Sensitivity analysis and artificial neural network-based optimization for low-carbon H2 production via a sorption-enhanced steam methane reforming (SESMR) process integrated with separation process

Vo, Nguyen D.; Kang, Jun-Ho; Oh, Donghoon; Jung, Min Young; Chung, Kyounghee; Lee, Chang Ha

doi:10.1016/j.ijhydene.2021.10.053

Cited by 27 publications

(2 citation statements)

References 62 publications

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“…The applied algorithm enables a significant reduction in the carbon deposition rate while maintaining a high power density and a safe temperature gradient (below 10 K cm −1 ). An artificial network was also used to optimize integrated sorption-enhanced steam methane reforming (SESMR) [21]. After conducting a sensitivity analysis of the system, it was concluded that the pressure swing adsorption variables distinctly affected product quality, while the cyclic fluidized bed variables mainly contributed to other performance parameters.…”

Section: Introductionmentioning

confidence: 99%

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Pizoń,

Kimijima,

Brus

2024

Energies

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Methane steam reforming is the foremost method for hydrogen production, and it has been studied through experiments and diverse computational models to enhance its energy efficiency. This study focuses on employing an artificial neural network as a model of the methane steam reforming process. The proposed data-driven model predicts the output mixture’s composition based on reactor operating conditions, such as the temperature, steam-to-methane ratio, nitrogen-to-methane ratio, methane flow, and nickel catalyst mass. The network, a feedforward type, underwent training with a comprehensive dataset augmentation strategy that augments the primary experimental dataset through interpolation and theoretical simulations of the process, ensuring a robust model training phase. Additionally, it introduces weights to evaluate the relative significance of different data categories (experimental, interpolated, and theoretical) within the dataset. The optimal artificial neural network architecture was determined by evaluating various configurations, with the aim of minimizing the mean squared error (0.00022) and maximizing the Pearson correlation coefficient (0.97) and Spearman correlation coefficient (1.00).

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Section: Introductionmentioning

confidence: 99%

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Pizoń,

Kimijima,

Brus

2024

Energies

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“…On the other hand, ref. [25] was found; the authors used different ANNs to optimize the PSA process to produce and purify hydrogen. The different ANNs were implemented on the cost function to obtain a better performance and be able to predict the highest production of hydrogen.…”

Section: Introductionmentioning

confidence: 99%

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Ramos-Martinez,

Torres-Cantero,

Ortiz-Torres

et al. 2023

Mathematics

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This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99% molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94% of fit in the control signal and a 97% fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process.

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Dehydration of Methanol to Dimethyl Ether (DME): Plant, Process, Operation, and Equipment

Zhang,

Qi,

et al. 2024

Reference Module in Chemistry, Molecular Sciences and Chemical Engineering

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Sensitivity analysis and artificial neural network-based optimization for low-carbon H2 production via a sorption-enhanced steam methane reforming (SESMR) process integrated with separation process

Cited by 27 publications

References 62 publications

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Dehydration of Methanol to Dimethyl Ether (DME): Plant, Process, Operation, and Equipment

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