The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, especially in polymer electrolyte fuel cell (PEFC), to guarantee optimal operation. Several researchers have employed optimization algorithms (OAs) to identify operating parameters to improve the PEFC performance. In the current study, several nature-based OAs have been performed to compute the optimal parameters used to describe the polarization curves in a PEFC. Different relative humidity (RH) values, one of the most influential variables on PEFC performance, have been considered. To develop this study, experimental data have been collected from a lab-scale fuel cell test system establishing different RH percentages, from 18 to 100%. OAs like neural network algorithm (NNA), improved grey-wolf optimizer (I-GWO), ant lion optimizer (ALO), bird swarm algorithm (BSA), and multi-verse optimization (MVO) were evaluated and compared using statistical parameters as training error and time. Results gave enough information to conclude that NNA had better performance and showed better results over other highlighted OAs. Finally, it was found that sparsity and noise are more present at lower relative humidity values. At low RH, a PEFC operates under critical conditions, affecting the fitting on OAs.
The paper proposed to use a recurrent neural network model, and a real-time Levenberg-Marquardt algorithm of its learning for decentralized fuzzy-neural data-based modeling, identification and control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess, used as process data generator, represented a distributed parameter system, which is reduced to a lumped system using the orthogonal collocation method, applied in four collocation points plus one-in the recirculation tank. The paper proposed to use direct adaptive integral plus states fuzzy-neural control, and indirect adaptive I-term sliding mode fuzzy-neural control. The comparative graphical simulation results of the digestion wastewater treatment system control, exhibited a good convergence and precise reference tracking, giving slight priority to the direct control with respect to the indirect control applied.Keywords-decentralized direct adaptive fuzzy-neural control with I-term; decentralized indirect fuzzy-neural sliding mode control; hierarchical fuzzy-neural multi model identifier; anaerobic digestion wastewater bioprocess plant; distributed parameter system; Levenberg-Marquardt learning; Takagi-Sugeno fuzzy rules with recurrent neural network antecedent parts.
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