The model and optimization of parameters have been performed using a computer intelligence approach in order to provide greater convenience and reduce costs. In this study, the power density and voltage behavior of 13 direct glucose fuel cells (three types: MEA1-MEA4, MEA5-MEA8, and MEA10-MEA14) are modeled through response surface methodology, multi-layer perceptron (MLP), and machine learning (ML). First, the regression coefficients (R2) of the power density RSM model for MEA1-MEA4, MEA5-MEA8, and MEA10-MEA14 are calculated to be 0.99839, 0.99529, and 0.95316, respectively. After that, the best MLP-ANN structure for predicting power density and the voltage of fuel cells is selected with two hidden layers, which have 5, 5 neurons in each hidden layer for MEA1-MEA4, 3, 5 neurons for MEA5-MEA8, and 5, 5 neurons for MEA10-MEA14. Next, multi-objective optimization for power density and the voltage of the fuel cells is performed using three different algorithms: RSM, non-dominated sorting genetic algorithm version 2 (NSGA-II), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). Finally, the best optimum mode of each type of direct glucose fuel cell is investigated by RSM.
