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Integrative Effects of Sonication and Particle Size on Biomethanation of Tropical Grass Pennisetum purpureum Using Superior Diverse Inocula Cultures

Chettaphong Phuttaro
Alissara Reungsang
Piyarat Boonsawang
4 and
Sumate Chaiprapat
Department of Civil Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
Department of Biotechnology, Faculty of Technology, Khon Kean University, Khon Kaen 40002, Thailand
Research Group for Development of Microbial Hydrogen Production Process from Biomass, Khon Kaen University, Khon Kaen 40002, Thailand
Department of Industrial Biotechnology, Faculty of Agro-Industry, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
PSU Energy Systems Research Institute (PERIN), Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
Author to whom correspondence should be addressed.
Energies 2019, 12(22), 4226;
Submission received: 12 October 2019 / Revised: 2 November 2019 / Accepted: 3 November 2019 / Published: 6 November 2019
(This article belongs to the Section A4: Bio-Energy)


Biogas from the fast growing crop, Pennisetum purpureum, has received considerable attention in Southeast Asia since wastewater and bio-waste materials are almost completely utilized. To overcome slow hydrolysis, a rate-limiting step in anaerobic digestion of lignocellulosic biomass, superior microorganism culture, size reduction, and sonication pretreatment were co-applied. In the first experiment, the selection of anaerobic microbial culture to be used in digestion, so-called inoculum, was carried out. Specific anaerobic activities for hydrolysis and methanogenesis of sludge from different sources, a slurry digester of cattle farm (CF) and a wastewater digester of rubber latex factory (RL) were assessed. Results revealed a remarkable synergistic capability in the combined sludge, adding 10% and 49% to the overall biomethanation efficiency over the individual CF and RL sludges. In the second part, interactive effects of size reduction and sonication intensity were studied. Biomethanation efficiency as methane yield increased by 62% by size and 115% by sonication variation, but when optimally combined an additional gain of 40% was recorded. The regression model generated could estimate the energy yield increase as a function of size and sonication intensity with a satisfactory statistical precision R2 of 0.945.

Graphical Abstract

1. Introduction

As the human population and technological transformation, such as robotics, electric vehicle, and machine automation, continue to grow, increase in energy consumption and energy security have become a common threat to the countries with limited energy resources. Dependence on fossil fuels for power generation in the past decades are fading out as more stringent regulations on environmental protection are implemented [1]. This trend will continue as the Paris Agreement on Climate Change is in effect. New forms of energy development rely heavily on clean and sustainable resources. Anaerobic digestion (AD) of organic materials has been widely accepted as an effective means to produce energy in a form of biogas containing an energy-rich methane gas. With the expansion of solar energy integration to the grid, electricity generation from biogas could help neutralize the greater fluctuations in energy availability to a more stable supply. While the main materials for AD has long been industrial wastewaters and food wastes, future direction leans toward lignocellulosic biomass from cultivated fast-growing crops.
Fast growing crops is one way to harness sun energy into intermolecular bonds of biomass, which can later be released as fuels. Many types of high potential grass have been tested for biomethanation with various methane yields, i.e., 112.4 ± 8.4 and 151.0 ± 21.4 L CH4/kg volatile solids (VS) from switchgrass (Panicum vergatum) and cattail grass (Typha latifolia), respectively [2,3]. However, Napier grass (Pennisetum purpureum), a perennial C-4 grass species native to Africa, has received great attention due to its higher methane yield, i.e., 184.6 ± 2.3 L CH4/kg VS [4] with a remarkable productivity, in a range of 438–500 ton/ha/year (59.4 to 78.9 ton DM/ha/year) [5]. Its ability to grow in almost any climate [6] and an alternative use as animal fodder [7] make it so robust that it could economically compete with other energy crops such as maize or beetroot [8,9,10]. Nevertheless, the fibrous constituents in this lignocellulosic biomass are still a challenge for biological degradation. Pretreatments of such biomass could optimally be designed in order to increase its digestibility [11] and hence release higher net total energy through subsequent enzymatic or biochemical conversions.
Pretreatments of the biomass aim to disrupt the cell structure and enhance subsequent bioconversion [12,13]. Biomass pretreatment can be classified into different categories; physical, chemical, biological, or any combination. The main disadvantages of chemical and thermal pretreatment include cost, chemical waste, and by-product inhibitors generated during pretreatment process such as 5-hydroxymethylfurfural (HMF) and furfural [14]. The most effective and practical pretreatment methods for biomass intended for use as feedstock for anaerobic digesters, hence, lie in the physical pretreatments. Ultrasonic waves refer to the energy of sound waves vibrating at a frequency of 20,000 cycles per second or higher [15]. Most of the waves in the frequency range 15–40 kHz are used in food industries, cell-lysis, and pretreatment [16,17] where the cavitation created induce a destructive force onto the biomass. Sufficient energy transmitted through ultrasonic waves is needed to compromise its structure for enzyme penetration. Specific energy (SE) in sonication is deemed as the determinant parameter to dictate the degree of biomass disruption. SE of 284 kJ/kg on dry weight basis of maize straw was sufficient to cause a biogas yield of 41% [18] while SE as high as 6000 kJ/kg on dry basis was needed to gain an additional 441 mL CH4/g VS (55% increase) for organic fraction of municipal solid waste (OFMSW) [19]. This variation of SE applied to different materials such as sewage sludge, animal manure, and some lignocellulosic biomasses [20] is subjected to the specific characteristics of such individual materials. Since the sonication caused cavitation force on the surface of the solid particles, there should be a strong interaction with the available surface area on the particles in the treated volume. Thus, size of biomass particles during sonication could play a big role in the effectiveness of sonication pretreatment. Research on the use of sonication pretreatment with ensilaged Napier grass has not been reported, and to our knowledge it has never been combined with the effect of particle size.
In order to assess the potential of methane generation from organic substrates, biochemical methane potential (BMP) assay is extensively used under different variables of interest. However, discrepancy of results from the same biomass is often found in the literature. To perform BMP accurately, several parameters must be arranged; for example, inoculum to substrate ratio (ISR), substrate (either solid or liquid) concentration, incubating temperature, mixing, and inoculum [21]. One factor that was often overlooked is inoculum. Source of the inoculum must be from a stable system with high microbial activities. In one inoculum, microbial activities of the main steps in anaerobic digestion; hydrolysis, acidogenesis, acetogenesis, and methanogenesis depend on its origin, dictated primarily by the feedstock and digester configuration and operations. These differences could affect the overall biomethanation efficiency of the substrate and inevitably reflect on the accuracy of BMP assay. Since the tailor made inoculum to obtain the highest digestion efficiency of studied substrate cannot be ensured each time, the variation may be normalized by a proper preparation of the inoculum, and the single and mixed sludge of different distinct sources are tested in this study.
While past studies were conducted mostly based on the dried Napier grass from fresh harvest, the Napier grass used in this study was in ensilaged form which would be close to that used in practical full-scale operation. The aims of this current study focus on improving anaerobic digestion of this model lignocellulosic biomass by the combined sonication and size reduction pretreatment. First, the performance of single and mixed inocula in anaerobic digestion of solid substrate was assessed in order to identify the appropriate inoculum for subsequent experiments. Main activities, namely hydrolysis and methanogenesis, were measured with standard substrates and verified in a separate Napier grass silage digestion assay. In the second part, influence of size and sonication pretreatment on digestibility of Napier grass silage was carried out. These results revealed interesting information on the impact of inocula diversity on the digestion efficiency and the accuracy of BMP, as well as the interactive influences of particle size and sonication specific energy in anaerobic digestion of lignocellulosic biomass.

2. Materials and Methods

2.1. Part I: Inoculum Characterization Using Specific Anaerobic Activity Assays

2.1.1. Inoculum Source

Two inocula used in this study were the anaerobic sludges taken from two different full-scale digesters. The first inoculum was taken from an upflow anaerobic sludge blanket (UASB) reactor of a concentrated rubber latex factory (designated as RL) and the second inoculum was from a CSTR digester treating cattle manure of a private cattle farm (designated as CF), both located in Songkhla Province, Thailand. The sludges were then analyzed for total solids (TS) and volatile solids (VS) after field collection. A mixture of RL and CF sludges at equal portion (1:1 ratio) VS basis (designated as MIX) was prepared in order to compare the activities with individual inoculum.

2.1.2. Napier Grass Preparation

A lignocellulosic biomass used in all experiments was Napier grass (Pennisetum purpureum). Fresh Napier grass was collected at 60-day harvest period from Satun Animal Nutrition Research and Development Center, Satun Province, Thailand. After harvesting, fresh grass was cut with a chopper machine (four blades, 2200 revolutions per minute, 8.5 horsepower) to reduce to size 1–3 inches in length then compacted by a hydraulic plunger into a 120-L plastic container and kept for 45 days to ensilage. Fresh Napier grass and the silage were analyzed for elemental composition.

2.1.3. Specific Anaerobic Activity Assays

Three types of inocula, RL, CF, and MIX, were tested for hydrolysis and methanogenesis activities according to [22] using cellulose and acetic acid as substrates, respectively. Both activities were studied at an identical substrate-to-inoculum ratio (COD:VS) of 0.1 g CODsubstrate per g VSinoculum in a reactor effective volume of 60 mL. The initial substrate concentration was set at 1.5 g COD/L.
Subsequently, the Napier grass silage was subject to a series of biochemical methane potential (BMP) assays using the three mentioned inocula to validate the correlation of their hydrolysis and methanogenesis capability to the methane potentials. The BMP assay was run at substrate to inoculum ratio of 1:3 composing of 5 g VS/L of Napier grass silage and 15 g VS/L of inoculum as suggested by [21].
All experiments were performed in batch mode using a 120-mL serum bottle as reactor. Each bottle contained substrate, inoculum, NaHCO3, and stock solution as outlined in [21]. The working volume was adjusted to 60 mL with deionized water and the pH was adjusted to 7.0 ± 0.1 with 0.1 M NaOH or HCl. Anaerobic condition was created by purging the reactor with nitrogen gas for 2 min and sealed immediately with rubber stopper and aluminum cap. A blank set (without substrate) was simultaneously commenced in order to quantify the biogas generated from the inoculum itself, which would be deducted from the treatment sets. All assays were carried out in an incubator shaker (WiseCube WIS-10RL, DAIHAN Scientific Co., Ltd., Gangwon-do, Korea) at 35 ± 1 °C and continuous stirring rate of 150 revolutions per minute. All treatments were conducted in triplicate.
Methane production potential and maximum specific production rate were calculated by the modified Gompertz equation [23], Equation (1).
H ( t ) = H . exp { exp [ e . R m H ( λ t ) + 1 ] }
where H(t) is cumulative methane production (mL) at time t; λ is lag phase time (days); H is methane production potential (mL); e is exp(1) = 2.71828; Rm is maximum methane production rate (mL/d).
Theoretical digestion of Napier grass silage (Equation (2)) was calculated based on the modified Buswell and Mueller equation [24]. The theoretical methane yield was found to be 434.0 L CH4/kg TS at STP.
C1065.4 H1619.4 O695.5 N16.0 S + 325.4H2O → 550.5CH4 + 509.9CO2 + 16.0NH3 + H2S.

2.2. Part II: Impact of Sonication and Size Reduction on Digestibility of Napier Grass Silage

2.2.1. Inoculum

The selected inoculum based on the results in Part I was employed in the experiment of Part II. The fresh sludges from two anaerobic digesters (rubber latex factory and cattle farm) were collected and screened to remove course materials and other impurities before being analyzed for VS, then mixed at equal volatile solids concentration. The blank set which used the new batch of mixed sludge as inoculum was simultaneously conducted along the treatment sets.

2.2.2. Pretreatments of Substrate and Biochemical Methane Potential Study

Napier grass silage prepared according to Section 2.1.2 was cut by a commercial cutting machine, then sieved into three size ranges of 0.6–2.0 mm (S), 4.8–9.5 mm (M), and 16.0–25.0 mm (L) using an ASTM E-11 standard sieve series (Endecotts LTD., London, England). Sonication pretreatments of Napier grass silage were performed in a 100-mL glass vessel with a probe-type transducer equipped with power generator Model AKHGZ-50420K (ACME-KORN, Nonthaburi, Thailand) operated at 18 kHz, 1000 watts. The Napier grass silage was mixed with distilled water to form a slurry at concentration 15 g VS/L, then placed in the sonication reactor. The sonication time varied from 5, 10, 15, and 30 s, the input energy of sonication pretreatment to the sample was termed specific energy (SE) and can be calculated according to Equation (3) [25], which corresponded to the sonication SE of 556, 1111, 1667, and 3333 kJ/kg TS, respectively. The sonication vessel temperature was controlled below 40 °C at all times by air cooling system.
S E = P × t V × T S ,
where SE is specific energy (kWs/kg TS or kJ/kg TS), P is the sonication power (kW), t is sonication time (s), V is the volume of sample (L), and TS is the total solids concentration (kg/L) of sample.
Full factorial design experiment was conducted at three levels of size and five levels of sonication SE that totaled 15 treatments. The pretreated slurry of Napier grass silage was then tested by Biochemical Methane Potential (BMP) assays. The protocol employed was similar to that described in Section 2.1.3. The substrate slurry and inoculum were placed in each reactor to make the final concentrations of 5 and 15 g VS/L, respectively, forming an ISR of 3:1. BMP assay lasted 41 days when the biogas production rate ceased below 3% of the maximum, then the experiment was terminated.

2.3. Analytical Methods

All substrates (fresh, silage, and pretreated silage) and inocula were analyzed for TS and VS according to the Standard Methods [26]. In sonication pretreatment experiment, supernatant was measured for soluble chemical oxygen demand (SCOD) using a Merck COD Spectroquant® solution (range 100–1500 mg/L) and a Merck Spectroquant® Pharo 100 instrument (Merck, Darmstadt, Germany). The fiber composition including neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) of the Napier grass silage were analyzed using detergent fiber technique according to the Van–Soest method [27], and the composition of cellulose, hemicellulose, and lignin were calculated. The elemental compositions of the Napier grass including carbon, hydrogen, nitrogen, oxygen, and sulphur were quantified by Dynamic Flash Combustion Technique using CHNS-O Analyzer (CE Instruments Flash EA 1112 Series, Thermo Quest, Milan, Italy). The morphology of non-pretreated and pretreated Napier grass silage was observed using a scanning electron microscope (JSM5800, JEOL, Tokyo, Japan) at an acceleration voltage of 15 kV.
The biogas production was measured using 10-mL Loss-of-Resistance (LOR) Syringe with 0.2 mL scale (B. Braun Medical Inc., Bethlehem, PA, USA). The collected gas sample was immediately analyzed for biogas composition using a gas chromatography system (Agilent 7820A, Agilent technologies Inc., Palo Alto, California, USA) equipped with a stainless steel packed column Hayesep Q80/100, 8’ × 1/8”. Helium (He) was used as carrier gas, and CH4, CO2, and N2 composition was determined by thermal conductivity detector (TCD).

2.4. Statistical Regression and Analysis

All experiments were conducted in triplicate where means and standard deviations were calculated and compared. The significant difference was determined at a significance level of 0.05 (p ≤ 0.05) using one-way analysis of variance (ANOVA) by SPSS software version 22.0. The interested response, methane yield increase, was measured as percentage compared to those of the non-pretreated silage (control). The relationship of the response to the independent variables, particle size (SIZ) (three levels), and sonication specific energy (SE) (five levels) was determined using quadratic regression (Equation (4)) with statistical significance of α = 0.05 by the Design Expert software (version 8.0.6, Stat-Ease, Inc., Minneapolis, MN, USA). The graphical illustration of the variable correlation was displayed in a three-dimensional plot with surface profile and contour of iso-surface connecting points with the same value, which is analogous to a contour plot.
Y = β 0 + i = 1 k β i X i + i = 1 k β i i X i 2 + i = 1 , < j k 1 j = 2 k β i j X i X j ,
where Y is the predicted response, β0 is a constant, βi is the linear coefficient, βii is the squared coefficient, βij is the cross-product coefficient, k is the number of studied variables, and Xi is the actual value of the studied independent variables.

3. Results and Discussion

3.1. Anaerobic Activities of Single and Mixed Inocula

Anaerobic microorganisms from digesters that are fed with different substrates could have a distinct community structure which therefore displays differences in anaerobic digestion activities. The specificity and familiarity of microorganisms with substrate and environmental condition are important determinants of the metabolic pathway in each step of anaerobic digestion [28]. In this experiment, the activities of microorganisms in hydrolysis and methanogenesis were tested by the specific substrate where the kinetics of methane evolution were analyzed. The cumulative methane production profiles of RL, CF, and MIX inoculum for each activity are shown in Figure 1a,b.
Considering the good agreement between data and model prediction, the modified Gompertz model (Equation (1)) was able to describe the metabolic activity in the reactors at sufficient precision. This was confirmed by high coefficient of determination (R2) above 0.97 in all tested conditions (Figure 1). The small standard deviation of data points implies the repeatability of the experiments and validity of the data acquisition.

3.1.1. Hydrolysis Activity

In the anaerobic digestion pathway, hydrolysis and methanogenesis are always the rate-limiting steps depending on the type of substrate. For solid substrates that are quite recalcitrant to biodegradation, such as plant biomass, hydrolysis is key to the overall success in anaerobic digestion. Therefore, methane evolution reflects the degree of hydrolysis and could be justified to represent the hydrolysis activity and this concurs with the protocol outlined by [22]. In order to reaffirm such analogy, the substrate utilization rate of CF (RCF) and RL (RRL) inoculum were monitored by the disappearance of the substrate (cellulose) and overlaid with methane evolution profiles (Figure 1a). It was clear that CF sludge possessed higher substrate utilization rate than RL sludge. The highest RCF was 73.4 ± 0.1 mg SCOD/h at 12 h versus only 41.3 ± 2.4 mg SCOD/h of RRL. This information corresponded well to the methane yield shown as hydrolysis activity therein. The familiarity of microbial community from cattle farm CSTR digester to fibrous feed such as that in the rumens of grass fed animals played an important role in efficient hydrolysis [29]. The microorganisms involving in hydrolysis reaction were found abundant in cattle feces, i.e., Clostridium spp., Bacteroides spp., Porphyromonas spp., and Prevotella spp. [30]. On the other hand, RL sludge in full scale UASB exposed to rubber latex wastewater which is composed majorly of carbohydrate, sugar, and protein [31,32].
At 58 h, cumulative methane yield in hydrolysis activities of the sludges were 10.84 ± 0.37, 10.54 ± 0.66, and 5.57 ± 0.21 mL CH4/g VSinoculum for MIX, CF, and RL, respectively (Figure 1a). Statistical analysis shows that the hydrolysis activity of MIX and CF were equivalent but significantly (p < 0.05) higher than RL. Biogas evolution rate from complex substrate (cellulose) digestion (Figure 1a) was clearly slower than soluble acetate in methanogenesis assays (Figure 1b). CF sludge showed a longer hydrolysis lag phase of 8.40 ± 0.16 h while MIX provided the shortest lag phase of only 1.96 ± 0.07 h. It was perhaps due to the synergism effect in MIX where available intermediates can be readily converted to methane by the more active methanogens in RL sludge. CF sludge also presented the longest lag phase in those methanogenesis activities (Figure 1b). By adding RL, hydrolysis lag phase of MIX was reduced to 2.0 ± 0.1 h (a reduction of 6.4 h) compared to CF single sludge (8.40 ± 0.16 h). In other words, alteration of microbial community composition by concentrating CF sludge with high methanogenesis sludge (RL) at 50% (by VS) could slightly enhance the overall hydrolysis activity instead since the hydrolysis end products were removed from the culture faster. This clearly showed the synergistic effect of the microbial diversity.

3.1.2. Methanogenesis Activity

In methanogenesis activity test, RL sludge responded to the acetate much quicker than CF sludge as evidenced by a shorter lag phase (2.21 ± 0.02 h versus 12.76 ± 0.07 h) in Figure 1b. This high methanogenic rate was probably inherited from the system operation of the granule forming reactor (UASB) and more soluble substrate in concentrated rubber latex wastewater. The layered structure in anaerobic granule from outer to inner layers of acidogens, acetogens, and methanogens is known to aid the interspecies substrate transfer by shortening the transfer distance. This fact made each group more active [33] and the granule contains less cell debris in the sludge sample compared to disperse sludge, especially that from a cattle manure digester. The maximum methane production rate belonged to RL sludge with the shortest lag phase.
In previous literature, it was reported that anaerobic organisms successively adapted their communities by acclimatizing to the new substrate in a couple of weeks [34,35] but, in the short assay (less than 3 days) as such conducted in this study, the hydrolytic bacteria would not substantially build up their community in time. Therefore, single sludge may not be a good inoculum for BMP assay. Furthermore, the sludge from one digester, although familiar with that type of substrate, in some cases, was reported to have lower methane production than the exogenous inoculum when digesting the similar substrate [31,36]. It suggests that the inoculum from a single active digester is not likely to be an appropriate inoculum for BMP assays of an arbitrary substrate.

3.1.3. Verification Test with Biomass Digestion

Napier grass silage was used as the substrate in this test in order to validate the correlation of the activity results previously determined with standard substrates of MIX, RL, and CF sludges. Figure 2 illustrates the cumulative methane yield from different inocula used. All results were fitted to the Gompertz model (Equation (1)). MIX sludge clearly produced methane faster than CF and RL from the beginning. The ultimate methane production of MIX was 140 ± 5 mL CH4/g VSsubstrate which is statistically higher than CF and RL (126.8 ± 5 and 96.7 ± 6 mL CH4/g VSsubstrate) at α = 0.05. The results from the anaerobic digestion performance of the biomass were in good agreement with the results from hydrolysis and methanogenesis activities (Figure 1). Moreover, it is clear that there was a synergistic effect in MIX sludge that combined the better of two activities from each sludge. Collectively, it resulted in the highest methane yield in the biomass digestion. The fact that RL was the poorest inoculum in the biomass digestion signifies the great importance of hydrolysis activity for lignocellulosic digestion.
When hydrolysis activity, methanogenesis activity, and biomass digestion performance were considered, MIX sludge apparently outperformed the individual sludges. Thus, by mixing the sludges from different sources, the quality of the inoculum is significantly improved, and leads to the more accurate derivation of the methane production potential for solid substrate. It should also follow the same analogy for liquid substrate, especially those with high suspended solids. Yet, it is always wise to mix the sludges that digest different kinds of substrate (solid and liquid), and that are active such as those with less debris.

3.2. Digestibility of Napier Grass Silage under Size Reduction and Sonication Pretreatments

As many of the previous studies used raw Napier grass mostly in dried form for AD experiments, there are some inevitable offsets in the digestibility and biogas yield due to the changes of the biomass properties (Table 1). Napier grass silage was used as substrate in this research to demonstrate a realistic yield in AD plants since the great energy required to dry fresh biomass is too costly in full-scale operation. Percentage of TS and VS of Napier grass silage increased due to the loss of moisture during ensiling process (Table 1). Some of the moisture may become a running liquid leaving the silage pile, and some carbon was converted to lactic acid, volatile fatty acids, and carbon dioxide [37] resulting in a change in the biomass composition as shown in Table 1. Although there could be some discrepancies of the Napier grass composition from different literature as plant age, soil, and weather condition play a big role in plant physiology and cell development, the Napier grass used in this work was well within the range of the reported value of this biomass [38,39].

3.2.1. Effects of Particle Size of Napier Grass Silage on Methane Production

Figure 3 demonstrates that the cumulative methane yield was directly affected by particle size. From the beginning, the small sized set generated the highest methane volume. The particle size also corresponded well with methane production rates (Rm) which are 9.11 ± 0.61, 8.44 ± 0.23, to 5.96 ± 0.85 mL/d for small, medium, and large grass sizes, respectively, while only the large sized set exhibited a lag phase (0.63 ± 0.37 days). Statistical analysis clearly suggested that the reduction of particle size had a significant (p < 0.05) impact on AD of the biomass. Methane yield at 41 days varied inversely to particle size which increased from 91.0 ± 10.9 to 115.6 ± 4.4 and 147.8 ± 7.3 mL CH4/g VSsubstrate for large to medium and small size, respectively.
There was a 27% increase of methane yield that resulted from the first size reduction from large to medium, and another 35% from medium to small size cut. The total improvement in methane yield of 62% was achieved by the increase in contact area per unit weight of the solid substrate, which enabled microbial enzymes to work on the expanding surface. Although the microorganisms could utilize the substrate more rapidly and more completely for smaller sizes, the energy spent into cutting and grinding compared to the gain in energy from increased methane yield might not be offset, especially when the size range is approaching a certain limit, beyond where there is only an insignificant gain [40]. In the size range tested here, the small size was still attained by a typical commercial cutting machine which is applicable for general AD plants.
However, some studies reported the opposite correlation between biomass particle size and methane yield. Methane production from anaerobic digestion of banana peel was lowered with smaller size (1 mm) particles compared to the larger ones [41], while [42] reported the lowest cumulative methane production from the smallest size of food waste with a mean particle size of 0.393 mm. The smallest particles can easily be digested which accelerated hydrolysis and acidogenesis in AD and could result in excessive volatile fatty acids (VFAs) accumulation as the rate of acidogenesis is typically faster than the methanogenesis. Over-acidification in anaerobic culture will obstruct the metabolism of microbial cells, especially the methanogens which are most sensitive. Thus, care must be taken with substrates having high portion of rapidly degradable organics, such as starch or soft tissue plants. In a continuous AD operation, organic loading rate (total mass of substrate fed to the reactor per unit volume per day) may not be sufficient as a meaningful control parameter when the property of substrate is altered by pretreatment. The digestibility rate could be much improved in pretreatment and easily causes over acidification. Thus, organic loading rate must be matched with the digestibility rate of the substrate [4].

3.2.2. Effects of Sonication Pretreatment on Methane Production

Large size Napier grass silage was used as substrate in this experiment in order to restrict the effect of particle size in the experiment. Results show that SCOD of liquid portion in pretreated slurry increased with elevated SE (Table 2). SCOD was increased approximately 6.1, 9.3, and 12.3 fold (compared to control) when applying SE of 556, 1111, and 1666 kJ/kg TS, respectively. As SE increased further to 3333 kJ/kg TS, only a 12.4 fold rise (an additional 0.1 fold increase) was gained compared to the control, indicating a diminishing return in the solubilization of SCOD. There was virtually no gain at this SE level. The (whole) slurry mixture was then brought to batch digestion BMP assay to test for changes in methane production potential.
At the end of the 41-day digestion, methane yield increased by 54.9%, 94.4%, 111.8%, and 115.5%, compared to the control, at SE 556, 1111, 1667, and 3333 kJ/kg TS, respectively. In all sonication conditions, methane yield dramatically increased in the first 10 days and progressed at a slower pace towards the end (Figure 4). This pattern indicated that the rapidly biodegradable components were converted first followed by the more resistant structure [43]. At any interval during digestion, statistical analysis indicated that methane yield between sonication pretreatment of 1667 and 3333 kJ/kg TS are not significantly different (p > 0.05), implying no further gain of digestibility beyond 1667 kJ/kg TS. The same phenomenon was observed in ultrasonic pretreatment of waste activated sludge. Not only that SCOD, soluble protein, and carbohydrate concentrations were not significantly increased after the optimal pretreatment time, but instead resulted in lower biogas and methane production due to the releasing of long-chain fatty acids from lipid or protein during the long time of the ultrasound [44]. It should be noted that SE was adjusted by extending the sonication time to the sample. This phenomenon was simply caused by the lag of dissolved gas to form microbubbles in water, which would oscillate and burst to create the shear force in cavitation/expansion cycle [45]. The fact that the dissolved air in liquid medium is driven off over time suggests that a too long sonication period will guarantee a diminishing return on plant cell destruction and the effectiveness of sonication. Additionally, when over-sonicating the biomass, there could be a slight increase in phenolic compounds in the solution from lignin exposed to a high temperature causing microbubbles to burst, which can be inhibitory to microorganisms in AD [46].
Scanning electron microscopy was used to observe the changes in surface morphology of the treated and non-pretreated biomass particles. Figure 5a shows a smooth surface with recognizable stomata and guard cells of the non-pretreated Napier grass silage. The biomass surface was noticeably broken as a result of only 5-s ultrasonic pretreatment (Figure 5b). Some parts of the leaf surface were destructed and detached. Disintegrated particles were released into the medium and further attacked by the sonic wave at higher contact area which quickly turned the medium color to greenish and increased the soluble fraction in liquid medium. The increasing microscopic channels observed in the structure of the biomass in higher magnification SEM pictures indicated the effect of the cavitation bubble bursts. However, sonication time longer than 10 s did not impose obvious changes further to the grass surface. This observation corresponds well with the data in Figure 4. In another preliminary test (data not shown), the dried Napier grass (from fresh biomass without ensilaging) was more recalcitrant to sonication than the ensilage one, showing very minimal changes at the same level of sonication here.

3.2.3. Interrelationship of Particle Size and Sonication Intensity on Anaerobic Digestibility

Except for small sized cuts, SCOD in the supernatant increased with sonication specific energy up to 1111 kJ/kg TS, as shown in Table 2. ANOVA confirmed that the effect of sonication was lesser above 1111 kJ/kg TS. At the end of 41-day digestion, the small sized grass provided the highest digestibility at any sonication intensities. The higher specific surface area (m2/kg) belonging to the small particle size grass provided more space not only for cavitation bubble collision but also for microbial enzymes to be in contact. Digestibility of all three sizes were not significantly different (p > 0.05) at sonication SE above 1111 kJ/kg TS (Table 2) because these SEs were already over the optimum condition.
To examine the effect of size and SE simultaneously, the term “increase methane yield” at different conditions were calculated against control (non-pretreatment large size) in percentage change, and then regressed with independent variables of (1) median value of each particle size range (1.3, 7.1, and 20.5 mm) and (2) SE of sonication. The quadratic regression model (Equation (5)) was then generated as a function of these two independent variables. Analysis of variance (ANOVA) was performed to determine the significance and adequacy of the model. The quadratic regression model was statistically valid with such a low F-value of <0.0001 as well as the acceptable coefficient of determination (R2) and coefficient of variation (C.V.) of 0.9450% and 11.98%, respectively. The model can adequately describe the relationship of these parameters with a satisfactory precision over the range of parameters tested. The graphical display of the relationship is plotted in 3D as shown in Figure 6.
Increase CH4 yield (%) = 71.297 − 9.253(SIZ) + 0.082(SE) + 3.088 × 10−4(SIZ × SE)
+ 0.304 × (SIZ2) − 1.673 × 10−5(SE2).
The effect of sonication SE appeared to intensify with the smaller size grass which also reflected in the increase in methane yield. This phenomenon was identifiable by the smaller gaps between the iso-surface contours at smaller sized grass (Figure 6). A higher SCOD release from small sized grass (Table 2) helped explain such occurrence by the heightened initial digestion rate compared to medium and larger sizes. However, this synergistic interaction between the two parameters dissipated as the sonication energy rose to around 1100–1200 kJ/kg TS where the surface started to flatten.
Figure 6 also shows that the increase CH4 yield was improved with rising SE to about 2500 kJ/kg TS. At any SE, small particle size (1.30 mm) provided a higher increase in CH4 yield than larger sizes in all sonication levels. Numerical optimization of Equation (5) showed the highest increase in CH4 yield from this study to be 161% at size 1.3 mm and SE 2458 kJ/kg TS, which is near the edge of the contour in Figure 6. SE beyond the optimum condition resulted in the declining gain in CH4 yield for all biomass sizes, possibly due to the generation of thermally induced inhibitors, such as lignin derivatives, from microsite cavitation burst of the remaining microbubbles, which generated high temperature [47]. An effective size to capitalize the aid of sonication by this analysis would be around 10 mm or smaller where the slope of the increase CH4 yield started to rise. Sizes up to around 5–6 mm would give equivalent biomethanation increase at sonication SE 2458 kJ/kg TS. The contour plot clearly shows the interaction between size and sonication energy, and suggests an appropriate combination between the two pretreatment parameters.
In order to perform the energy balance, the energy input from the shredding machine that cut the grass to different sizes must be evaluated. Upon our review of the literature, it was found that there are various factors affecting the energy spent on cutting of biomasses such as fiber toughness, moisture, types of wood/material, type and design of cutting machine, feed rate to the cutting machine, initial size, desired final size, etc. It is also not accurate to estimate the energy from the machine used in this experiment, as it is not properly designed and optimally operated. There is no reliable information on this subject for particular biomasses. Further studies on the energy requirement for size reduction on different biomasses and final sizes should be explored.
Size reduction and sonication pretreatment could give a two-fold benefit. On one hand, since the digestion rate is accelerated under the combined pretreatments, the material retention time in the reactor could be shortened. Based on our results, to attain the same biomethanation efficiency of the large size Napier grass at 41 days (91 mL CH4/g VSsubstrate), the small size sonicated Napier grass required only 6.8 days in batch digestion. In continuous operation, this could lead to a smaller reactor size provided that the feed rate of the pretreated substrate must be carefully controlled to prevent over-acidification in the reactor by the rise in readily degradable organics. This phenomenon of soluble organic overloading by the substrate pretreatment was observed in [4]. On the other hand, a more complete degradation of the pretreated substrate gives higher methanation efficiency at equal retention time. Biogas production could then be enhanced. With careful balance between the energy invested in size reduction and sonication, higher feasibility of the biogas production facility could be realized. While the size reduction is simple to implement, effective sonication of the substrate is still challenging. It is also possible that a portion of the digester content can be mixed with the incoming feed in order to provide dissolved biogas for effective sonication. Alternatively, the digester content can be retracted, sonicated, and returned back to the digester while the grass is fed to the digester directly.

4. Conclusions

Quality of anaerobic sludge used as inoculum affected the accuracy of biochemical methane potential assay. By using specific anaerobic activities to indicate the ability of individual sludges, the mixed sludge was found to possess the collective abilities and showed higher digestion efficiency of lignocellulosic substrates. In the second part, size and sonication energy were found to significantly improve methane yield of the Napier grass silage. When combined, methane yield improved by 155% at small size (0.6–2.0 mm) and SE 3333 kJ/kg TS, compared to only 62% and 115% by size reduction (small size) and sonication (3333 kJ/kg TS), individually. The optimal condition found from the regression model was at 1.3 mm and 2458 kJ/kg TS, but sizes up to around 5–6 mm should still be sufficient to obtain equivalent improvement. The effectiveness of the two parameters could be integratively optimized.

Author Contributions

Conceptualization, A.R.; Data curation, P.B.; Investigation, C.P.; Resources, A.R.; Supervision, S.C.; Writing—original draft, C.P.; Writing—review and editing, S.C.


This research was financially supported by Thailand Research Fund (TRF) through the Royal Golden Jubilee Ph.D. (RGJ-PHD) Program (Grant No. PHD/0156/2554), Graduate School of Prince of Songkla University, The Thammasat University Research Fund under the Research University Network (RUN) initiative, and TRF Senior Research Scholar Grant No. RTA6280001.


The authors would like to thank Satun Animal Nutrition Research and Development Center, Satun Province, Thailand for Napier grass biomass and anaerobic inoculum, Chalong Latex Industry Co., Ltd. Songkhla Provice, Thailand for anaerobic inoculum, Energy Technology Research Center, Prince of Songkla University for the sonication apparatus.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Anaerobic activity of different inocula; (a) hydrolysis activity, (b) methanogenesis activity (symbols = observed data, lines = predicted data by Equation (1)). Cellulose depletion in terms of substrate utilization rates of rubber latex factory (RL) and cattle farm (CF) sludges (RRL and RCF) over time are plotted to support hydrolysis activity data.
Figure 1. Anaerobic activity of different inocula; (a) hydrolysis activity, (b) methanogenesis activity (symbols = observed data, lines = predicted data by Equation (1)). Cellulose depletion in terms of substrate utilization rates of rubber latex factory (RL) and cattle farm (CF) sludges (RRL and RCF) over time are plotted to support hydrolysis activity data.
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Figure 2. Methane yield from Napier grass with difference sludges (symbols—observed data, dashed lines—predicted data with the Gompertz model).
Figure 2. Methane yield from Napier grass with difference sludges (symbols—observed data, dashed lines—predicted data with the Gompertz model).
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Figure 3. Cumulative methane yield from Napier grass at different sizes (symbols—observed data, dashed lines—predicted data with the Gompertz equation).
Figure 3. Cumulative methane yield from Napier grass at different sizes (symbols—observed data, dashed lines—predicted data with the Gompertz equation).
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Figure 4. Methane yield of Napier grass silage (large size) at different levels of sonication pretreatment during 41-day digestion.
Figure 4. Methane yield of Napier grass silage (large size) at different levels of sonication pretreatment during 41-day digestion.
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Figure 5. Napier grass silage (large size) with different sonication intensity (a) non-pretreated, (b) 556 kJ/kg TS (5 s), (c) 1111 kJ/kg TS (10 s), and (d) 1667 kJ/kg TS (15 s).
Figure 5. Napier grass silage (large size) with different sonication intensity (a) non-pretreated, (b) 556 kJ/kg TS (5 s), (c) 1111 kJ/kg TS (10 s), and (d) 1667 kJ/kg TS (15 s).
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Figure 6. Surface and contour plot of the increase in methane yield as a result of size and specific energy in sonication pretreatment.
Figure 6. Surface and contour plot of the increase in methane yield as a result of size and specific energy in sonication pretreatment.
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Table 1. Characteristics of fresh Napier grass and Napier grass silage used in the experiments.
Table 1. Characteristics of fresh Napier grass and Napier grass silage used in the experiments.
Proximate analysis
Moisture (%)86.277.8
TS (g/kg wet wt.)138.4232.5
VS (g/kg dry wt.)918.8892.5
Cellulose (%)37.442.8
Hemicellulose (%)29.224.6
Lignin (%)9.611.3
Ultimate analysis
C (%)41.839.9
H (%)5.35.1
N (%)1.50.7
S (%)0.10.1
O (%)28.432.3
Ash and others (%)22.921.9
Table 2. Soluble chemical oxygen demand (SCOD) and digestibility of Napier grass silage after sonication pretreatment.
Table 2. Soluble chemical oxygen demand (SCOD) and digestibility of Napier grass silage after sonication pretreatment.
ParametersSpecific Energy (kJ/kg TS)
SCOD (mg COD/L) *
Small121.00 ± 12.47 a610.00 ± 8.16 b813.33 ± 10.27 c831.67 ± 2.36 c891.67 ± 10.80 d
Medium128.33 ± 21.60 a623.33 ± 25.50 b711.67 ± 11.79 c723.33 ± 13.12 c725.00 ± 13.12 c
Large23.33 ± 8.16 a141.67 ± 8.16 b216.67 ± 6.24 c286.67 ± 10.27 d288.33 ± 8.50 d
Digestibility at 41 days (%)
Small34.06 ± 1.69 a39.53 ± 1.43 ab48.59 ± 3.22 bc51.85 ± 2.30 c53.51 ± 2.06 c
Medium26.64 ± 1.00 a36.90 ± 1.57 b40.69 ± 2.28 bc43.77 ± 1.46 bc43.63 ± 1.80 c
Large20.97 ± 2.52 a32.48 ± 0.20 b40.75 ± 1.8 c44.41 ± 1.12 c45.19 ± 1.12 c
Note * SCOD was subtracted by the control (Napier grass soaked in DI water for equal time in each sonication level). Comparison of means was carried out within the same size across the specific energy levels. Numbers (mean values) followed by the different superscript letter (a, b, c and d) in the same row are significantly different at α = 0.05. Means followed by the combination of letters and the letter therein such as “ab” and “a” are not statistically different.

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Phuttaro, C.; Reungsang, A.; Boonsawang, P.; Chaiprapat, S. Integrative Effects of Sonication and Particle Size on Biomethanation of Tropical Grass Pennisetum purpureum Using Superior Diverse Inocula Cultures. Energies 2019, 12, 4226.

AMA Style

Phuttaro C, Reungsang A, Boonsawang P, Chaiprapat S. Integrative Effects of Sonication and Particle Size on Biomethanation of Tropical Grass Pennisetum purpureum Using Superior Diverse Inocula Cultures. Energies. 2019; 12(22):4226.

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Phuttaro, Chettaphong, Alissara Reungsang, Piyarat Boonsawang, and Sumate Chaiprapat. 2019. "Integrative Effects of Sonication and Particle Size on Biomethanation of Tropical Grass Pennisetum purpureum Using Superior Diverse Inocula Cultures" Energies 12, no. 22: 4226.

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