Prediction of electrocatalyst performance of Pt/C using response surface optimization algorithm‐based machine learning approaches

Abstract

Nowadays, fuel cells have attracted a lot of attention because of their unique efficiency, high -power density and zero gas emission, and many studies have been conducted to improve their efficiency. The difficulties that occur must be fully grasped and minimized to optimize the energy efficiency and the performance of the fuel cells. To increase the performance of Pt/C catalysts and ensure effective synthesis, precise control of the synthesis conditions is necessary. In the present study, the effect of the synthesis process parameters on the catalyst performance used in fuel cells was comprehensively investigated using statistical methods and machine learning algorithms. The polyol synthesis process was implemented to prepare efficient Pt/C electrocatalysts with reducing synthesis cost and time. The synthesis parameters including duration of reaction, pH and reaction temperature were experimentally studied to determine the optimal working conditions. This study also intended to create an adequate mathematical model with response surface methodology and a prediction model with machine learning algorithms to predict the amount of reduced Pt and the ECSA value depending on the synthesis parameters, and to understand the interaction of parameters. Various ML algorithms that are multilayer perceptron artificial neural network (MLP-ANN), support vector regression (SVR) and random forest (RF) model were used and each model's performance was evaluated using several performance indicators (R 2 , mean absolute errors, mean squared error and root mean square errors). The results show that pH is the prominent parameter for both responses. To obtain maximum Pt/C electrocatalyst performance and reduction of Pt, the optimum parameters are determined as pH of 4, reaction temperature of 135 C, and reaction duration of 1 hour. The validation results show a good agreement between predicted and experimental data is obtained with the developed model. Results have obviously shown that this approach can effective in optimizing the electrocatalyst performance with the multiple process parameters. Moreover, it was found that the MLP-ANN model was outperformed to predict electrocatalyst performance of Pt/C more precisely compared to SVR and RF model.

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

“…4.2.6. ML in the Field of CL Machine learning approaches have been used in CL for feature extraction [244], optimization [58,147,245,246], predicting performance [247], and degradation of CL on PEMFC [156,[248][249][250][251][252]. Wang et al [244] implemented deep learning super-resolution and multi-label segmentation to process the images from X-ray micro-computed tomography, followed by LBM with multi-relaxation time (MRT) for water management modeling.…”

“…Four critical features were identified, and peak power density and limiting current density were increased to 9.96% and 10.47%, respectively, by optimizing the catalyst ratio and agglomeration. Elçiçek et al [247] utilize a multilayer perceptron ANN model (Figure 7d), where they used reaction temperature, pH, and reaction duration as inputs to predict electrochemical active surface area (EASA) and reduction of Pt. The MLP-ANN model exhibited superior performance, standing out as the best among various machine learning algorithms when considering accuracy, overall performance, and generalization capabilities in comparison to SVR and RF.…”

Application of Machine Learning in Fuel Cell Research

“…Elcicek and Özdemir [82] analyzed how synthesis process parameters affect fuel cell catalyst performance using statistical methods and RF. Many factors need to be considered to optimize catalyst performance, such as catalyst composition, morphology, and surface properties, as well as process parameters such as reaction conditions, reactant concentration, and reactant types.…”