Biowaste energy potential in Kenya

Energy affects all aspects of national development. Hence the current global energy crisis demands greater attention to new initiatives on alternative energy sources that are renewable, economically feasible and sustainable. The agriculture-dependent developing countries in Africa can mitigate the energy crisis through innovative use of the available but underutilised biowaste such as organic residues from maize, barley, cotton, tea and sugarcane. Biogas technology is assumed to have the capacity to economically and sustainably convert these vast amounts of biowaste into renewable energy, thereby replacing the unsustainable fossil energy sources, and reducing dependency on fossil fuels. However, the total energy potential of biogas production from crop residues available in Kenya has never been evaluated and quantified. To this end, we selected five different types of residues (maize, barley, cotton, tea and sugarcane) from Kenya and evaluated their energy potential through biomethane potential analysis at 30 °C and a test time of 30 days. The specific methane yields for maize, barley, cotton, tea and sugarcane residues obtained under batch conditions were respectively 363, 271, 365, 67 and 177 m3 per tonne volatile solids. In terms of energy potential, maize, cotton and barley residues were found to be better substrates for methane production than tea and sugarcane residues and could be considered as potential substrates or supplements for methane production without compromising food security in the country. The evaluated residues have a combined national annual maximum potential of about 1313 million cubic meters of methane which represent about 3916 Gigawatt hour (GWh) of electricity and 5887 GWh of thermal energy. The combined electrical potential is equivalent to 73% of the country’s annual power production of 5307 GWh. Utilization of the residues that are readily available on a ‘free on site’ basis for energy production could substitute the fossil fuels that account for a third of the country’s total electricity generation. Besides, exploitation of the potential presented by the biowaste residues can spur an energy revolution in the country resulting in a major economic impact in the region

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

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