Experimental Research on the Law of Energy Conversion during CO2 Sequestration in Coal

Gao, Tao; Deng, Cunbao; Han, Qing

doi:10.3390/en14238079

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Because the order of magnitude of the variables differed considerably, and the tanh function has the best effect when the features are remarkably different, we used tanh as the excitation function between the input and hidden layers. To maintain the numerical scaling of any previous Energies 2023, 16, 3760 9 of 16 range and facilitate comparison with the sample values, we used purelin as the activation function between the hidden and output layers [31].…”

Section: Model Training and Results Analysismentioning

confidence: 99%

“…For many years, traditional adsorption models, including the Langmuir, D-A, D-R, and BET models, have been used to fit the experimental data of CO 2 adsorption [14][15][16]. Han et al [17] conducted CO 2 isothermal adsorption experiments on coal and rock under different temperature conditions and established an improved evaluation model for coal seam CO 2 storage capacity.…”

Section: Introductionmentioning

confidence: 99%

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Model for Predicting CO2 Adsorption in Coal Left in Goaf Based on Backpropagation Neural Network

Gao

Wang

Wang³

et al. 2023

Energies

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Injecting power plant flue gas into a goaf stores CO2 in the flue gas and effectively prevents the spontaneous combustion of the coal remaining in the goaf. Here, we investigated the adsorption behavior of three types of coal at normal temperature and pressure using a self-developed adsorption experimental device. We used a specific surface area and porosity analyzer to study the effects of pore structure, mineral content, and moisture content on CO2 adsorption in coal. Based on the experimental data, we designed a multifactor CO2 adsorption prediction model based on a backpropagation (BP) neural network. The results indicated that the pore size of most micropores in coal was in the range of 0.5–0.7 and 0.8–0.9 nm. The specific surface area and pore volume were positively correlated with the CO2-saturated adsorption capacity, whereas the mean pore diameter, mineral content, and moisture content were inversely associated with the CO2-saturated adsorption amount. The accuracy of the multifactor BP neural network prediction model was satisfactory: the determination coefficients (R2) of the training and test sets were both above 0.98, the root mean square error (RMSE) and mean absolute error (MAE) of the test set were both less than 0.1, and the prediction results satisfied the requirements. To optimize the prediction performance of the model, we used the random forest algorithm to calculate the importance of each factor. The sum of the importance weights of the specific surface area, moisture content, and pore volume was 91.6%, which was much higher than that of the other two factors. Therefore, we constructed an optimization model with specific surface area, moisture content, and pore volume as input variables. The R2 values of the training and test sets in the simplified model were improved compared with those of the multifactor model, the RMSE and MAE were reduced, and the fitting effect was ideal. The prediction model of CO2 adsorption in coal based on the BP neural network can predict the CO2 adsorption capacity of coal under different physical and chemical conditions, thereby providing theoretical support for the application of CO2 storage technology in goafs.

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Section: Model Training and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%