Integrating fuzzy modelling and war strategy optimization for identifying optimal operating factors of direct ethanol fuel cell

-Operating factors, including temperature, pressure, alcohol concentration, and reactant flow rate, significantly impact how much energy is produced by direct alcohol fuel cells. The cell's performance may go down or up depending on how these factors vary. For instance, greater alcohol temperatures and concentrations can speed up reactions and boost fuel cell efficiency, whereas lower alcohol temperatures and concentrations can have the opposite effect. Consequently, optimizing the operating circumstances is essential to getting the most energy possible out of direct alcohol fuel cells. Therefore, the primary goal is to develop a solid fuzzy model to simulate direct ethanol fuel cells (DMFC). Three process variables—ethanol flow rate, ethanol concentration, and temperature—are considered to increase the power density of DEFC. First, a fuzzy model of the DEFC was developed using experimental data. The best operating conditions to increase power density are then determined using war strategy optimization (WSO). Thanks to fuzzy, the RMSE decreased from 0.529 using RSM to 0.0292 using fuzzy (decreased by 94.5 %), compared to 0.529 using RSM. By around 11.7 %, the squared-R for prediction increases from 0.88 (using RSM) to 0.9831 (using fuzzy). The fuzzy model's low RMSE and high R-square values show the successful modelling phase. Compared to measured data and RSM, integrating fuzzy and WSO increased DEFC's power density by 5 % and 7.26 %, respectively. The ideal ethanol flow rates, concentrations, and temperatures are 9.4 ml/min, 0.25 M, and 74C, respectively. • Fuzzy model developed for simulating direct ethanol fuel cells (DEFC) reduced the RMSE by 94.5 % (from 0.529 to 0.0292) and increased the R-square from 0.88 to 0.9831, demonstrating significantly improved modeling accuracy. • Integrating fuzzy modeling with war strategy optimization (WSO) increased the DEFC power density by 5 % compared to measured data and by 7.26 % compared to the response surface methodology (RSM). • Optimal operating conditions identified are an ethanol flow rate of 9.4 ml/min, an ethanol concentration of 0.25 M, and a temperature of 74 °C.

Authors

Institutions

Publication Details

Journal
Results in Engineering
Published
2024-09-23
DOI
https://doi.org/10.1016/j.rineng.2024.102983
Citations
8
Primary Topic
Fuel Cells and Related Materials
Type
article
Field-Weighted Citation Impact
1.67

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating fuzzy modelling and war strategy optimization for identifying optimal operating factors of direct ethanol fuel cell

Hamza Faraji, Hegazy Rezk
8 citations
Results in Engineering
Fuel Cells and Related Materials
1.67
article

Integrating fuzzy modelling and war strategy optimization for identifying optimal operating factors of direct ethanol fuel cell

Hamza Faraji, Hegazy Rezk
article en
8 citations

Abstract

-Operating factors, including temperature, pressure, alcohol concentration, and reactant flow rate, significantly impact how much energy is produced by direct alcohol fuel cells. The cell's performance may go down or up depending on how these factors vary. For instance, greater alcohol temperatures and concentrations can speed up reactions and boost fuel cell efficiency, whereas lower alcohol temperatures and concentrations can have the opposite effect. Consequently, optimizing the operating circumstances is essential to getting the most energy possible out of direct alcohol fuel cells. Therefore, the primary goal is to develop a solid fuzzy model to simulate direct ethanol fuel cells (DMFC). Three process variables—ethanol flow rate, ethanol concentration, and temperature—are considered to increase the power density of DEFC. First, a fuzzy model of the DEFC was developed using experimental data. The best operating conditions to increase power density are then determined using war strategy optimization (WSO). Thanks to fuzzy, the RMSE decreased from 0.529 using RSM to 0.0292 using fuzzy (decreased by 94.5 %), compared to 0.529 using RSM. By around 11.7 %, the squared-R for prediction increases from 0.88 (using RSM) to 0.9831 (using fuzzy). The fuzzy model's low RMSE and high R-square values show the successful modelling phase. Compared to measured data and RSM, integrating fuzzy and WSO increased DEFC's power density by 5 % and 7.26 %, respectively. The ideal ethanol flow rates, concentrations, and temperatures are 9.4 ml/min, 0.25 M, and 74C, respectively. • Fuzzy model developed for simulating direct ethanol fuel cells (DEFC) reduced the RMSE by 94.5 % (from 0.529 to 0.0292) and increased the R-square from 0.88 to 0.9831, demonstrating significantly improved modeling accuracy. • Integrating fuzzy modeling with war strategy optimization (WSO) increased the DEFC power density by 5 % compared to measured data and by 7.26 % compared to the response surface methodology (RSM). • Optimal operating conditions identified are an ethanol flow rate of 9.4 ml/min, an ethanol concentration of 0.25 M, and a temperature of 74 °C.

Results in EngineeringVol. 24
Cadi Ayyad University (MA), Prince Sattam Bin Abdulaziz University (SA)
Prince Sattam bin Abdulaziz University, Deanship of Scientific Research, Prince Sattam bin Abdulaziz University
Affordable and clean energy
Openalex Percentile: Top 16%
Fuel Cells and Related Materials
1.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.