Electricity Generation From Dewatered Sludge Using Membrane-Less Microbial Fuel Cell

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