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- Title
Statistical Modeling and Performance Optimization of a Two-Chamber Microbial Fuel Cell by Response Surface Methodology.
- Authors
Naseer, Muhammad Nihal; Zaidi, Asad A.; Khan, Hamdullah; Kumar, Sagar; Owais, Muhammad Taha bin; Abdul Wahab, Yasmin; Dutta, Kingshuk; Jaafar, Juhana; Hamizi, Nor Aliya; Islam, Mohammad Aminul; Hussin, Hanim; Badruddin, Irfan Anjum; Alrobei, Hussein
- Abstract
Microbial fuel cell, as a promising technology for simultaneous power production and waste treatment, has received a great deal of attention in recent years; however, generation of a relatively low power density is the main limitation towards its commercial application. This study contributes toward the optimization, in terms of maximization, of the power density of a microbial fuel cell by employing response surface methodology, coupled with central composite design. For this optimization study, the interactive effect of three independent parameters, namely (i) acetate concentration in the influent of anodic chamber; (ii) fuel feed flow rate in anodic chamber; and (iii) oxygen concentration in the influent of cathodic chamber, have been analyzed for a two-chamber microbial fuel cell, and the optimum conditions have been identified. The optimum value of power density was observed at an acetate concentration, a fuel feed flow rate, and an oxygen concentration value of 2.60 mol m−3, 0.0 m3, and 1.00 mol m−3, respectively. The results show the achievement of a power density of 3.425 W m−2, which is significant considering the available literature. Additionally, a statistical model has also been developed that correlates the three independent factors to the power density. For this model, R2, adjusted R2, and predicted R2 were 0.839, 0.807, and 0.703, respectively. The fact that there is only a 3.8% error in the actual and adjusted R2 demonstrates that the proposed model is statistically significant.
- Subjects
RESPONSE surfaces (Statistics); MICROBIAL fuel cells; STATISTICAL models; POWER density; WASTE treatment
- Publication
Catalysts (2073-4344), 2021, Vol 11, Issue 10, p1202
- ISSN
2073-4344
- Publication type
Article
- DOI
10.3390/catal11101202