Modeling and Optimization of Anode-Supported Solid Oxide Fuel Cells on Cell Parameters via Artificial Neural Network and Genetic Algorithm

Fuel Cells ◽  
2012 ◽  
Vol 12 (1) ◽  
pp. 11-23 ◽  
Author(s):  
S. Bozorgmehri ◽  
M. Hamedi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Fuel Cells ◽  
Solid Oxide Fuel Cells ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Modeling And Optimization ◽  
Cell Parameters ◽  
Artificial Neural

scholarly journals Modeling and Optimization of the BSCF-Based Single-Chamber Solid Oxide Fuel Cell by Artificial Neural Network and Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Minh-Vien Le ◽  
Tuan-Anh Nguyen ◽  
T.-Anh-Nga Nguyen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuel Cells ◽  
Energy Demand ◽  
Cell Structure ◽  
High Energy ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Preparation Temperature ◽  
Artificial Neural

Fuel cells could be a highly effective and eco-friendly technology to transform chemical energy stored in fuel to useful electricity and thus are presently appraised as a standout among the most encouraging advancements for future energy demand. Solid oxide fuel cells (SOFCs) have several advantages over other types of fuel cells, such as the flexibility of fuel used, high energy conversion, and relatively inexpensive catalysts due to high-temperature operation. The single chambers, wherein the anode and cathode are exposed to the same mixture of fuel, are promising for the portable power application due to the simplified, compact, sealing-free cell structure. The empirical regression models, such as artificial neural networks (ANNs), can be used as a black-box tool to simulate systems without solving the complicated physical equations merely by utilizing available experimental data. In this study, the performance of the newly proposed BSCF/GDC-based cathode SOFC was modeled using ANNs. The cell voltage was estimated with cathode preparation temperature, cell operating temperature, and cell current as input parameters by the one-layer feed-forward neural network. In order to acquire the appropriate model, several network structures were tested, and the network was trained by backpropagation algorithms. The data used during the training, validation, and test are the actual experimental results from our previous study. The optimum conditions to achieve maximum power of the cell were then determined by the genetic algorithm and the developed ANN.

scholarly journals PLASTIC WASTE CONVERSION TO LIQUID FUELS OVER MODIFIED-RESIDUAL CATALYTIC CRACKING CATALYSTS: MODELING AND OPTIMIZATION USING HYBRID ARTIFICIAL NEURAL NETWORK – GENETIC ALGORITHM

REAKTOR ◽  
2011 ◽  
Vol 13 (3) ◽  
pp. 131 ◽  
Author(s):  
Istadi Istadi ◽  
Luqman Buchori ◽  
Suherman Suherman
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Liquid Fuel ◽  
Weight Ratio ◽  
Liquid Fuels ◽  
Modeling And Optimization ◽  
Reactor Temperature ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

The plastic waste utilization can be addressed toward different valuable products. A promising technology for the utilization is by converting it to fuels. Simultaneous modeling and optimization representing effect of reactor temperature, catalyst calcinations temperature, and plastic/catalyst weight ratio toward performance of liquid fuel production was studied over modified catalyst waste. The optimization was performed to find optimal operating conditions (reactor temperature, catalyst calcination temperature, and plastic/catalyst weight ratio) that maximize the liquid fuel product. A Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) method was used for the modeling and optimization, respectively. The variable interaction between the reactor temperature, catalyst calcination temperature, as well as plastic/catalyst ratio is presented in surface plots. From the GC-MS characterization, the liquid fuels product was mainly composed of C4 to C13 hydrocarbons.KONVERSI LIMBAH PLASTIK MENJADI BAHAN BAKAR CAIR DENGAN METODE PERENGKAHAN KATALITIK MENGGUNAKAN KATALIS BEKAS YANG TERMODIFIKASI: PEMODELAN DAN OPTIMASI MENGGUNAKAN GABUNGAN METODE ARTIFICIAL NEURAL NETWORK DAN GENETIC ALGORITHM. Pemanfaatan limbah plastik dapat dilakukan untuk menghasilkan produk yang lebih bernilai tinggi. Salah satu teknologi yang menjanjikan adalah dengan mengkonversikannya menjadi bahan bakar. Permodelan, simulasi dan optimisasi simultan yang menggambarkan efek dari suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis terhadap kinerja produksi bahan bakar cair telah dipelajari menggunakan katalis bekas termodifikasi Optimisasi ini ditujukan untuk mencari kondisi operasi optimum (suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis) yang memaksimalkan produk bahan bakar cair. Metode Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) telah digunakan untuk permodelan dan optimisasi simultan tersebut. Inetraksi antar variabel suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis digambarkan dalam bentuk plot surface. Berdasarkan karakterisasi GC-MS, produk bahan bakar yang diperoleh terdiri dari komponen-komponen hidrokarbon C4-C13.

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