Abstract
The increasing demand for renewable energy and sustainable waste management solutions
necessitates urgently exploring optimized biomethane production techniques. This study
investigated the potential of biomethane production from mono and co-digestion of jatropha
cake with poultry dung and food waste at different mixing ratios, hypothesizing that codigestion
will enhance biomethane yield through improved carbon-to-nitrogen (C/N) balance,
synergistic microbial interactions, and optimized substrate properties. The research addresses
energy insecurity, environmental and economic impacts of waste accumulation from agriculture
and households by exploring anaerobic digestion as a pathway for sustainable bioenergy
production. A series of mono- and co-digestion trials with varying substrate ratios were
conducted utilizing the application of Automatic Methane Potential Testing System II (AMPTS
II) at mesophilic temperature (37 °C ± 2). The study substrates morphological analyses using
Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and
X-ray diffraction (XRD) confirmed structural and compositional differences that influenced
biodegradability. The digestion was carried out using 100% poultry dung, 60% poultry dung +
20% jatropha cake + 20% food waste, 40% jatropha cake + 30% poultry dung + 30% food
waste, 60% jatropha cake + 20% poultry dung + 20% food waste, 100% jatropha cake, 60%
food waste + 20% poultry dung + 20% jatropha cake and 100% food waste. Experimental
results showed that food waste is the most effective feedstock for biomethane generation for
this study, it had the highest theoretical biomethane yield (TMY) of 633.42 mLCH4/g VSadded
and experimental biomethane yield (EMY) of 558.95 mLCH4/g VSadded with a biodegradability
(BD) of 88.24% and a C/N ratio of 30.34. Among the co-digestion treatments, the optimum
substrate mix ratio was 60% food waste + 20% poultry dung + 20% jatropha cake, which
yielded 424.50 mL CH₄/g VSadded with a well-balanced C/N ratio of 17.83. The study modeling
through Response Surface Methodology (RSM) and Artificial Neural Networks (ANN)
iii ABSTRACT
identified temperature as the most significant (individual) operational factor impacting
biomethane yield, while the relationship between mixing ratio and digestion time played a
critical role in enhancing production. While both models provided insights, ANN (R = 0.46)
outperformed RSM (R = 0.27) regarding regression and fitting, demonstrating a superior ability
to model non-linear relationships in anaerobic digestion.
This study findings demonstrate the potential of co-digestion for enhancing biomethane yield
while addressing waste disposal challenges. By converting agricultural and household residues
into renewable energy, this study offers substantial financial benefits and supports circular
economy principles, reduces reliance on fossil fuels, and contributes to global energy security,
climate change mitigation, and socioeconomic development.