Design and implementation of hydrogen economy using artificial neural network on field programmable gate array

In this study, economic analysis of the hydrogen generation and liquefaction system has been modeled using Multi-Layer Feed-Forward Artificial Neural Network (MLFFANN) and implemented on Field Programmable Gate Array (FPGA). Firstly, the 100X6 data set has been created to be used in the ANN-based modeling of the system using the Engineering Equation Solver (EES) program. This data set has been divided into two data sets as 80X6 for training and 20X6 for testing. The structure of the ANN-based economic analysis of hydrogen generation and liquefaction has been composed of 3 neurons in the input layer, ten neurons in the hidden layer, and three neurons in the output layer. Elliott-2-based TanSig transfer function and Purelin transfer function have been used in the neurons of the hidden layer and the output layer, respectively. Then, the ANN-model has been trained and tested using the Matlab program. The MSE values, 1.40x10E-7 and 2.07x10E-5, have been obtained as the results of the training phase and test phase of the ANN-based system, respectively. After getting fruitful results from training and testing phases, the economic analyses of hydrogen generation and liquation systems have been modeled in VHDL using bias and weight values located in the constructed ANN-based system using Matlab. The modeling has been performed in the Xilinx ISE Design Tools program using a 32-bit IEEE754-1985 floating-point number standard. Then, the modeled ANN-based economic analysis of the hydrogen generation and liquation system has been implemented on the Xilinx Virtex-7 FPGA chip by performing the Place&Route process. The maximum operating frequency of the ANN-based hydrogen generation and liquefaction economy system implemented on FPGA has been obtained as 281.702 MHz using Xilinx ISE Design Tools. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.