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Modeling and optimization strategies towards performance enhancement of microbial fuel cells

pmid: 33120058
Considering the complexity associated with bioelectrochemical processes, the performance of a microbial fuel cell (MFC) is governed by input operating parameters. For scaled-up applications, a MFC system needs to be modeled from engineering perspectives in terms of optimum operating conditions to get higher performance and energy recovery. Several conceptual numerical models to advanced computational simulation approaches have been developed to represent simple-form of a complex MFC system. Application of mathematical and computation models are explored to establish the relationship between operating input-variables and power output. The present review discusses about the complexity of system, modeling strategies used and reality of such modeling for scaling-up applications of MFCs. Additionally, the selection of an appropriate mathematical model reduces the computational duration and provides better understanding of the system process. It also explores the possibility and progress towards commercialization of MFCs and thus the need of development of model-based optimization and process-control approaches.
Electricity, Bioelectric Energy Sources, Models, Theoretical, Electrodes
Electricity, Bioelectric Energy Sources, Models, Theoretical, Electrodes
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).117 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.1%
