carbon in liquid phase

kg-C/m3

10

Equilibrium concentration of organic nitrogen in liquid phase Physiochemical dissolusion coefficient for organi carbon in hardly degradable solid phase Physiochemical dissolusion coefficient for organi nitrogen in hardly degradable solid phase Physiochemical dissolusion coefficient for organi carbon in easily degradable solid phase Physiochemical dissolusion coefficient for organi nitrogen in easily degradable solid phase Diffution coefficient for methane

kg-N/m3

7

m3/kg-C/h

1.00E-18

m3/kg-N/h

1.00E-19

m3/kg-C/h

2.00E-09

m3/kg-N/h

4.00E-06

βN,Seas DCH4,g DCO2,g DO2,g DN2,g DN2O,g ξg ξg-soil Kg Kg-soil

-p

βC,Seas

m2/h

0.01

2

m /h

0.06

2

m /h

0.07

2

m /h

0.05

2

m /h

0.06

Tortuosity of gas path way in refuse

-

13.9

Tortuosity of gas path way in cover soil

-

Diffution coefficient for carbon dioxide Diffution coefficient for oxygen

re

βN,Shar

Diffution coefficient for nitrogen

Diffution coefficient for nitrous oxide

lP

βC,Shar

13.9

Permeability of all gas components in refuse

2

m

1.0E-09

Permeability of all gas components in cover soil

m2

1.0E-10

Pa·h

5.00E-09

kPa·m3/mol/K

0.00831

na

CONeq,l

ro of

Kd,4

Viscosity of the air

R

Gas constant

Jo

ur

μ

36

37

ro of

-p

re

lP

na

ur

Jo