Artificial Neural Networks based thermodynamic and economic analysis of a hydrogen production system assisted by geothermal energy on Field Programmable Gate Array

Yılmaz, Ceyhun; Koyuncu, İ̇smail; Alçın, Murat; Tuna, Murat

doi:10.1016/j.ijhydene.2019.05.049

Cited by 67 publications

(13 citation statements)

References 42 publications

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“…Electrolysis is the most common method to produce hydrogen from water when integrated with other energy sources. Hydrogen production cost over electrolysis using geothermal energy is about $1.09/kg 7 . Hydro and wind energy in a flexible transmission grid system for hydrogen production are used for electrolysis when electricity surplus is available 9,106 .…”

Section: Discussionmentioning

confidence: 99%

“…There were previous studies of several types of hybrid system for hydrogen production from low carbon intensive energy sources. Hydrogen production by electrolysis was studied using solar, 4,5 geothermal, 6,7 wind, 8 hydro power, 9 and combination of solar and wind 10 . Electrolysis and thermochemical cycles using nuclear fission energy was simulated, employing two potential nuclear power plants in Turkey 11 .…”

Section: Introductionmentioning

confidence: 99%

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Techno‐economic analysis of hydrogen production from the nuclear fusion‐biomass hybrid system

Nam

Konishi

2020

Int J Energy Res

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Summary The hydrogen production over the combination of nuclear fusion and biomass conversion technologies achieves the carbon‐negative effect by employing carbon capture and storage technology. Techno‐economic analysis on the fusion‐biomass hybrid system is performed to understand the hydrogen cost and the cost competitiveness as compared to other existing technologies. Cellulose, hemicellulose, and lignin for biomass are chosen for the techno‐economic analysis in addition to the municipal solid waste as another feedstock. Stoichiometric calculation for biomass conversion to hydrogen is employed. Nuclear fusion thermal heat capacity and its heat cost are selected based on literature survey. Gasification plant for hydrogen production is assumed to employ the bubbling fluidized bed gasifier, fed by 20.6 MJ/kg of biomass. The result shows that total syngas capacity is 203 PJ and 137 PJ of hydrogen is produced through a ceramic membrane for H2 separation. The levelized cost of hydrogen is $2.45/kg and the estimated 90% confidence interval of the LCOH ranges from $1.87 to $3.24/kg. The LCOH locates at a competitive position among the existing technologies. The LCOH can further decreased with the development of carbon pricing by the time of fusion commercialization in 2050.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Techno‐economic analysis of hydrogen production from the nuclear fusion‐biomass hybrid system

Nam

Konishi

2020

Int J Energy Res

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“… 130 Geothermal energy is another source of sustainable energy, and a recent study demonstrated that geothermal powered electrolysis is a viable method for hydrogen production (1.1 $/kg H 2 ) with a payback period of only 4–5 years. 131 …”

Section: Technologies For Renewable Hydrogen Productionmentioning

confidence: 99%

“…2.4 $/kg H 2 , and if the upfront investments are excluded (by using existing hydropower plants), the hydrogen cost goes down to 1.2 $/kg H 2 130. Geothermal energy is another source of sustainable energy, and a recent study demonstrated that geothermal powered electrolysis is a viable method for hydrogen production (1.1 $/kg H 2 ) with a payback period of only 4-5 years 131. …”

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confidence: 99%

Renewable hydrogen for the chemical industry

Rambhujun

Salman

Wang

et al. 2020

MRS Energy & Sustainability

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“…ANNs based on hardware have been a big part of many areas in recent years [9]. For example, random number generation [10], prediction [8], design oscillation, synchronization [11], image processing, medical technology, chemical industry [12], and secure communication [13]. Software-based ANNs are simple to implement, but large neural networks are run very slowly and cannot be adapted to many real-time applications [14].…”

Section: Introductionmentioning

confidence: 99%

New artificial neural network design for Chua chaotic system prediction using FPGA hardware co-simulation

Al-Musawi

Wali

Al-Ibadi

2022

IJECE

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<p>This study aims to design a new architecture of the artificial neural networks (ANNs) using the Xilinx system generator (XSG) and its hardware co-simulation equivalent model using field programmable gate array (FPGA) to predict the behavior of Chua’s chaotic system and use it in hiding information. The work proposed consists of two main sections. In the first section, MATLAB R2016a was used to build a 3×4×3 feed forward neural network (FFNN). The training results demonstrate that FFNN training in the Bayesian regulation algorithm is sufficiently accurate to directly implement. The second section demonstrates the hardware implementation of the network with the XSG on the Xilinx artix7 xc7a100t-1csg324 chip. Finally, the message was first encrypted using a dynamic Chua system and then decrypted using ANN’s chaotic dynamics. ANN models were developed to implement hardware in the FPGA system using the IEEE 754 Single precision floating-point format. The ANN design method illustrated can be extended to other chaotic systems in general.</p>

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Artificial Neural Networks based thermodynamic and economic analysis of a hydrogen production system assisted by geothermal energy on Field Programmable Gate Array

Cited by 67 publications

References 42 publications

Techno‐economic analysis of hydrogen production from the nuclear fusion‐biomass hybrid system

Techno‐economic analysis of hydrogen production from the nuclear fusion‐biomass hybrid system

Renewable hydrogen for the chemical industry

New artificial neural network design for Chua chaotic system prediction using FPGA hardware co-simulation

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