Electricity Generation From Dewatered Sludge Using Membrane-Less Microbial Fuel Cell
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Date
2018-07-01
Authors
Mohd Zaini Makhtar, Muaz
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
The membrane-less microbial fuel cell (ML-MFC) is an innovative renewable energy
technology that becomes the alternative energy to overcome the global energy crisis.
The ML-MFC operated electrochemically incorporate electrogenic bacteria (EB)
acted as a biocatalyst in order to produce electricity. Dewatered sludge from three
different wastewater treatment plant (A – IWK Kerian, B – IWK Butterworth and C
– IWK Juru) were used as substrate in the ML-MFC. From the preliminary test
sludge A showed a better performance compared to the others in term of nutrient
composition, COD value and power generation. Then performance of the ML-MFC
using sludge A was evaluated using one-factor-at-a-time (OFAT) method followed
by response surface methodology (RSM) via Central Composite Design using a
quadratic model. In the preliminary OFAT study, the highest voltage generation
(852.7 mV) and COD removal (149.2 mg/L) were obtained when the pH 6.0,
electrode distance (ED) 3 cm, moisture content (MC) 30 % (v/w), and temperature
35 °C. After incubation of the ML-MFC using optimum conditions suggested by the
RSM (ED 3 cm, MC 32 % v/w, temperature 38 °C) the voltage (927.7 mV) and COD
removal (170.8 mg/L) were successfully increased about 8.79 % and 14.47 %,
respectively. This showed that optimization using RSM gave better results than the
OFAT method and the maximum power density (41.31 mW/m2) was recorded. The
scanning electron microscope (SEM) observation revealed the EB biofilm formation
at the anode surface. The phylogenetic analysis of EB proved the presence of
Pseudomonas and Bacillus subtilis species in the biofilm which actively boosted the
electron transfer. The study also showed unstructured kinetic growth model, Logistic,
describing well the growth behaviour of EB in the ML-MFC with high R2 value
(0.991) and low RMSE (0.189). While the Leudeking-Piret like model for COD
removal also performed well with R2 values recorded was 0.971 and had low RMSE
value which was 0.203. The experimental data show that the Logistic and
Leudeking-Piret-like model could best describe the growth of EB and COD removal
in the ML-MFC. The parametric uncertainty analysis on COD removal was then
assessed using the Monte Carlo simulation (stochastic variable) to determine
probability distributions due to fluctuation and variation of kinetic model parameters.
Result showed that based on 100,000 samples tested, the substrate removal (S) was
ranged from 179.23 – 191.13 mg/L. Sensitivity analysis was also done to evaluate
the impact of each kinetic parameter on the ML-MFC performance. It was found that
ML-MFC performance highly depends on growth of EB present in the ML-MFC.