Thermodynamic modelling of dimethyl ether steam reforming

Ighalo, Joshua O.; Adeniyi, Adewale George

doi:10.1007/s10098-021-02033-y

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…By increasing the S/C, the equilibrium is shifted towards the production of CO 2 and hydrogen through the WGSR. As a result, the quantity of CO is reduced 22 . Figure 7 f shows that increasing both O/C and S/C reduces CO due to more DME oxidization and shifts the WGSR equilibrium to CO 2 and hydrogen production.…”

Section: Resultsmentioning

confidence: 99%

“…Employing Response Surface Methodology (RSM), they investigated potential interactions among various process factors. They developed regression models to forecast the percentage molar yield of each species, considering key factor ranges such as temperature (450–750 °C), pressure (1–5 atm), and steam-to-carbon (S/C) ratio (1–5) 22 .…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network

Mansouri,

Bahmanzadegan,

Ghaemi

2024

Sci Rep

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This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg–Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e−05 for the hydrogen mole fraction and 4.88e−05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%