3. Results and discussion
The data presented in Table 1 demonstrates the characteristics of food
wastes and maize straw before being mixed to be used as substrate for
anaerobic digestion process. The predominant macromolecule of food
wastes was lipid, exhibited 37.4% of total macromolecules. The protein
fraction was approximately 21% of total macromolecules. Both
macromolecules summed up 60% of total food wastes macromolecules,
mediating C/N ration to be relatively low (14). Opposite, the prevailing
content of C-rich molecules in maize straw mediated a higher C/N ratio
to be 53, which acts as a limiting factor for the regular growth of
bacteria. Macromolecules distribution and elemental analyses attained
for food wastes and maize straw is in good agreement with literature
(Algapani et al., 2016; Peng et al., 2016). The characteristic results
of both substrates indicated that both substrates were far away of ideal
C/N ratio for anaerobic digestion i.e. 20 to 30 (Nayak and Bhushan 2019)
and this ratio thus needs to be adjusted. In this study, the mixture of
N-rich food waste and C-rich maize straw by (1:1) had a balanced C/N
ratio of 33.
3.1 Process performance
under stable and disturbed states targeting OLR
As shown in Fig. 1a, in the first stage, when the OLR was promoted from
2.6 to 5.4, 7.9 and 10.2 g VS L-1
d-1, the biogas production gradually increased from
0.9 to 3.0, 4.6, and 5.7 L L-1 d-1,
respectively. However, an additional increase in OLR (17.1 g VS
L-1 d-1) resulted in a sharp
decrease in the biogas production. The highest specific methane yield
measured under a steady-state was 393±25 mL-CH4 g
VSin-1 at 7.9 g VS
L-1 d-1 of OLR. No-significant
(p >0.05) reduction was observed with the OLR
promoted to 10.2 g VS L-1 d-1 (i.e.
385±25 mL g VSin). Contrary, the specific methane yield
significantly (p <0.05) decreased down to 348±59 mL g
VSin at 5.4 g VS L-1
d-1. Results reported in previous studies were less
than that attained in current study in both the thermophilic and
mesophilic AD processes of agro-food wastes (Hobbs et al., 2019; Shi et
al., 2018) which may be more likely related to the C/N balance and/or a
good synergistic of such substrates. Remarkably, the specific methane
yield dropped suddenly at 17.1 g VS L-1
d-1 of OLR (Fig. 1b), implying process overloading and
thus AD process was partially inhibited. These results indicated that
the capacity of the process could be increased with increasing OLR up to
10 g VS L-1 d-1 without affecting
the process stability, however a closer look into common intermediates
could be helpful to elucidate the reason behind the observed methane
yield reduction at highest OLR.
In Fig. 1c, the total VFAs content in the reactor varied between 124-398
mg L-1 under different OLRs up to 10.2 g VS
L-1 d-1. All VFAs values were much
lower than 1000 mg L-1, the threshold reported as the
levels in which acid suppression becomes evident (Chen et al., 2012),
implying that the activity of anaerobic microbiome (acidogens, acetogens
and methanogens) was balanced and consequently, proper operation concert
was achieved. Nevertheless, the OLR of 2.6 and 5.4 g VS
L-1 d-1 were not enough for an
efficient performance and thus their methane yields were lower than
higher OLR. On contrary, once the OLR reached 17.1 g VS
L-1 d-1, the VFAs accumulated
sharply to 4730 mg L-1 which triggered the cessation
of the methane production (Table 2 and Fig. 1). It seems likely that
additional increase in OLR over than 10.2 g VS L-1
d-1 led to organic matters overload and accordingly,
unbalance equilibrium between acidogenesis/acetogenesis and
methanogenesis took place. These results signified that different
microbial groups might be influenced differently at variable OLRs. The
acetate is the main driver for the overall methane production,
nevertheless, the accumulation of acetate (1,411 mg
L-1 in this study with an OLR of 17.1 g VS
L-1 d-1) could hamper not only
acetogenic bacteria but also the degradation of propionate (Wagner et
al., 2014). By this way, the propionate/acetate ration could serve as a
reliable indicator for bacteria stress in overloaded digesters and
impending failure (Marchaim and Krause 1993). In this study, the VFA
composition for AD carried out at OLR of 17.1 g VS L-1
d-1 was quite different compared to the steady-state
period (OLR≤10.2 g VS L-1 d-1)
during which acetic and propionic acid were at an average concentration
of lower than 340 and 50 mg L-1, respectively. In
fact, propionate (2213 mg L-1) tended to dominate the
VFAs (accumulated to 4730 mg L-1) at OLR of 17.1 g VS
L-1 d-1 (Fig. 1c), implying a lower
substrate utilization by acetogens. The fluctuation in the VFA content
was in accordance with the specific methane yield (Fig 1b, c),
indicating a close relationship between VFAs concentration and methane
yield and consequently, an overloading of the system and subsequent
reduction in methane production were attained.
Interestingly, despite the main reason of inhibition associated with
VFAs being the creation of a pH decline, the pH values (Fig. 1b)
registered under acceptable levels for AD process regardless OLR levels.
The pH values for all OLRs were between 7.2 and 7.6 throughout the first
stage of the experiment (OLR 2.6-17.1 g VS L-1
d-1). The volatile solids removal calculated under
different OLRs was 60% ±7% except at 17.1 VS L-1
d-1 of OLR (continuously decreasing) (Fig. 1d).
Specifically, VS removal efficiency with OLR of 17 g VS
L-1 d-1 decreased 2-fold when the
value was compared to data attained with other OLRs. These values are in
good agreement with the values attained for methane yields which were
sharply declined, leading the process to be crashed. The data was in
agreement with previous investigation that OLR is a deterministic
parameter affecting the process performance by shaping the microbial
profile in the digesters (Mahdy et al., 2019a). Consequently, both OLR
of 7.9 and 10.2 g VS L-1 d-1 which
demonstrated highest methane yields with stable performance could be
considered as optimal ORL threshold when treating agro-food wastes and
thus both values are recommended for process optimization.