Biotechnology for Biofuels and Bioproducts (Jul 2023)

Optimization of biogas production from anaerobic co-digestion of fish waste and water hyacinth

  • Hortence Ingabire,
  • Milton M. M’arimi,
  • Kirimi H. Kiriamiti,
  • Boniface Ntambara

DOI
https://doi.org/10.1186/s13068-023-02360-w
Journal volume & issue
Vol. 16, no. 1
pp. 1 – 8

Abstract

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Abstract Many fresh water bodies face a great challenge of an invasive weed called water hyacinth (WH) which has great impacts on the environment, ecology, and society. Food and Agriculture Organization (FAO) estimates that over nine million tons of Fish wastes (FW) are thrown away each year. The fish waste generated poses environmental and health hazards because in most cases it is either disposed into pits or discarded onto the open grounds. Both WH and FW are potential substrates for biogas production. However, utilization of FW substrate alone has a limitation of producing a lot of amounts of volatile fatty acids (VFAs) and ammonia. Their accumulation in the digester inhibits substrate digestion. Consequently, as stand-alone it is not suitable for anaerobic digestion (AD). This can be overcome by co-digestion with a substrate like WH which has high carbon to nitrogen (C/N) ratio prior to biodigestion. Experimental variable levels for biogas were substrate ratio (WH:FW, 25–75 g), inoculum concentration (IC, 5–15 g/250 mL), and dilution (85–95 mL). Design-Expert 13 was used for optimization and results analysis. Response surface methodology (RSM) was used to examine the effects of operating parameters and identify optimum values for biogas yield. Optimum values for maximum biogas with the highest methane yield of 68% were found to be WH:FW ratio, 25:75 g, 15 g of IC, and 95 mL for dilution. The yield was 16% and 32% greater than FW and WH mono-digestion, respectively. The biogas yield was expressed as a function of operating variables using a quadratic equation. The model was significant (P < 0.05). All factors had significant linear and quadratic effects on biogas while only the interaction effects of the two factors were significant. The coefficient of determination (R 2) of 99.9% confirmed the good fit of the model with experimental variables.

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