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BIOCHEMICAL METHANE POTENTIAL TESTING AND MODELLING FOR INSIGHT INTO ANAEROBIC DIGESTER PERFORMANCESarah Daly (9183209) 30 July 2020 (has links)
<p>Anaerobic digestion uses a mixed, microbial
community to convert organic wastes to biogas, thereby generating a clean
renewable energy and reducing greenhouse gas emissions. However, few studies have quantified the
relationship between waste composition and the subsequent physical and chemical
changes in the digester. This Ph.D.
dissertation aimed to gain new knowledge about how these differences in waste
composition ultimately affect digester function. This dissertation examined three areas of
digester function: (1) hydrogen sulfide production, (2) digester foaming, and
(3) methane yield. </p>
<p>To accomplish these aims, a variety of materials
from four different large-scale field digesters were collected at different
time points and from different locations within the digester systems, including
influent, liquid in the middle of the digesters, effluent, and effluent after
solids separation. The materials were used
for biochemical methane potential (BMP) tests in 43 lab-scale lab-digester
groups, each containing triplicate or duplicate digesters. The materials from field digesters and the effluents
from the lab-digesters were analyzed for an extensive set of chemical and
physical characteristics. The three areas of digester function were examined with the physical and
chemical characteristics of the
digester materials and effluents, and the BMP performances. </p>
<p>Hydrogen sulfide productions in
the lab-digesters ranged from non-detectable to 1.29 mL g VS<sup>-1</sup>. Higher H<sub>2</sub>S concentrations in the
biogas were observed within the first ten days of testing. The initial Fe(II) :
S ratio and OP concentrations had important influences on H<sub>2</sub>S
productions. Important parameters of digester influents related
to digester foaming were the ratios of Fe(II) : S, Fe(II) : TP, and TVFA :
TALK; and the concentrations of Cu. Digesters receiving mixed waste streams could
be more vulnerable to foaming. The
characteristics of each waste type varied significantly based on substrate and
inoculum type, and digester functioning.
The influent chemical characteristics of the waste significantly impacted
all aspects of digester function. Using multivariate statistics and machine
learning, models were developed and the prediction of digester outcomes were simulated
based on the initial characteristics of the waste types. </p>
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