Biogas production is an anaerobic waste-to-energy technology, involving waste degradation and stabilization. The sustainable, cheap and clean nature of biogas has led to the unprecedented rise in its use as an alternative energy source. Due to the increased interests, availability of conventional biodegradable organics has shrunk enormously over the years, necessitating the aggressive search for novel energy crops and substrate enhancement options. These novel options ensure feedstock security, optimize conventional biomass feedstocks, improve feedstock degradability and increase in biogas yield. Low biodegradability of most lignocellulosic wastes like okra waste, limits their use as a viable substrate in the anaerobic digestion process. Over the years, several elements, compounds and nanoparticles have been applied to anaerobic digestion systems as supplementary nutrients with a view to enhancing substrate degradation. Such supplements like iron-based additives have gained prominence in anaerobic digestion processes of wastes, owing to their electron donation abilities, promotion of solubilization, hydrolysis, acidification, and hydrogenotrophic methanogenesis. In a bid to enhance substrate degradation, reduce inhibitions, increase both biogas yield and methane content, a comparative study on the influence of four different iron-based additives (nanoscale zero-valent iron (nZVI), Polypyrrole-magnetic nanocomposite (Ppy-Fe3O4), Iron powder (Fe) and Hematite (Fe2O3)) on the entire anaerobic digestion of okra waste was done. Previously determined optimum doses, 20 mg, 20 mg, 750 mg, 750 mg and 0 respectively for nZVI, Ppy-Fe3O4, Fe, Fe2O3 and control were added to the bioreactors containing okra wastes in a 500 mL biomethane potential bioreactors under mesophilic temperature (37°C) for 20 days.

The cumulative volumes of the biogas from different reactors were recorded and analyzed. The morphological deformation, structures and analysis of the undigested substrate, digestates of substrate supplemented with iron-based additives and the control were evaluated with scanning electron microscopy (SEM). Artificial neural network (ANN) model and the modified Gompertz model were validated with the experimental data. The ANN model showed better goodness of fit and was better correlated with the experimental data. Experimental data were subjected to analysis of variance at a 95% confidence level. Results showed that Ppy-Fe3O4 additives better enhanced both biogas yield and methane contents significantly when compared to the control. It was also observed that all iron-additive supplemented processes were more degraded when compared with the control.

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