Characterization of mixing by CFD simulation and optimization of mixing frequency to break scum and enhance methane yield in Chinese dome digester

doi:10.21203/rs.3.rs-1596590/v1

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Characterization of mixing by CFD simulation and optimization of mixing frequency to break scum and enhance methane yield in Chinese dome digester

https://doi.org/10.21203/rs.3.rs-1596590/v1

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The Chinese dome digester (CDD) is a self-mixed, low-cost, and most popular digester that faces the challenge of scum formation due to insufficient mixing. Mixing intensity in CDD is controlled by gas valve operation during gas production and usage. This study explores computational fluid dynamics (CFD) simulation to characterize mixing in CDD and the effect of mixing frequency on the performance of semicontinuous anaerobic digestion (AD) to improve mixing intensity, break scum and enhance methane yield. ANSYS software was applied to simulate the flow fields of a lab-scale CDD and four CDDs were operated at different mixing frequencies (0, 4, 6, and 8 times per day) to investigate the optimum mixing frequency that could break scum. 45% of CDD working volume was dead zones at the top of CDD which nurtured scum. In the AD experiments, scum thickness increased progressively in the non-mixed digester (2.2 ± 0.12 cm), compared to the mixed digesters, 4, 6, and 8 times per day (0.23 ± 0.05, 0.83 ± 0.11, and 1.31 ± 0.16 cm, respectively). The optimum mixing frequency was 4 times per day and the energy required to break scum was 6.12 ± 0.25 Joules per mixing cycle. The average methane yields for 0, 4, 6, and 8 times per day were 206.39 ± 192.09, 601.45 ± 88.80, 555.83 ± 59.92 and 493.11 ± 109.76 mL g-VS^− 1, respectively. This study proves that scum can be broken in CDD by an optimum mixing frequency of 4 times per day without additional investment cost.

Anaerobic digestion

Chinese dome digester (CDD)

Scum formation

Mixing frequency

Mixing intensity

Computational Fluid Dynamics (CFD)

Energy is a vital component required for improving the human quality of life, abating poverty, and promoting socioeconomic activities [1]. However, millions of communities and households, particularly in developing countries, still lack access to basic energy services such as electricity, liquid fuels, and natural gas [2]. For example, ~ 1.5 billion people (> 20% of the global population) have no access to electrical power, while ~ 3 billion people (~ 45% of the global population) still rely on traditional biomass sources such as firewood, dry food waste, and coal for their cooking needs [3, 4]. Moreover, large quantities of municipal solid waste (MSW) from numerous urban areas in developing countries are being dumped in unregulated landfills, and these pose severe threats to both the environment and human health [5]. Anaerobic digestion (AD) is a cost-effective, clean, and–arguably–the most popular renewable energy source that can utilize MSW such as food waste to produce biogas [6].

The Chinese dome digester (CDD) is one of the most popular, low-cost and widely applied household digesters in rural areas of developing countries, given its energy-efficient self-mixing process, no moving parts, reliability, low maintenance, and long lifespan [7, 8]. CDD is usually constructed underground with a hemispherical dome top (headspace), which serves as gas storage and gas pressure is maintained through the height of the expansion tank [9]. When the biogas produced accumulates and is stored in the headspace above the slurry, the stored gas results in a pressure build up and pushes part of the slurry into the expansion tank. During gas release through a valve, the slurry flows back into the main digester, thus, creating a mixing regime [10]. Figure 1 illustrates the self-mixing process in a CDD. Furthermore, CDDs are relatively inexpensive and have non-forced mixed systems, making them suitable to meet the energy needs of millions of rural households in developing countries and mitigate greenhouse gas emissions from MSW [11].

The major hindrance to achieving sufficient biogas production in a CDD is the formation of scum. Scum is a mixture of undigested substrate, microbes and, virtually any material that can float [12, 13]. Scum represents a severe technological challenge because it hampers the release of biogas and can reduce the available digester capacity by 30% [14]. Rankin & Schlenz. [13], Raman et al. [14], and Jegede et al. [15] have reported that insufficient mixing is the main cause of scum formation in CDD. Notably, the structural design of CDD and mixing frequency have a direct impact on digester hydrodynamics and eventually digester performance. In CDD, optimized mixing can reduce applicable hydraulic retention time (HRT) and therewith digester volume and investment costs [16]. Some previous studies had developed various methods to break scum in CDD such as heating the digester and the application of stirrer. However, when heat was applied, the lower liquid zone heated up, but the heat failed to penetrate the scum layer at the top [13]. Also, the application of a stirrer was inefficient as a stirrer was persistently stocked with scum. Although some previous studies have applied various methods to break scum, no measures for scum control have been incorporated in the designs of CDD, applicable for large-scale applications, yet [14]. Mixing intensity in Chinese dome digester is controlled by a gas valve operation (mixing frequency) during gas production and gas usage [9, 19]. For instance, if a large amount of gas is released at once, it would make the mixing intensity stronger. Thus, by optimizing the mixing frequency in CDD, scum formation can be controlled without additional investment cost. The reason scum is formed at the upper part of CDD is attributed to initial dead zones created due to insufficient mixing. Because the presence of the dead zone in CDD led to formation of the scum, advanced modeling and simulation techniques could be employed.

Computational fluid dynamics (CFD) is a major tool for evaluating velocity profile, particle trajectories and dead (stagnant) zones in anaerobic digesters, reducing both expense and time [17]. CFD predictions show good qualitative comparison with the experimental data in terms of flow pattern, location of dead zones, and trends in velocity profiles [18]. Notably, computer simulations had showed that insufficient mixing results in lower methane yield and treatment efficiency [18].

This study pioneers in exploring 3D, multiphase CFD simulation in CDD to characterize mixing and the effect of mixing frequency on the performance of semi-continuous AD in CDD to identify dead zones, improve mixing intensity, prevent scum formation, and enhance methane yield. To this end, we elucidate 3D, multiphase CFD simulation in CDD and the optimum mixing frequency to achieve sufficient mixing intensity.

2.1. CFD modeling and simulation

2.1.1. Geometry and operating principle

The modeling and simulations of CDD were performed in ANSYS Workbench (v. 19.2). The CDD was constructed from PVC materials, with a digester volume of 15 L and an expansion tank of 2 L for slurry displacement and outlet [9]. The 3D geometry was developed in ANSYS SpaceClaim (v.19.2) as shown in Fig. 2a.

2.1.2. Model development

Slurry flow inside an anaerobic digester is complex and it is governed by the conservation of mass and momentum [9, 18]. Therefore, the following assumptions were made in developing a theoretical model describing the mixing process in CDD:

Fluid flow in the digester is laminar.
Fluid is Newtonian.
The model is limited to the flow model without considering the heat transfer, as the slurry temperature is constant at 35^oC.
The model is a single phase (liquid), in which gas-liquid and solid-liquid phase interactions are negligible.

2.1.3. Governing equations

The CFD codes were solves based on the conservation laws of fluid mechanics, conservation of mass and momentum in its calculations [20, 21].

Continuity Equation (conservation of mass): \(\frac{\partial }{\partial {x}_{i}} \left(\rho {u}_{i}\right)=0\)………………………... (1)

where\({\rho } \text{i}\text{s} \text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y} \text{o}\text{f} \text{t}\text{h}\text{e} \text{l}\text{i}\text{q}\text{u}\text{i}\text{d} \text{s}\text{u}\text{b}\text{s}\text{t}\text{r}\text{a}\text{t}\text{e} \text{a}\text{n}\text{d} {\text{u}}_{\text{i}} \text{i}\text{s} \text{f}\text{l}\text{u}\text{i}\text{d} \text{v}\text{e}\text{l}\text{o}\text{c}\text{i}\text{t}\text{y} \text{i}\text{n} \text{t}\text{e}\text{n}\text{s}\text{o}\text{r} \text{f}\text{o}\text{r}\text{m}\)

Momentum Equation: \(\frac{\partial }{\partial {x}_{j}}\left(\rho {u}_{i}{u}_{j}\right)= -\frac{\partial p}{\partial {x}_{i}}+\frac{\partial {\tau }_{ij}}{\partial {x}_{j}}+\rho {g}_{i}\)…………………………… (2)

\(\text{w}\text{h}\text{e}\text{r}\text{e} \text{p} \text{i}\text{s} \text{s}\text{t}\text{a}\text{t}\text{i}\text{c} \text{p}\text{r}\text{e}\text{s}\text{s}\text{u}\text{r}\text{e}\) , \({{\tau }}_{\text{i}\text{j}} =\mu \left(\frac{\partial {u}_{i}}{\partial {x}_{j}}+\frac{\partial {u}_{j}}{\partial {x}_{i}}\right)\left(\text{s}\text{t}\text{r}\text{e}\text{s}\text{s} \text{t}\text{e}\text{n}\text{s}\text{o}\text{r}\right),\) \({\rho }{\text{g}}_{\text{i}}\text{i}\text{s} \text{g}\text{r}\text{a}\text{v}\text{i}\text{t}\text{a}\text{t}\text{i}\text{o}\text{n}\text{a}\text{l} \text{f}\text{o}\text{r}\text{c}\text{e} {\mu } \text{i}\text{s} \text{m}\text{o}\text{l}\text{e}\text{c}\text{u}\text{l}\text{a}\text{r} \text{v}\text{i}\text{s}\text{c}\text{o}\text{s}\text{i}\text{t}\text{y}\)

2.1.4. Physical parameters and boundary conditions

The CFD simulation was performed on CDD for gas release process. The fluid was assumed to be a Newtonian and single-phase (liquid-slurry). The density of the slurry was 997.0 kg/m³, and the dynamic viscosity was 8.90 × 10^− 4 Pa·s. The flow modeling was focused on slurry movement from the expansion tank to the main digester to find the optimal degree of mixing. An initial velocity of 0.025 m/s was applied to the top of the expansion tank [17]. The top of the expansion tank was at atmospheric pressure (98100 Pa).

2.1.5. Mesh

Mesh independence analysis was performed on the CDD. The user-defined mesh used was primarily tetrahedral (5-cell, triangular pyramid) with minimal skewness, which had surface quality independence and aligned with the user coordinate system (Fig. 2b). The total number of elements were 160808. The maximum growth rates were constant at 1.2, while the average element quality was 0.96381.

2.1.6. CFD simulation

The commercial CFD software, ANSYS Fluent (V.19.2) was used to simulate the flow field in CDD. The finite volume method was used as the numerical technique. The momentum and continuity equations were discretized using finite differences. A first-order upwind scheme was used for convective terms [22].

2.1.7. Simulation steps

The simulation steps were as follow: (a). The solver was defined as 3D, time-dependent implicit, and pressure based. (b). Laminar flow model was selected for gas release process simulations. (c). Define the material properties of the slurry as water. (d). Configure boundary conditions for single phase. (e). Define the operational conditions by activating the gravitational acceleration. Governing equations (1) and (2) were then solved by using the semi-implicit method for pressure-linked equations (SIMPLE) algorithm [20].

2.2. Semicontinuous AD experiments

2.2.1. Substrate and inoculum collection

The food waste (FW) in this study was freshly collected from the Ohmura Commercial Company Ltd (Saitama, Japan). Over 56.46% wet weight (wwt) of the food waste was vegetables, 21.53% wwt was rice, 14.36% wwt was eggshell, 7.39% wwt was pasta and 0.26% wwt was fish. The food waste was milled to 3–5 mm with a milling machine (RSC-2500/MC, O-Ring Ltd, Japan) and was stored in a refrigerator at 4°C before feeding the digesters. The carbon to nitrogen ratio (C: N) was 9.97:1 and the total solid (TS) and volatile solid (VS) of the food waste were 14.51% wwt and 13.55% wwt, respectively. Milli-Q water was added to maintain HRT of 20 days. The inoculum used for the digester start-up was mesophilic anaerobic digestion sludge, which has been acquired from the Hokubu Sludge Treatment Centre (Yokohama, Japan) with the TS concentration of 3.73% and VS concentration of 1.68%. The obtained sludge was preserved at 37°C for two days before the startup.

2.2.2. Digester design and setup

The CDDs were constructed from polyvinyl chloride (PVC) and the effective volume of each CDD was 15 L, working volume was 11 L and an additional 2 L for the expansion tank. The height of the expansion tank from the base was 25 cm. The schematic diagram of the CDD setup in semicontinuous AD is shown in Fig. 2c. Biogas produced in the CDD was stored in the digester headspace to create pressure for displacing some of the digester slurry to the expansion tank. Also, the effluents were removed from the digester through the expansion tank. The generated biogas was collected in aluminum gasbags.

2.2.3. Digester operation

Semi-continuous anaerobic digestion was performed in four laboratory-scale CDDs to elucidate the effect of mixing frequency on the scum thickness and on the methane yield. All the digesters were operated at the hydraulic retention time (HRT) of 20 days, organic loading rate of 1.2 g- volatile solid (VS) L^− 1 d^− 1. The period of operation was 60 days to achieve the level of threefold HRT. Also, 1.2 L of effluent was removed after every two days from the expansion tank of each digester. Then, the same quantity of freshly prepared food waste slurry was added. The digesters were operated at the temperature of 37° C and at different mixing frequencies, non-mixed (R1), 4 times per day (R2), 6 times per day (R3), and 8 times per day (R4) by opening an electric valve controlled by a timer. The digesters were mixed by hydraulic variation (slurry displacement), while the energy created and utilized during mixing to break scum was derived in the form of potential energy by using Eq. (1):

PE = mgh…….....…………………………………………………….……………… (1)

where PE is the potential energy in joule (J), m is the mass (kg) of the maximum volume of slurry displaced during biogas production (because of pressure build-up due to gas production) each day, g is 9.8 m/s², h (m) is the height of the expansion tank. The volumes of the displaced slurry in the expansion tank were found to be 0.0000 m³, 0.0025 m³, 0.002 m³, and 0.0017 m³ for the digesters R1, R2, R3, and R4 respectively. The mass of the displaced slurry was derived from its volume and density [16].

2.2.4. Monitoring and analytical methods

Temperature was monitored using a digital temperature logger. The pH of feed and effluents were measured using a tabletop pH meter with a probe (B-212, HORIBA, Japan). The effluent samples were analyzed for TS, VS, volatile fatty acids (VFAs: acetate, propionate, and butyrate). The biogas generated was collected in aluminum gas bags every two days and were measured using the water displacement method. Biogas composition was determined in terms of methane (CH₄) and carbon dioxide (CO₂) content. The CH₄ content was measured indirectly by passing the biogas through 3 mol L^− 1 NaOH to absorb the CO₂ present. TS and VS were analyzed using standard procedures (APHA, 2006). VFAs were measured using high-performance liquid chromatography with a Shim-pack Fast-OA high-speed organic acids analytical column (LC-2030C Shimadzu) in combination with the Shimadzu post-column pH-buffered electrical conductivity detection method. The following organic acids were used at an analytical grade: acetic, propionate, butyrate, and valeric acids. Specific biogas and methane yields were expressed as methane produced daily. They were divided by the amount of VS daily fed to the digester and applied to monitor the digestion efficiency of the CDDs. The scum thickness was measured with a ruler and calculated based on the scum thickness. The biogas pressure was monitored with a pressure gauge (TOKO Micro-Pressure Gauge 75 Diameter BL-B-AT G3/8 Diameter 75 x 5 kPa) and the slurry displacement was monitored with a camera (Apexcam Action Camera, 4K 20 Million Pixels, Sony Sensor, WiFi Equipped, 40M Waterproof).

3.1. CFD modeling and simulation

3.1.2. Velocity contours and vectors

The velocity data of the model are captured from the simulation results. The results show the values about the hydrodynamic of the slurry inside CDD, as velocity fields, percentage of volume with dead zones and mixing zones. Figure 3a shows the variation in velocity magnitude in the range of 0–4.2 ms^− 1, self-mixing process (gas release process). The self-mixing process in CDD is due to hydraulic displacement between the main digester and expansion, which is to distribute organic materials in all part of the CDD. The velocity vectors in Fig. 3b, show that the fluid rotates in circular shape at the bottom of CDD, implying that the bottom part was well mixed. The range of velocity magnitude at the bottom was 1.5–3 ms^− 1 and at the top of CDD was 0 to 0.3 ms^− 1. Notably the range of velocity magnitude at the wall opposite to the flow inlet was 0.5–1.3 ms^− 1, across the vertical length of the CDD. The reason why velocity magnitude increased only on one side of the wall was because of the slurry flow direction and the shape of CDD.

3.1.3. Dead zones and mixing zones definition

The dead zones or stagnant zones are the part of the CDD with no slurry flow or very low velocities. The definition of dead zones in this study was chosen according to the definition of Wu and Chen. [23], where the slurry velocities < 0.001 ms^− 1 with respect to percentage volume of CDD are denoted as dead zone, velocities in the range of 0.1-1 ms^− 1 are denoted as medium mixing zone and velocities > 1 ms^− 1 are denoted as high mixing zone. The percentage volume of CDD that was found in the dead zones was 45% (in the middle and at the top, above 15 cm of CDD). This huge volume of dead zones in CDD can nurture massive quantity of scum at the top of CDD, leading decrease in biogas production. The percentage volume of CDD that was found in the medium mixing zone (0.1–1 ms^− 1) was 30% (at the wall opposite to flow inlet). The slurry flow from the expansion tank to the bottom of CDD and to the wall opposite to the flow inlet with low velocity magnitude. The percentage volume of CDD that was found in the high mixing zone (> 1 ms^− 1) was 25% (At the bottom of CDD).

The mixing zones and dead zones are shown in Fig. 3a. The mixing zones are represented by multicolor areas and dead zones are represented in blue spaces, from the front view. Identifying the dead zones and high mixing zones in CDD is an important step to decide the optimum mixing frequency that can increase the velocity magnitude (improve mixing intensity) at the top of CDD to break scum.

3.2. Semicontinuous AD experiments

3.2.1. Methane yield

Methane yields from the four digesters are shown in Fig. 4. Interestingly, these yields varied depending on the mixing conditions. R1 produced the lowest methane yield; it persisted throughout the experiment with an average of 206.39 ± 192.09 mL g-VS^− 1. The methane yield in R2 was the highest, at an average of 601.45 ± 88.80 mL g-VS^− 1, while R3 and R4 had average methane yields of 555.83 ± 59.92 mL g-VS^− 1 and 493.11 ± 109.76 mL g-VS^− 1, respectively. Methane yields in R2, R3 and R4 were nearly constant throughout the experiment, while that from R1 decreased sharply from day 32. Furthermore, R1 was a non-mixed digester, while R2, R3, and R4 were intermittently mixed digesters that were stirred four, six, and eight times per day, respectively. The driver of the low methane yield in R1 and the increase in R2, R3, and R4 was likely the restricted gas release in R1 due to scum formation; the intermittent mixing in R2, R3, and R4 increased the probability of mass transfer from the liquid phase to the gas phase [24]. This inference is in line with the results of Lin & Pearce. [25] and Karim et al. [26], who have identified the effect of intermittent mixing and unmixed modes on methane yield. However, the increase in the yield from R2 over the other intermittently mixed digesters (R3 and R4) was due to the increase in mixing intensity. It was also found that R3 (mixed 6 times mixing per day) and R4 (mixed 8 times per day), with their greater gas release, exhibited lower mixing intensities and were considered insufficiently mixed digesters. Similar results were previously reported by Jegede et al. [16], who concluded that insufficient mixing in digesters can create initiation zones in anaerobic digesters, from where methane-producing bacteria can grow, flourish, and seed the rest of the digester. Overall, these results indicate that different mixing frequencies induced the variation in methane yield.

3.2.2. VFA accumulation

The concentrations of VFAs were analyzed for all digesters (R1, R2, R3 and R4). Acetate, propionate, and i-butyrate were the main VFAs analyzed, and i-butyrate had the highest concentration. i-butyrate is produced mainly from degradation of the branched amino acid valine [27], implying that the substrate was rich in proteins. The high concentration of i-butyrate was due to its slower degradation rate [28]. VFAs concentrations were high in R1, acetate: 39.55 ± 34.91 mg L^− 1, propionate: 85.52 ± 77.98 mg/L, and i-butyrate: 301.77 ± 79.85 mg L^− 1 compared with the mixed digesters. The VFA concentration in R2 were the lowest; only acetate (0.54 ± 1.90 mg L^− 1) and i-butyrate (148.11 ± 92.13 mg L^− 1) were detected. Acetate, propionate, i-butyrate, n-butyrate and i-valeric were detected in R3. However, the VFA concentrations in R3 were relatively low with acetate: 39.71 ± 35.55 mg L^− 1, propionate: 31.25 ± 18.08 mg L^− 1, i-butyrate: 166.98 ± 77.11 mg L^− 1, n-butyrate: 5.34 ± 2.69 mg L^− 1 and i-valeric: 4.28 ± 0.33 mg L^− 1; i-butyrate was the main VFA in R4 with acetate and propionate at lower concentrations. The concentrations of VFAs in R4 were as follows: acetate: 21.61 ± 31.28 mg L^− 1, propionate: 11.45 ± 16.19 mg L^− 1 and i-butyrate: 103.13 ± 96.03 mg L^− 1. The high VFA concentrations in R1, compared with R2, R3, and R4, could potentially have been driven by the inhibition of methanogenesis, given the instability of the system in the absence of mixing [16]. Meanwhile, mixing in the mixed digesters was sufficient to stabilize the digester and prevent overloading. The pH range throughout the experiment was 6.89–8.15, signifying the stability of the process [12, 29]. The stability of the VFA concentration could be due to the pH range. The growth rate of methanogens appeared to be strongly reduced at pH < 6.6 [30, 31] although an excessive decrease in pH can cause microbial granule disintegration, VFA accumulation, and the failure of the process. Wang et al. [32], explored the effects of VFAs on methane yield and methanogenic bacteria growth and reported that at high concentrations of acetate and butyrate (2,400 and 1,800 mg L^− 1, respectively), no significant inhibition of the activity of methanogenic bacteria was detected. However, when the concentration of propionic acid was increased to 900 mg L^− 1, significant inhibition occurred while the concentration of bacteria decreased. In this study, the average concentration of VFAs in all the digesters was < 800 mg L^− 1. Thus, they could be regarded as well-balanced digesters [33, 34]. We surmise that the low concentration of VFAs in digesters R1, R2, R3 and R4 were unlikely to inhibit the methane yield. Moreover, the lower concentration of VFAs in R2 and R3 indicates that acid produced by the microbes during acetogenesis was consumed by the methanogenic bacteria, leaving low inhibition level [33]. These results suggest that mixing facilitated the conversion of VFAs to methane, but this study did not identify any cogent effects of VFA conversion in different mixing conditions.

3.2.3. Scum formation

Figure 5 shows the formation of scum in the four digesters (R1, R2, R3 and R4). This began on day 32 (scum thickness = 0.5 cm) in R1 and progressively increased until day 44 (scum thickness = 2.2 cm). It then stabilized until day 60. Scum formation began in R2 from day 40 (scum thickness = 0.1 cm), and there was not much increase in the former from this time till day 60 (scum thickness = 0.2 cm). Scum formation began on day 38 (scum thickness = 0.2) in R3 and increased till day 60 (scum thickness = 0.9). In R4, scum formation started from day 34 (scum thickness = 0.5 cm) and increased continuously until day 60 (scum thickness = 1.5 cm). The continuous increase in the scum formation in R1 was driven by the non-mixed state of the digester and because gas bubbles were trapped in the scum and could not be discharged [13, 35]. The buoyant force caused by gas bubbles were gradually strengthened as more biogas was produced. It pushed the slurry to go up and then form scum layer on the top of the slurry [36]. The delay in scum formation in R2, R3, and R4 occurred because they were mixed digesters (Fig. 6), and more shear was exerted on the scum layers by the mixing [37]. The scum was nearly completely suppressed in R2 (mixed 4 times per day), likely because more slurry was displaced to the expansion tank therein; it increased the mixing intensity more than the other mixed digesters (R3 and R4). The slight increase in the scum formation in R3 and R4 could potentially have been driven by the limited mixing that did not suppress the formation of scum. Overall, this study reveals that mixing intensity can be improved simply by optimizing the mixing frequency (4 times per day), while scum formation can be suppressed to a minimum.

3.2.4. Effects of scum thickness on methane yield

Figure 7 shows the effects of scum thickness on methane yield at a steady-state period (44–60 days). At the beginning of the experiments (0–31 days), no scum was formed, while methane yield was nearly stable in all mixing conditions. As scum formation was started since the day 32 in R1, a sharp decrease in methane yield emerged as well. The methane yield in R1 was greatly dwindled and, then maintained the low level from the day 32 to 60. However, the scum formation in R2 was started 6 days after that of R1 without any significant increase in scum thickness. Methane yield in R2 was higher and was nearly constant throughout the experiment. Note that these findings go in line with that of Ong et al. [38], who did findings on the anaerobic digestion of cattle manure slurry in impeller mixed digesters (160 rpm) with mixing frequency of 4 times per day and duration of 30 minutes. Specifically, they have demonstrated that gas released from the liquid digestate in intermittently mixed digesters was 70% higher than that in the un-mixed digester. Scum formation was started in R3 from the day 38 and the day 34 in R4. There was a slight increase in scum thickness and a slight decrease in methane yield in R3 and R4. We suggest that the biogas produced in R3 and R4 could have been trapped in the scum without being able to be discharged. The average scum thickness and methane yield of the digesters were determined to be 2.15 ± 0.20 cm and 3.5 ± 6.01 mL g-VS^− 1, 0.23 ± 0.05 cm and 536 ± 10.70 mL g-VS^− 1, 0.83 ± 0.11 cm and 519 ± 39.02 mL g-VS^− 1, 1.31 ± 0.16 cm and 386 ± 34.25 mL g-VS^− 1, for R1, R2, R3 and R4, respectively. As seen, R1 (no mixing) exhibited the highest scum thickness and lowest methane yield, while R2 (4 times per day) exhibited the lowest scum thickness and highest methane yield.

These findings agree with Tian et al. [35] results, where the effects of scum layer on biogas production at different mixing conditions have been reported. Scum layers were formed in the early stage of no-mixing period. Notably, the daily biogas production was decreased by 81.87–87.90% in the digesters with no mixing, compared with the mixed digesters. The reduction of biogas production in the none-mixed digesters was mainly associated with the scum formation, which induced the poor contact of substrate microorganisms [35]. Overall, these results prove that that optimization of mixing frequency can enhance methane yield in CDD without design modification or without adding new parts to improve mixing intensity.

3.2.5. Effects of mixing frequency on slurry displacement

The minimum (R2, 4 times per day) and maximum (R4, 8 times per day) mixing frequencies were examined with an Apexcam action camera for 48 hours to investigate the effect of mixing frequency on slurry displacement. The volume of slurry displaced in the expansion tank at the first hour of gas production in R2 was 0.32 ± 0.05 L and the fifth hour before gas release was 2.02 ± 0.15 L. The gauge pressure of biogas for the first hour in R2 was 0.29 ± 0.07 kPa and the fifth hour was 2.51 ± 0.18 kPa. The volume of slurry displaced in the expansion tank at the first hour of gas production in R4 was 0.31 ± 0.04 L and the third hour before gas release was 0.93 ± 0.14 L. The gauge pressure of biogas for the first hour in R4 was 0.28 ± 0.07 kPa and the third hour was 0.94 ± 0.08 kPa. Notably, the volume of slurry displaced in R2 was more than 2 times the volume of slurry displaced in R4. These results show that more slurry was displaced into the expansion tank at a lower mixing frequency which increased the mixing intensity during gas release operation. This was because more gas was retained in the digester headspace over a long period (six hours) before the next mixing cycle which displaced more slurry into the expansion tank. The above result could explain the reason why scum was suppressed in R2.

3.2.6. Energy required to break scum

The gravitational potential energy (P.E), created by the CDDs was calculated from the results of the effects of mixing frequency on slurry displacement, using Eq. (1). Since scum was broken (suppressed) in R2, the energy required to break scum was calculated from the P.E created in each mixing cycle in R2 (4 times per day). The average P.E created during the six mixing cycles in R2 was 6.12 ± 0.25 Joules per mixing cycle. In R4 (8 times per day), the average P.E created was just 1.80 ± 0.24 Joules per mixing cycle. Notably, the gravitational potential energy (P.E) created in R2 increased threefold compared to P.E created in R4. The energy consumed for breaking scum in CDD was generally low (6.12 ± 0.25 Joules per mixing cycle) and was achieved by the slurry displacement between the main digester and expansion tank as a result of biogas pressure build up in the digester [9]. Importantly, this study demonstrates that by optimizing mixing frequency, the natural P.E created in CDD can break scum without the use of eternal energy.

This study explores CFD simulation to characterize mixing in CDD and the effect of mixing frequency on the performance of semicontinuous AD in CDD to improve mixing intensity, prevent scum formation, and enhance methane yield. The percentage volume of CDD that was found in the dead zones was 45% (in the middle and at the top, above 15 cm of CDD). Identifying the dead zones was a pertinent step to evaluate the optimum mixing frequency that could improve mixing intensity at the top of CDD and break scum in semicontinuous AD experiments. The AD experiments demonstrated that the mixing has facilitated the conversion of volatile fatty acids (VFAs) to methane, while the VFAs concentration in this study was below the inhibition level. Notably, scum formation in CDD with no mixing occurred early and progressively increased until the very end of the experiment, compared with the other mixing conditions. An opposite trend between scum thickness and methane yield was identified. Moreover, the optimum mixing frequency (4 times per day) yielded efficient mixing intensity for (a) suppressing scum, (b) enhancing methane yield and for (c) improving the anaerobic digestion performance. The reason why 4 times mixing per day could suppress scum and enhance methane was because more slurry was displaced to the expansion tank which increased the mixing intensity during gas release process. This study demonstrates that natural P.E of 6.12 ± 0.25 Joules per mixing cycle could break scum without the use of eternal energy. These results prove that optimization of mixing frequency can break scum and enhance methane yield in CDD without any additional investment cost. Ultimately, this finding is particularly valuable for operating anaerobic digestion in CDD efficiently and cost-effectively.

Acknowledgement

This study was funded by “the Private University Branding Research Project (PLANE3T)” from Japan’s Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Science and Technology Agency (JST) (Grant number: JPMJSA 2005), Japan International Cooperation Agency (JICA) and Science and Technology Research Partnership for Sustainable Development (SATREPS). Special thanks to the Ohmura Commercial Company Ltd, Saitama, Japan and Hokubu Sludge Treatment Center, Yokohama, Japan for their support and for providing substrate and inoculum used in this study.

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Figure 8. Effects of mixing frequency on slurry displacement (a) and extraction point in CFD simulation (b)

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Characterization of mixing by CFD simulation and optimization of mixing frequency to break scum and enhance methane yield in Chinese dome digester

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. CFD modeling and simulation

2.1.1. Geometry and operating principle

2.1.2. Model development

2.1.3. Governing equations

2.1.4. Physical parameters and boundary conditions

2.1.5. Mesh

2.1.6. CFD simulation

2.1.7. Simulation steps

2.2. Semicontinuous AD experiments

2.2.1. Substrate and inoculum collection

2.2.2. Digester design and setup

2.2.3. Digester operation

2.2.4. Monitoring and analytical methods

3. Results And Discussion

3.1. CFD modeling and simulation

3.1.2. Velocity contours and vectors

3.1.3. Dead zones and mixing zones definition

3.2. Semicontinuous AD experiments

3.2.1. Methane yield

3.2.2. VFA accumulation

3.2.3. Scum formation

3.2.4. Effects of scum thickness on methane yield

3.2.5. Effects of mixing frequency on slurry displacement

3.2.6. Energy required to break scum

4. Conclusions

Declarations

Acknowledgement

References

Supplementary Files

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