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Maximizing power production in a stack of microbial fuel cells using multiunit optimization method
Author(s) -
Woodward Lyne,
Tartakovsky Boris,
Perrier Michel,
Srinivasan Bala
Publication year - 2009
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.115
Subject(s) - microbial fuel cell , stack (abstract data type) , anode , maximization , convergence (economics) , power (physics) , cathode , computer science , maximum power principle , control theory (sociology) , electricity generation , production rate , stability (learning theory) , volumetric flow rate , maximum power point tracking , mathematical optimization , rate of convergence , process (computing) , process engineering , mathematics , electrode , chemistry , engineering , electrical engineering , physics , mechanics , control (management) , channel (broadcasting) , inverter , artificial intelligence , economic growth , computer network , operating system , quantum mechanics , machine learning , programming language , economics
This study demonstrates real‐time maximization of power production in a stack of two continuous flow microbial fuel cells (MFCs). To maximize power output, external resistances of two air–cathode membraneless MFCs were controlled by a multiunit optimization algorithm. Multiunit optimization is a recently proposed method that uses multiple similar units to optimize process performance. The experiment demonstrated fast convergence toward optimal external resistance and algorithm stability during external perturbations (e.g., temperature variations). Rate of the algorithm convergence was much faster than in traditional maximum power point tracking algorithms (MPPT), which are based on temporal perturbations. A power output of 81–84 mW/L A (A = anode volume) was achieved in each MFC. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009