Anaerobic digestion of food waste: Correlation of kinetic parameters with operational conditions and process performance

Accepted Manuscript Title: Anaerobic digestion of food waste: Correlation of kinetic parameters with operational conditions and process performance Authors: Lei Li, Qin He, Xiaofei Zhao, Di Wu, Xiaoming Wang, Xuya Peng PII: DOI: Reference:

S1369-703X(17)30310-8 https://doi.org/10.1016/j.bej.2017.11.003 BEJ 6816

To appear in:

Biochemical Engineering Journal

Received date: Revised date: Accepted date:

14-7-2017 16-10-2017 11-11-2017

Please cite this article as: Lei Li, Qin He, Xiaofei Zhao, Di Wu, Xiaoming Wang, Xuya Peng, Anaerobic digestion of food waste: Correlation of kinetic parameters with operational conditions and process performance, Biochemical Engineering Journal https://doi.org/10.1016/j.bej.2017.11.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Anaerobic digestion of food waste: Correlation of kinetic parameters with operational conditions and process performance Lei Li, Qin He, Xiaofei Zhao, Di Wu, Xiaoming Wang, Xuya Peng*

Education, Chongqing University, Chongqing 400045, China. *

Corresponding author: Xuya Peng

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Address: No. 174, Shapingba Zhengjie Street, Chongqing, 400045, China

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Key Laboratory of Three Gorges Reservoir Region’s Eco-Environment, Ministry of

Tel: +86-15923153201

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Fax: +86-023-65121734

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E-mail address: L. Li: [email protected]

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Q. He: [email protected]

D. Wu: [email protected]

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X. Wang: [email protected]

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X. Zhao: [email protected]

Highlights

Links between kinetics and process performance/operational conditions were

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

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X. Peng: [email protected]

studied

k and Rm are not correlated with operational conditions or process parameters

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k/Rm′ was positively correlated with SC and S/I but negatively with BC and MY

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SC and S/I positively influenced k/Rm′ by impacting VFA and pH

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The promoting effect of BC on process efficiency was dependent on alkalinity

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Abstract The correlations between kinetic parameters and both operational conditions and process performance during the anaerobic digestion of food waste were investigated. Substrate concentration (SC), biomass concentration (BC), and substrate–inoculum ratio (S/I) were selected as the operational conditions, while first-order and modified Gompertz models were introduced to evaluate digestion kinetics. The results indicated

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that no significant correlations between both hydrolysis and methanogenesis kinetic

parameters (k and Rm) relative to the operational conditions and process parameters

could be determined; however, k/Rm′ was observed to significantly correlate with these

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parameters. Specifically, substrate load (both SC and S/I) positively influenced k/Rm′ via its impact on volatile fatty acid (VFA) and pH, while BC reduced k/Rm′ by increasing the alkalinity of the system. Moreover, k/Rm′ was negatively correlated with process

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efficiency. The methane recovery rate exceeded 90% when 1.55 < k/Rm′ < 3.13, but

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decreased by 50% when k/Rm′ > 4.64 (p = 0.05). These findings provide a scientific foundation for predicting the behavior of anaerobic systems and optimizing the

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digestion process.

Keywords: Anaerobic digestion; Food waste; Kinetic analysis; Process performance;

1. Introduction

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Operational conditions

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With global economic development, population growth, and urbanization, food waste (FW) is generated at an ever-increasing rate [1, 2]. Incomplete FW management

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has led to a series of food safety issues, and the disposal of FW is attracting wide social attention [1, 3]. Anaerobic digestion (AD) is considered the most promising option for FW disposal, as it simultaneously reduces waste streams and produces renewable

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energy [4, 5]. China has 100 demonstration projects for FW disposal, and more than 90% of these projects have chosen AD technology [3]. In the United States, the states California, Massachusetts, New York, Connecticut, and Vermont have instated new FW diversion regulations, which make AD the leading treatment option [6]. Anaerobic digestion is a complex biochemical process in which organic materials undergo hydrolysis, acidogenesis, acetogenesis, and methanogenesis in series. The

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stability and efficiency of the AD process rely on coordinated interactions among the different stages, and a mismatch between the stages leads to process deterioration [7]. Nevertheless, controlling the major operational parameters can improve the process efficiency and ensure stable methane generation, with substrate concentration (SC) one of the most important operational conditions that affects the stability of the process. However, the optimal SC for efficient and stable operation is controversial because of

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the effects of biomass concentration (BC) [8-11]. Both Markou et al. [12] and Wu et al. [13] have reported that increasing BC can help alleviate inhibition in digesters and

enhance SC tolerance. To evaluate the combined effects of SC and BC, some

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researchers have further investigated the influence of substrate–inoculum ratio (S/I) on

process performance [8, 14-16]. The selection of the ideal S/I is not only helpful for setting operational conditions for batch digesters, but it also can help establish a start-up

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protocol for continuous anaerobic digesters to optimize the critical operational stage

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[17]. However, existing studies have always using a specific BC and different SC gradients or a specific SC and different BC gradients to vary S/I. Few studies have

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examined whether the S/I threshold is uniform under different BCs and SCs, and no

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researcher has analyzed the impact mechanisms of BC, SC, and S/I on process performance.

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Additionally, understanding the digestion kinetics is important for designing digesters, predicting the behavior of anaerobic systems, and optimizing the digestion process [18, 19]. Information on digestion kinetics can also help establish correlations

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between process performance and kinetic characteristics as well as reveal the kinetic mechanisms of process instability. Various kinetic models have been applied to evaluate

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the kinetics of methane evolution. However, most related studies have concentrated on (1) the biogas potential and kinetic characteristics of an individual substrate [20, 21] and (2) comparison of AD performance and kinetics under different operational conditions

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(temperature, mixing ratio, S/I, etc.) and then have roughly determined the optimal operating conditions based on the values of the kinetic parameters [2, 22, 23]. However, the correlation of kinetic parameters with operational and process parameters has not yet been fully explored. In addition, the kinetic mechanisms of instability have not been adequately investigated. With this in mind, this study selected SC, BC, and S/I as operational conditions,

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monitored the response of performance parameters under different conditions, and evaluated the digestion kinetics using first-order and modified Gompertz models. The objectives of this study were to (1) clarify the correlation of kinetic parameters with operational conditions and process performance and (2) reveal the kinetic mechanisms of process instability induced by the operational conditions. The results will provide a basis for optimizing the operational conditions and predicting the process performance

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of anaerobic systems. 2. Materials and Methods 2.1 Substrates and inoculum

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The composition of FW is complicated and varies greatly among regions and seasons. In view of this, synthetic FW was used to ensure substrate consistency and reproducibility of results. As crude fat (CF) in FW may be separated for biodiesel

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production before feeding to a digester [2], synthetic FW was prepared based on a

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weight ratio of rice: vegetable: meat equal to 7:2:1 to minimize the CF content. This proportion was determined based on the characteristics of de-oiled FW, as determined

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and de-oiled FW are shown in Table 1.

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from previous investigations [24]. The characteristics of the raw materials, synthetic FW,

Mesophilic anaerobic digestion sludge, collected from a 30-L lab-scale completely

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stirred tank reactor (CSTR), was used as the inoculum. The CSTR was fed with FW at an organic loading rate (OLR) of 3 g VS·L-1·d-1, and it had been stably operating for more than 3 months prior to collection. The hydraulic retention time (HRT) of the

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digester was 44 d, and the methane yield (MY) was high (0.419 ± 0.037 L CH4∙gVS-1). Prior to use, the inoculum was passed through a 2-mm sieve to remove any large

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particles or grit and then pre-incubated at 36ºC ± 1°C for a week to allow any residual organic matter to be depleted. After de-gassing, the characteristics of the inoculum were analyzed. The inoculum had a pH of 7.44, total solid (TS) content of 5.55%, and volatile

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solid (VS) content of 2.15%. Total suspended solid (TSS), volatile suspended solid (VSS), volatile fatty acid (VFA), total alkalinity (TA), and total ammonium nitrogen (TAN) concentrations were 54.29, 19.8, 52, 4314, and 934 mg·L-1, respectively. 2.2 Experimental design Biogas production was determined in a series of 500-mL jars (reactors) with a working volume of 400 mL. Synthetic FW was added to each jar to create five SC

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treatments of 5, 10, 15, 20, and 30 g VS·L-1, and an inoculum was added to create three biomass treatments of 5, 10, and 15 g VSS·L-1. The corresponding S/I varied from 0.33 to 6. In addition, three controls containing only inoculum were set up to determine the amount of methane produced from the inoculum during endogenous respiration. Each operational condition was carried out in two parallel reactors. After adding the required amounts of inoculum and substrate, 10% (v/v) basal medium was added to each jar [25]

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and distilled water was added to reach the desired volume. Subsequently, a 1 mol·L-1 hydrochloric acid (HCl) solution or a 3 mol·L-1 sodium hydroxide (NaOH) solution was

used to adjust the initial pH to 8. Prior to initiating the experiments, the bottles were

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flushed with nitrogen gas for 5 min to ensure anaerobic conditions. After being sealed

with rubber stoppers, all digesters were maintained at 36ºC ± 1°C in a temperature-controlled chamber until the daily gas production was less than 1% of total

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gas production.

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2.3 Analytical methods

The volume of biogas production was determined by displacement with a 3

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mol·L-1 NaOH solution. The measured methane volume was adjusted to the volume at

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standard temperature (273.15 K) and pressure (101.325 kPa). The TS concentration was measured by heating at 105°C for 24 h, and the VS concentration was determined by

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heating at 550°C for 8 h. The VS removal rate (VSr) was calculated according to the equation introduced by Kafle et al. [26]. The pH was determined using a pH meter (Horiba, B-212). The total VFA, TA, and partial alkalinity (PA) concentrations were

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analyzed according to Li et al. [27]. Elemental contents (C, H, N, S) of the FW were quantified using an elemental analyzer (Elementar Vario ELIII, Germany). Oxygen

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content was calculated by subtracting the content of C, H, N, and S from VS content. 2.4 Kinetic model

First-order kinetic and modified Gompertz models were used to describe the

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kinetics of methane production for each experimental group, as shown in Equations (1) and (2), respectively [23, 28].

M (t )  P  (1  e kt )

(1)

 R e  M (t)  P  exp  exp  m  ( λ  t )  1   P  

(2)

where M(t) is specific MY at time t (mL·g VS-1); P is maximum methane potential 5

(mL·g VS-1); k is the hydrolysis rate constant (d-1); t is digestion time (d); Rm is the maximum methane production rate (mL·g VS-1 d-1); λ is the lag-phase (d); and e is the natural constant, 2.72. The parameters P, k, Rm, and λ were estimated by fitting the gas production data to the models. The correlation coefficient (R2) and standard error of the estimate (SEE) were calculated to evaluate the accuracy of the models.

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2.5 Statistical analysis

After evaluating the kinetics, the efficiency parameters (MY and VSr) for each incubation were plotted as functions of the kinetic parameters. As described in Poirier et

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al. [29], the two curves were fitted to the BiHill model by nonlinear regression, and the

k/Rm′ thresholds for process operation (efficient or unsteady) were calculated. Statistical significance was established at p = 0.05. In addition, Spearman correlation analysis was

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performed to determine the relationship between the kinetic parameters, process

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parameters, and operational conditions [30]. Origin 9.3 software was used to process the data and produce graphs.

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3. Results and Discussion

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3.1 Influence of SC and BC on process performance

The efficiency of AD reactors is typically evaluated based on MY and VSr,

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whereas pH, VFA, PA, and TA are used to evaluate process stability [27]. The influences of SC and BC on process efficiency are shown in Fig. 1. The responses of state indicators are presented in Supplemental Materials (SM) Figs. S1–S3, and performance

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indicators for digesters at the end of the experiments are summarized in Table 2. Under low BC (5 g VSS·L-1) and low to moderate SC (5–15 g VS·L-1), MY reached up to

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406.65–442.71 mL CH4·g VS-1, the corresponding methane recovery rate (MRr) exceeded 95%, and VSr was 86.7%–91.6% (Fig. 1). These results are consistent with high MY (400–500 mL CH4·g VS-1) and VSr (81%–92%) reported in previous studies [9,

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27]. Other process parameters for these experimental reactors did not substantially fluctuate beyond their baseline values. Compared with the control reactors, the VFA in each digester was observed to accumulate during the initial stage, especially in the digester with an SC of 15 g VS·L-1, with the VFA concentration reaching up to 4017mg·L-1. However, the accumulated VFA gradually decreased after three days (Fig. S1). Congruently, the initially decreased pH was observed to recover after three days,

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increasing to a value >7. This indicated that the acidification in these digesters was reversible [5]. With the consumption of accumulated VFA, the alkalinity in the reactor also recovered and the VFA/TA approached zero at the end of experiment. In contrast, when SC was increased to 20 and 30 g VS·L-1, irreversible acidification was observed. This may have been due to the rapid proliferation of fermentative microbial communities under high SC and subsequent conversion of organics to VFA;

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slow-growing methanogens were unable to proliferate as rapidly and could not directly

or indirectly degrade the generated VFA in the timeframe of these experiments. Ultimately, the imbalance between high yield and low consumption resulted in

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continued VFA accumulation [13, 31]. The accumulated VFA lowered TA in the

digesters and reduced the pH to <5.5, which was far below the optimal pH (6.5-8.2) required to maintain high methanogen activity [5]. Eventually, gas production ceased.

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Irreversible acidification resulted in an extremely lowered MRr and VSr in these two

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digesters (Table 2), which confirmed the overloading and is consistent with previous reports [5, 13]. Notably, though the VSr in the overloaded digesters was lower than that

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in the stable reactors, the decline in VSr was far less than that of MRr, resulting in high

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VSr and low MRr. In contrast, VSr was always lower than MRr in the stable digesters (Table 2). Yang et al. [32] observed a similar phenomenon, which may have been a

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result of the analytical method (oven drying) for VS determination—any volatile organic compounds (such as VFA) that exist in the samples will be evaporated during drying, leading to lower measured VS. In the stable digesters, volatile organic

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compounds were more abundant in the substrate than in the digestate; thus, the measured VS of the substrate and the corresponding VSr were lower than the true values.

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However, when overloading occurred, VFA was far more abundant in the digestate than in the substrate, resulting in overestimation of VSr compared with the true value. Therefore, although high MY represents good AD performance, effective hydrolytic

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fermentation can create an illusion of sufficient VSr, which explains the coexistence of high VSr and low MRr in overloaded digesters. Reactors with a BC of 10 g VSS·L-1 and an SC of 20 g VS·L-1 operated stably, and

performance was further improved when BC was increased to 15 g VSS·L-1, with high efficiency under all SCs (Fig. 1 and Table 2). Moreover, under the same SC, lower peak VFA concentrations and VFA/TA were observed in digesters with higher BC. These

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observations

verify

that

increasing

BC

can

positively

impact

process

performance—higher BC corresponds to a greater SC threshold and more stable AD. Therefore, no unified SC threshold can be defined. Similar phenomena have also been reported in continuous reactors. For example, Agyeman and Tao [10] and Tampio et al. [11] pointed out that to achieve long-term stable operation of FW digesters, OLR should be maintained between 1 and 4 g VS·L-1·d-1. Kumar et al. [7], Nagao et al. [9] and Park

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et al. [33] reported that digesters can operate efficiently at high OLR (5–10 g VS·L-1· d-1) through biomass retention and consequent enrichment of BC. Differences between these results may be due to the fact that increased BC can relieve the burden on the unit cell

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[12, 13, 34]. In view of this, the response of reactor performance was further analyzed considering both SC and BC through S/I. 3.2 Influence of S/I on process performance

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Compared with SC, S/I had a more consistent relationship with process efficiency

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(Fig. 2). Digesters with S/I ≤ 2 operated efficiently, with MRr and VSr ranging from 88.47% to 99.53% and from 86.88% to 94.71%, respectively, independent of BC;

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process efficiency fluctuated dramatically for digesters with S/I > 2. Process parameters

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showed similar responses to changes in S/I. Thus, S/I is more suitable than SC for defining the overloading threshold for AD because the former can eliminate the

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influence of BC. In this study, the overloading threshold was 2. Previous reports also investigated proper S/I thresholds during AD of FW (Table 3). As shown in Table 3, though all the feed was FW, the S/I thresholds suggested by prior research are not

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exactly the same. On one hand, this may be attributed to the different characteristics of FW among the different studies [8]. Most of the previous studies used raw FW as the

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substrate for digestion, and the CF content was about 15%~35% [4, 35]. In contrast, to simulate the feed in FW anaerobic digesters more realistically, de-oiled FW with a CF content of 5.17% was used in this study. High CF content in FW causes acidification,

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and this has been demonstrated in previous studies [2, 5]. The adoption of a well acclimated and, thus, highly active inoculum in the current study may be another reason for the high S/I. It has been proposed that high-activity sludge can contribute to overcoming irreversible acidification, since the inoculum should be able to process a higher flow of metabolites, such as hydrogen, acetate, and other VFAs, preventing their accumulation [36]. A decreasing S/I will reduce the effective space in the reactor,

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resulting in a decrease in its volume biogas production and economic feasibility [13]. Thus, a commercial-scale AD plant always wants to achieve the highest possible S/I. In order to achieve this goal, improving the oil removal efficiency as much as possible and selecting a highly active sludge for digestion is necessary. 3.3 Kinetic analysis Two types of models were employed to simulate the principal kinetic patterns of

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biogas production during batch tests, and the obtained kinetic parameters are summarized in Table 4. Both models could predict the actual evolution of methane production, as supported by high R2 and low SEE values. The R2 and SEE values of the

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modified Gompertz model were significantly higher (p < 0.05) and lower (p < 0.01), respectively, than those of the first-order regression model, indicating that the former

provided a more robust estimation. Similarly, Zhang et al. [28] and Li et al. [21]

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reported that a modified Gompertz model could better fit data from a co-digestion experiment compared with a first-order regression model. In addition, as shown in Table

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4, k values for stable digesters ranged from 0.13 to 0.56 d-1. For comparison, Browne

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and Murphy [20] reported k values for FW digesters in the range of 0.134–0.364 d-1,

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which are consistent with values in this study. In contrast, Rm values found in this study ranged from 28.03 to 174.63 mL CH4 ·g VS-1·d-1, which are higher than those reported

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for chicken manure (19.4–48.9 mL CH4 ·g VS-1·d-1) [21], corn stover (16.3–32.1 mL CH4·g VS-1·d-1) [21], and co-digestion of pig manure with dewatered sewage sludge (4.8–14.0 mL CH4 ·g VS-1·d-1) [28]. This discrepancy may be due to that fact that FW is

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easily degradable compared with other substrates, such as livestock manure, straw, and sludge. High substrate lability resulted in elevated hydrolysis and methanogenesis rates

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[5]. Aside from the k and Rm, λ and digestion time are also important indicators of substrate biodegradability and utilization rate. The λ value reflects the delayed response and subsequent adaptation of microorganisms to the changing environment [28, 30].

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The effective digestion time (Tef) was calculated by subtracting λ from the time taken to achieve 90% of maximum cumulative methane production (T90). Previous studies have suggested that shorter Tef with longer λ indicates a shorter AD period and irreversible process inhibition, longer Tef with longer λ indicates longer periods of AD and reversible process inhibition, and shorter Tef with shorter λ could reveal a high biogas production rate and a short AD period [30]. These inferences are well verified in this

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paper (Table 4). It is noteworthy that, compared with previously reported 0.5-12.3 d [2, 15], the λ in all digesters of this study was relatively short, which may be determined by the CF content of the substrate. Nevertheless, a transient lag phase was observed in digesters with low BC and high SC or with high BC and low SC. In the former case, microorganisms may need a certain period to adapt to the high SC; in the latter case, the SC was probably too low to trigger enzymatic activity of microbes in the digesters [14].

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3.4 Correlation of kinetic parameters with operational conditions and process performance

Figure 3 shows the influence of SC, BC, and S/I on kinetic parameters. The effects

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of operational parameters on k and Rm were variable. In stable digesters, increasing SC simultaneously decreased k and Rm (Fig. 3a and b), whereas increasing BC increased both parameters (Fig. 3c and d). Similarly, k and Rm values decreased with increasing

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S/I for S/I ≤ 2 (Fig. 3e and f). These observations indicated that increasing load weakens

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hydrolysis and methanogenesis, whereas high BC promotes these processes. Remarkably, reverse k and Rm trends were observed in the overloaded experimental

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reactors. Because of their non-monotonic fluctuations, no quantitative correlation

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between these two parameters and operational parameters could be determined. Thus, it is not feasible to optimize the operational conditions of the AD process directly from the

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values of k or Rm.

Process deterioration typically is attributed to differences between rates of acidogenesis and methanogenesis rather than the absolute metabolic rate at a certain

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stage. Thus, it is of great significance to study the relative changes between these two kinetic parameters. In view of this, after separately analyzing the trends of these two

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kinetic parameters, the correlation between the ratio of these two parameters and operational parameters was further investigated. As we know, the first-order kinetic model can be linearized as an equation for a straight line with a slope whose magnitude is the hydrolysis rate constant (k), and researchers have also used it to represent

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hydrolysis rates [23, 28, 30]. While the modified Gompertz model is a typical ‘‘S’’ style curve equation, the maximum methane production rate (Rm) was determined by linear regression of the initial, linear portion of a plot of cumulative methane yield versus time [2, 28]. The dimensions of k and Rm are different (Table 4). For comparison, we should first unify the dimensions. According to the elemental analysis (Table 1) and

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stoichiometry of the oxidation reaction [14], the COD of the substrate was 1.22 g COD· g VS-1; besides, the theoretical methane yield was 350 mL CH4· g COD-1 [25], so Rm′ equal to “Rm/ (1.22*350)”. After this calculation, the dimensions of k and Rm′ are unified to “d-1”. The physical meaning of “k/Rm′” is the ratio of the hydrolysis rate constant to the methanogenesis rate constant, which can be used to evaluate the rate ratio of hydrolysis to methanogenesis. Because acidogenesis is usually the fastest

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reaction during AD [37], k/Rm′ may also be used to approximate the rate ratio between

acid production and consumption. The closer this ratio is to 1, the higher the balance between acid production and consumption.

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As shown in Table 4, k/Rm′ was >1 in all reactors, independent of operational

conditions, indicating that the hydrolysis rate was faster than the methanogenesis rate. Thus, methanogenesis was the rate-limiting step of the whole process. This is consistent

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with the conclusions of Ma et al. [37] and Lin et al. [38]. In addition, k/Rm′ generally

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increased monotonously with increasing SC and S/I. This indicated that, although an increasing load decrease the rates of both hydrolysis and methanogenesis, the effect on

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the latter was greater. This result was expected because methanogenic communities are

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generally more sensitive to changes in environmental conditions. Similarly, k/Rm′ generally decreased with increasing BC in reactors with the same SC. Thus, the positive

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effect of BC on methanogenesis was greater than that on hydrolysis. This is consistent with previous reports showing that increasing initial BC can help a system avoid the inhibition phenomenon [12, 13].

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After defining the correlation between the operating conditions and the kinetic parameters, plots of MY and VSr versus k/Rm′ were generated (shown in Fig. 4) and the

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BiHill model was used to fit the two curves (R2 = 0.97 and 0.81, respectively). In highly efficient digesters, k/Rm′ maintained a level of 2.31 ± 0.56. Out of this range, MY and VSr declined with increasing k/Rm′, and the reduction in the former was greater. As

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mentioned earlier, this is because deterioration during any stage can affect MY, whereas only disturbance in the hydrolytic fermentation stage can result in low VS r. In view of this, MY was used as the only efficiency parameter, and the k/Rm′ threshold was calculated when process operation was efficient and unsteady. Based on the model equation, to achieve high MY (> 90%), k/Rm′ should be maintained in the range of 1.55–3.13; a 50% reduction in MY will occur when k/Rm′ > 4.64 (p = 0.05).

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From the above results, we can conclude that the kinetic parameters are closely related to the digester operating conditions and process performance. In order to further explain the results and clarify the kinetic mechanism of process instability (the impacting mechanism of operational conditions on process stability), the relationships among operational conditions (SC, BC, and S/I), process parameters (pH, VFA, PA, MY, and VSr), and kinetic parameters (k, Rm, k/Rm′, λ, and Tef) were further examined based

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on Spearman correlation analysis (Table 5). Overall, the correlations presented in Table 5 are highly consistent with the above analysis. For example, there is no significant

correlation of k and Rm with operational parameters or process parameters, except for

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their negative correlations to MY. This seems to imply that a digester with a lower reaction rate is more efficient. This sounds reasonable, as many natural AD sites (such

as swamps and lake sediments) are operating steadily and consistently under slow

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reaction rates.

Operational parameters, especially SC and S/I, are significantly related to process

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parameters (VFA, pH, and PA), efficiency parameters (MY and VSr), and k/Rm′. Among

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which, the substrate load (both SC and S/I) showed a better correlation with process

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parameters (especially VFA and pH) than with k/Rm′ and MY. Therefore, the substrate load accelerated the digestion kinetics by influencing VFA and pH and then decreased

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the process efficiency by the increased k/Rm′. In contrast, the promoting effect of BC on process efficiency was mainly dependent on alkalinity. The BC was positively correlated with PA, whereas increased PA promoted the decline of k/Rm′. Therefore,

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VFA and PA are two critical process parameters, and it is necessary to ensure sufficient alkalinity in the reactor to avoid acid accumulation. Many previous studies have

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proposed similar conclusions [3, 39]. In addition, there are correlations among different kinetic parameters. Most notably, k and Rm were negatively correlated with Tef, which is predictable, as a faster reaction rate shortens the required digestion time. There is also a

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significant positive correlation between k and Rm, which is consistent with the above observation that the effects of load and BC on k and Rm are always in the same direction. In summary, we concluded that investigation of the correlation of kinetic parameters with process performance and operational conditions is important for operation and control of the AD process and biogas plants. After setting the operating

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conditions and analyzing the substrate characteristics, industrial scale anaerobic digesters could firstly estimate the gas production potential and stability of the digesters through kinetic analysis, and based on the estimation results, operators can, in turn, optimize the operating conditions. This is of great practical value. Of course, in order to implement this function better, adopting more robust models for performance estimation is urgent. Nevertheless, this study serves as a precursor to encourage optimization of all

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operational conditions and achieve efficient and stable operation by analyzing the kinetic parameters and, finally, promote the comprehensive benefits of anaerobic digestion engineering.

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4. Conclusions

The k/Rm′ was negatively correlated with process efficiency. The methane recovery rate exceeded 90% when 1.55 < k/Rm′ < 3.13, but decreased by 50% when k/Rm′ > 4.64

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(p = 0.05). Although an increasing load decreased the rates of both hydrolysis and

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methanogenesis, the effect on the latter was greater, thus the k/Rm′ increased with the increasing load. Similarly, the positive effect of BC on methanogenesis was greater than

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that on hydrolysis, thus k/Rm′ generally decreased with increasing BC in the reactors.

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Under high SC, the substrate load accelerated the digestion kinetics by influencing VFA and pH and subsequently decreased the process efficiency due to the increased k/Rm′. In

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contrast, the promoting effect of BC on process efficiency was mainly dependent on the increased alkalinity. This study provides a precursor for optimizing the operational conditions and predicting the process performance of anaerobic systems by analyzing

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kinetic parameters, which is helpful for the efficient and stable operation of anaerobic digesters for the treatment of FW.

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Competing interests

The authors declare no competing financial interest.

Acknowledgements

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This work was financially supported by the Fundamental Research Funds for the

Central Universities (NO.106112017CDJXY210006).

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digestion characteristics of food waste, Bioresource Technol. 232 (2017)

[3] L. Li, X. Peng, X. Wang, D. Wu, Anaerobic digestion of food waste: A review on

process

stability,

Bioresource

10.1016/j.biortech.2017.07.012.

Technol.

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SC R

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[4] N.B.D. Thi, C. Lin, G. Kumar, Waste-to-wealth for valorization of food waste to

U

hydrogen and methane towards creating a sustainable ideal source of bioenergy, J.

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[5] M. Kawai, N. Nagao, N. Tajima, C. Niwa, T. Matsuyama, T. Toda, The effect of the

A

labile organic fraction in food waste and the substrate/inoculum ratio on

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anaerobic digestion for a reliable methane yield, Bioresource Technol. 157 (2014) 174-180.

ED

[6] Y.M. Amha, P. Sinha, J. Lagman, M. Gregori, A.L. Smith, Elucidating microbial community adaptation to anaerobic co-digestion of fats, oils, and grease and food waste, Water Res. 123 (2017) 277-289.

PT

[7] G. Kumar, P. Sivagurunathan, J. Park, S. Kim, Anaerobic digestion of food waste to methane at various organic loading rates (OLRs) and hydraulic retention times

CC E

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[8] V. Moset, N. Al-zohairi, H.B. Møller, The impact of inoculum source, inoculum to

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substrate ratio and sample preservation on methane potential from different substrates, Biomass Bioenerg. 83 (2015) 474-482.

[9] N. Nagao, N. Tajima, M. Kawai, C. Niwa, N. Kurosawa, T. Matsuyama, F.M. Yusoff, T. Toda, Maximum organic loading rate for the single-stage wet anaerobic digestion of food waste, Bioresource Technol. 118 (2012) 210-218. [10] F.O. Agyeman, W. Tao, Anaerobic co-digestion of food waste and dairy manure:

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Effects of food waste particle size and organic loading rate, J. Environ. Manage. 133 (2014) 268-274. [11] E. Tampio, S. Ervasti, T. Paavola, S. Heaven, C. Banks, J. Rintala, Anaerobic digestion of autoclaved and untreated food waste, Waste Manage. 34 (2014) 370-377. [12] G. Markou, D. Vandamme, K. Muylaert, Ammonia inhibition on Arthrospira

IP T

platensis in relation to the initial biomass density and pH, Bioresource Technol. 166 (2014) 259-265.

[13] C. Wu, Q. Wang, M. Yu, X. Zhang, N. Song, Q. Chang, M. Gao, K. Sonomoto,

SC R

Effect of ethanol pre-fermentation and inoculum-to-substrate ratio on methane yield from food waste and distillers’ grains, Appl. Energ. 155 (2015) 846-853.

[14] F. Pellera, E. Gidarakos, Effect of substrate to inoculum ratio and inoculum type on

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the biochemical methane potential of solid agroindustrial waste, J. Environ.

N

Chem. Eng. 4 (2016) 3217-3229.

[15] M. Fang, S.B. Wu, W.Q. Zhang, W. Li, C.L. Pang, R.J. Dong, Influence of

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inoculum-substrate ratio on food waste mesothermal anaerobic digestion, J.

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China Agric. Univ. (2014) 186-192.

[16] J. Li, X. Li, Effect of feed to inoculum ratios on high-solids anaerobic fermentation

ED

of food waste, Environ. Sci. Manage. (2012) 131-135. [17] G. Liu, R. Zhang, H.M. El-Mashad, R. Dong, Effect of feed to inoculum ratios on biogas yields of food and green wastes, Bioresource Technol. 100 (2009)

PT

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CC E

Assessment of two-phase olive mill solid waste and microalgae co-digestion to improve methane production and process kinetics, Bioresource Technol. 157 (2014) 263-269.

A

[19] X. Yuan, X. Shi, C. Yuan, Y. Wang, Y. Qiu, R. Guo, L. Wang, Modeling anaerobic digestion of blue algae: Stoichiometric coefficients of amino acids acidogenesis and thermodynamics analysis, Water Res. 49 (2014) 113-123.

[20] J.D. Browne, J.D. Murphy, Assessment of the resource associated with biomethane from food waste, Appl. Energ. 104 (2013) 170-177. [21] Y. Li, L. Feng, R. Zhang, Y. He, X. Liu, X. Xiao, X. Ma, C. Chen, G. Liu,

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Influence of Inoculum Source and Pre-incubation on Bio-Methane Potential of Chicken Manure and Corn Stover, Appl. Biochem. Biotech. 171 (2013) 117-127. [22] G.K. Kafle, S. Bhattarai, S.H. Kim, L. Chen, Effect of feed to microbe ratios on anaerobic digestion of Chinese cabbage waste under mesophilic and thermophilic conditions: Biogas potential and kinetic study, J. Environ. Manage. 133 (2014) 293-301.

IP T

[23] G. Zhen, X. Lu, T. Kobayashi, Y. Li, K. Xu, Y. Zhao, Mesophilic anaerobic

co-digestion of waste activated sludge and Egeria densa: Performance assessment and kinetic analysis, Appl. Energ. 148 (2015) 78-86.

SC R

[24] Q. He, L. Li, Q.M. He, X.Y. Peng, Physical and chemical properties and methane

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U

[25] J.C. Costa, J.V. Oliveira, M.M. Alves, Response surface design to study the

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influence of inoculum, particle size and inoculum to substrate ratio on the methane production from Ulex sp., Renew. Energ. 96 (2016) 1071-1077.

A

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production: A lab scale evaluation of biochemical methane potential (BMP) and kinetics, Bioresource Technol. 127 (2013) 326-336.

ED

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PT

[28] W. Zhang, Q. Wei, S. Wu, D. Qi, W. Li, Z. Zuo, R. Dong, Batch anaerobic co-digestion of pig manure with dewatered sewage sludge under mesophilic

CC E

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Identification of key microbial phylotypes, Bioresource Technol. 207 (2016) 92-101.

[30] C. Mao, X. Wang, J. Xi, Y. Feng, G. Ren, Linkage of kinetic parameters with process parameters and operational conditions during anaerobic digestion, Energy. 135 (2017) 352-360. [31] C. Zhang, H. Su, T. Tan, Batch and semi-continuous anaerobic digestion of food

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waste in a dual solid–liquid system, Bioresource Technol. 145 (2013) 10-16. [32] L. Yang, Y. Huang, M. Zhao, Z. Huang, H. Miao, Z. Xu, W. Ruan, Enhancing biogas generation performance from food wastes by high-solids thermophilic anaerobic digestion: Effect of pH adjustment, Int. Biodeter Biodegr. 105 (2015) 153-159. [33] J. Park, G. Kumar, Y. Yun, J. Kwon, S. Kim, Effect of feeding mode and dilution

IP T

on the performance and microbial community population in anaerobic digestion of food waste, Bioresource Technol. (2017) DOI:10.1016/j.biortech.2017.07.02.

[34] S. Poirier, C. Madigou, T. Bouchez, O. Chapleur, Improving anaerobic digestion

SC R

with support media: Mitigation of ammonia inhibition and effect on microbial communities, Bioresource Technol. 235 (2017) 229-239.

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waste valorization via anaerobic processes: a review, Rev. Environ. Sci. Bio. 15

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bio-methanization of a kitchen waste under different waste/inoculum ratios,

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Process Biochem. 39 (2004) 2019-2024.

[37] J. Ma, C. Frear, Z. Wang, L. Yu, Q. Zhao, X. Li, S. Chen, A simple methodology

ED

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PT

[38] L. Lin, Z. Yu, Y. Li, Sequential batch thermophilic solid-state anaerobic digestion of lignocellulosic biomass via recirculating digestate as inoculum – Part II:

CC E

Microbial diversity and succession, Bioresource Technol. 241 (2017) 1027-1035.

[39] X. Goux, M. Calusinska, S. Lemaigre, M. Marynowska, M. Klocke, T. Udelhoven, E. Benizri, P. Delfosse, Microbial community dynamics in replicate anaerobic

A

digesters exposed sequentially to increasing organic loading rate, acidosis, and process recovery, Biotechnol. Biofuels. 8 (2015).

17

Figure captions

Figure 1. Influence of SC on process efficiency under different biomass concentrations Experimental groups with BC = 5 g VSS·L-1 and SC = 20 g VS·L-1 were halted on Day 6 because NaOH was introduced into the bottles for unidentified reasons.

SC R

Figure 3. Influence of SC, BC, and S/I on kinetic parameters.

IP T

Figure 2. Influence of S/I on process efficiency under different biomass concentrations.

A

CC E

PT

ED

M

A

N

U

Figure 4. Correlations between k/Rm′ and process efficiency parameters.

18

500

500 （ b） BC=10 g VSSL-1

400

300

200

100

400

300

200

100

0

0 2

4

6

8

10

12

0

2 SC=5 gVSL-1 SC=20 gVSL-1

500 100

（ d）

400

0 2

4

6

10

12

8

U

SC=15 gVSL-1

20

12

A

CC E

PT

ED

Time (days)

10

60

40

SC=10 gVSL-1 SC=30 gVSL-1

N

SC=5 gVSL-1 SC=10 gVSL-1 SC=15 gVSL-1 SC=20 gVSL-1 SC=30 gVSL-1 Theoretical methane yield 0

8

A

300

VSr (%)

80

M

Methane yield (mL CH4g VS-1)

（ c） BC=15 g VSSL-1

100

6

Time (days)

Time (days)

200

4

SC R

0

IP T

Methane yield (mL CH4g VS-1)

Methane yield (mL CH4g VS-1)

（ a） BC=5 g VSSL-1

19

0

5

10 BC (g VSSL-1)

15

100

700 Methane yield: BC=10 g VSSL-1

600

BC=15 g VSSL-1

80

500

400

IP T

VSr (%)

70

Methane yield (mL CH4g VS-1)

BC=5 g VSSL-1

90

60

300

SC R

50 VSr:

40

200

-1

BC=5 g VSSL BC=10 g VSSL-1 BC=15 g VSSL-1 2

4

S/I

A

CC E

PT

ED

M

A

N

0

100

U

30

20

6

SC=5 g VSL-1

k (d-1)

SC=30 g VSL-1

0.6 0.4 0.2 0.0 10

0.0 -0.3 150 120 90 60 30 0

-0.6 -0.9

10

5

15

BC (g VSSL-1)

15

BC (g VSSL-1)

4.8 (d)

BC=5 g VSSL-1

270

k (d-1) 0.6 0.4 0.2 0.0 20

30

SC (g VSL-1)

0 0

1

PT

1

2

3

4

5

15

10

20

30

SC (g VSL-1)

(f)

3 2

-0.6

250

ED

k (d-1)

4

-0.3

-0.9

300

BC=5 g VSSL-1 BC=10 g VSSL-1 BC=15 g VSSL-1

0.0

M

(e)

5

5

A

15

10

Rm (mLg VS-1d-1)

5

150 120 90 60 30 0

0.3

U

BC=15 g VSSL-1

N

Rm (mLg VS-1d-1)

BC=10 g VSSL-1

4.6

SC R

(c)

200 150 100 50 0

6

0

1

2

3

4

S/I

A

CC E

S/I

21

 (d)

Rm (mLg VS-1d-1)

SC=20 g VSL-1

5

0.3

270

SC=15 g VSL-1

4.6



(b)

SC=10 g VSL-1

5

6

 (d)

(a)

IP T

4.8

100

Methane yield BiHill Fit of methane yield R2=0.97

400 350 300

VSr 90

R2=0.81

80 70

250

150

50

100

40

50

IP T

60

200

30 1

2

3

4

5

6

7

8

1

9

2

SC R

0

BiHill Fit of VSr

VSr(%)

Methane yield (mL CH4g VS-1)

450

3

4

5

k/Rm'

A

CC E

PT

ED

M

A

N

U

k/Rm'

22

6

7

8

9

Tables Table 1. Characteristics of FW. The composition of Synthetic FW Synthetic FW De-oiled FW Meat

Rice

Total solid (TS) (%)

6.89

12.93

20.64

17.12

18.30

Volatile solid (VS) (%)

6.21

12.68

20.54

16.89

17.00

VS/TS

90.10

98.07

99.52

98.65

pH

6.33

5.96

5.81

5.87

C (%)

44.15

52.87

44.21

H (%)

5.55

7.32

6.13

O (%)

34.41

21.71

46.98

N (%)

5.20

14.81

1.74

3.01

3.62

S (%)

0.79

1.36

0.46

0.55

0.64

C/N

8.49

3.57

25.41

14.90

12.80

Carbohydrate (%)

79.5

1.67

86.5

76.62

65.30

Crude fat (%)

0

24.28

3.92

5.17

11.16

20.5

74.05

9.58

18.21

16.42

465.60

551.72

413.23

427.40

428.39

92.88

SC R

3.67

44.86

46.32

6.17

4.21

44.06

38.09

U

A

M

ED

Protein (%)

IP T

Vegetables

N

Parameters

Theoretical methane yield

a

PT

(mL CH4·g VS-1)a

Theoretical methane yield was estimated through elemental composition using

A

CC E

Buswell’s formula.

23

Table 2. Reactor performance parameters for different operational conditions. SC

(g VSS·L-1) (g VS·L-1)

MRr

VSr

S/I

VFA pH

(%)

(%) /

PA

TA

(mg·L-1) (mg·L-1) (mg·L-1)

VFA/TA PA/TA

0

/

/

8.33 0

3256

3531

0.00

0.92

5

1

98.48 91.65 7.75 0

4113

4411

0.00

0.93

10

2

95.14 88.65 7.8 0

4312

4544

0.00

0.95

15

3

103.58 86.70 7.81 896

4179

4942

0.18

0.85

20

4

23.16 67.61 5.4 5781

0

2161

2.67

0.00

30

6

13.82 30.67 3.6 11805

0

0

/

/

8.22 0

4816

5

0.5 88.47 91.32 7.87 0

5406

10

1

15

1.5 95.70 87.86 7.95 21

20

2

99.53 90.38 7.97 842

30

3

0

/

IP T

BC

0

/

/

5134

0.00

0.94

5779

0.00

0.94

5920

U

0.01

0.94

4880

5212

0.00

0.94

5207

6003

0.14

0.87

18.41 61.22 4.91 13294

0

1169

11.38

0.00

/

8.09 0

5368

6349

0.00

0.85

5

0.33 89.30 94.71 7.92 0

6666

0.00

0.87

10

0.67 93.06 86.88 7.77 0

5969

6761

0.00

0.88

/

5560

5779

15

1

95.52 92.49 8.03 82

6434

7230

0.01

0.89

20

1.33 97.44 93.98 7.83 64

7396

7761

0.01

0.95

2

5771

8889

0.47

0.65

CC E

PT

ED

/

M

A

N

96.12 91.16 7.86 40

10

15

SC R

5

98.58 90.85 7.72 4209

A

30

24

Table 3. S/I ratio during AD of FW as reported in the literature. Studied

S/I

References Suggested S/I

Operational conditions

range Depending

on VS ratio, standard FW (CF [36]

0.5, 1.0, 1.35, inoculum

activity content

8.4%),

mesophilic

and buffer capacity 1.6, 3.1, 4.0, 5.0

temperature VS (substrate)/VSS (inoculum),

≤4

[17]

SC R

thermophilic temperature

IP T

2.3

TS ratio, high–solid anaerobic [16] 0.5,1,2,4,6,8,10

<2

digestion (TS=12.6%~17.1%)

U

VS ratio, both standard (CF [15] content 28.3%) and de-oiled FW 0.33 (CF

content

N

0.25, 0.33, 0.5, 1

11.42%),

A

mesophilic temperature VS

<0.33

ratio,

standard

mesophilic temperature

A

CC E

PT

ED

2.0, 4.0

M

0.33, 0.5, 1.0,

25

FW, [5]

Table 4. Kinetic parameters for different operational conditions.

(gVSS·L-1) (gVS·L-1)

Rm

(d-1)

R2 S.E.E.

λ

(mLCH4·g VS-1d-1) (d)

R2 S.E.E.

Rm′

Tef k/Rm′ (d-1) (d-1)

0.48 0.97 29.80 84.98

0

0.99 10.00

10

0.37 0.89 58.42 50.29

0

0.98 17.26 0.12 3.14 8.00

15

0.23 0.91 50.84 34.52

0

0.98 18.89 0.08 2.85 12.00

20

2.30 0.98 18.23 181.25

0.27 0.99 2.65 0.42 5.42 /

30

4.73 0.83 27.83 272.35

0.34 1.00 0.30 0.64 7.42 0.29

5

0.54 0.99 17.06 147.88

0.09 0.98 15.18 0.35 1.56 4.37

10

0.41 0.94 33.71 79.75

0

15

0.40 0.93 49.23 60.44

20

0.27 0.94 47.85 42.60

30

1.99 0.99 3.81 96.61

5

0.56 0.99 20.70 174.63

0.15 0.99 13.33 0.41 1.37 4.85

10

0.46 0.99 20.70 113.06

0.07 0.99 12.07 0.26 1.74 6.10

15

0.48 0.97 50.23 92.33

0.13 0.99 11.68 0.22 2.22 5.87

20

0.43 0.95 50.33 67.45

0

0.99 14.06 0.16 2.72 7.00

0

0.99 13.31 0.07 1.98 19.13

0.13 0.95 19.75 28.03

A

CC E

30

26

SC R

IP T

5

PT

15

k

0.98 16.65 0.14 2.83 7.00

N

U

0.99 15.28 0.19 2.20 8.00

0

0.99 14.87 0.10 2.71 9.79

A

10

Modified Gompertze

M

5

First order SC

ED

BC

0

0.02 1.00 0.88 0.23 8.80 0.98

Table 5. Spearman's correlation coefficients of kinetic parameters with process parameters and operational conditions for all combinations. k/Rm SC

BC

S/I

pH

VFA PA

MY

VSr

k

Rm

′

λ

BC 0.000 0.792 -0.581 **

IP T

S/I *

-0.731 0.567 -0.916 pH *

**

VF 0.949

SC R

**

0.902 -0.857 -0.208

A

**

**

**

U

-0.701 0.645 -0.938 0.953 -0.849 PA M -0.578

**

**

*

A

-0.771

0.094 Y

**

N

**

0.107 -0.018 0.100 **

M

**

-0.535 0.548 -0.774 0.779 -0.636 0.738 VSr **

**

*

0.346

**

-0.858

-0.011 -0.132 0.002 -0.016 -0.080 -0.041

-0.148 **

PT

k

*

ED

*

-0.864

CC E

Rm -0.142 -0.057 -0.133 0.086 -0.229 0.115

0.956 -0.125

**

**

k/R 0.611 -0.643 0.879 -0.729 0.721 -0.799 -0.912 -0.711 0.177 -0.004

*

λ

0.047 0.031 0.009 0.019 -0.045 0.035

A

m′

**

**

**

**

**

**

**

-0.815

0.844 0.899 0.00 -0.186

**

**

0.841 Tef 0.147 0.057 0.200 -0.194 0.260 -0.132

4

-0.983 -0.923 -0.0 -0.778 0.018

**

**

Statistically significant values are indicated: **P < 0.01; *P < 0.05.

27

**

**

42

**

pH, VFA, and PA use the average value during the digestion. The units of SC, BC, VFA, PA, MY, VSr, k, Rm, λ, and Tef are g VS·L-1, g VSS·L-1, mg·L-1, mg·L-1, mL·g

A

CC E

PT

ED

M

A

N

U

SC R

IP T

VS-1, %, d-1, mL·g VS-1d-1, d, and d, respectively.

28