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.