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Optimisation of biogas production from Anaerobic Digestion through the identification of ammonia tolerances, optimal C:N ratio and optimal labile and recalcitrant fraction for carbon supplementation

Bühlmann, Christopher (2017) Optimisation of biogas production from Anaerobic Digestion through the identification of ammonia tolerances, optimal C:N ratio and optimal labile and recalcitrant fraction for carbon supplementation. Honours thesis, Murdoch University.

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Abstract

The annual disposal of large quantities of Food Waste (FW) has led to increased greenhouse gas (GHG) emissions and depletion of soil nutrients. Anaerobic Digestion (AD) is a promising, cost-effective technology capable of producing energy and recovering nutrients from this ever-increasing resource. However, some AD plants are running sub-optimally as many process variables need to be optimised to maximise the biogas produced from the process.

This paper aims to optimise Richgro’s AD facility, located in Jandakot Western Australia, via the determination of; the inoculum’s ammonia inhibition threshold, optimal feed C:N ratio, and optimal labile carbon (LC) and recalcitrant carbon (RC) ratio within the supplemented carbon mixture, to maximise biogas production. Three bench-scale experiments were conducted to assess the ammonia toxicity on the anaerobic inocula, optimise the feed C:N ratio and determine the optimal labile and recalcitrant carbon contents of the supplemented carbon source.

The results of this study indicate the inoculum has a high tolerance to ammonia due to the previous acclimation of the methanogens and can withstand free ammonia concentrations of 480 mg NH3-N/L with no inhibition. Increasing the substrate C:N ratio to 29.00 lead to a 32.84 % increase in the biogas produced when the substrate was digested at mesophilic conditions. Additionally, supplementing the feed with a mixture containing 50 % LC and 50 % RC will increase the conversion of volatile solids (VS) into biogas by 11.70%.

Publication Type: Thesis (Honours)
Murdoch Affiliation: School of Engineering and Information Technology
Supervisor: Bahri, Parisa and Mickan, Bede
URI: http://researchrepository.murdoch.edu.au/id/eprint/40472
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