Artificial Neural Network Model for the Prediction of Methane Bi-Reforming Products Using CO2 and Steam

Abstract

The bi-reforming of methane (BRM) is a promising process which converts greenhouse gases to syngas with a flexible H2/CO ratio. As there are many factors that affect this process, the coupled effects of multi-parameters on the BRM product are investigated based on Gibbs free energy minimization. Establishing a reliable model is the foundation of process optimization. When three input parameters are changed simultaneously, the resulting BRM products are used as the dataset to train three artificial neural network (ANN) models, which aim to establish the BRM prediction model. Finally, the trained ANN models are used to predict the BRM products when the conditions vary in and beyond the training range to test their performances. Results show that increasing temperature is beneficial to the conversion of CH4. When the molar flow of H2O is at a low level, the increase in CO2 can enhance the H2 generation. While it is more than 0.200 kmol/h, increasing the CO2 flowrate leads to the increase and then decrease in the H2 molar flow in the reforming products. When the numbers of hidden layer neurons in ANN models are set as (3, 3), (3, 6) and (6, 6), all the correlation coefficients of training, validation and test are higher than 0.995. When these ANN models are used to predict the BRM products, the variation range of the prediction error becomes narrower, and the standard deviation decreases with the increase in neuron number. This demonstrates that the ANN model with more neurons has a higher accuracy. The ANN model with neuron numbers of (6, 6) can be used to predict the BRM products even when the operating conditions are beyond the training ranges, demonstrating that this model has good extension performance. This work lays the foundation for an artificial intelligent model for the BRM process, and established ANN models can be further used to optimize the operating parameters in future work.

Study of Methane Solubility Calculation Based on Modified Henry’s Law and BP Neural Network

“…Taherdangkoo et al [12] demonstrated the application of machine learning algorithms, including boosted regression trees optimized with Bayesian optimization, to accurately predict CH4 solubility in water and seawater across a range of temperatures and pressures, achieving a high coefficient of determination (R 2 = 0.99). Deng and Guo [13] developed an artificial neural network model to predict the products of CH4 bi-reforming using CO2 and steam, demonstrating its accuracy with correlation coefficients over 0.995 across various operational conditions. Li et al [14] developed two neural network models to predict CO2 solubility in aqueous blended amine solvents, using extensive experimental data and a backpropagation learning algorithm.…”

Enhancement of Ni-NiO-CeO2 Interaction on Ni–CeO2/Al2O3-MgO Catalyst by Ammonia Vapor Diffusion Impregnation for CO2 Reforming of CH4

“…Over the catalyst surface, metal sintering is caused by a high operating temperature, while coke is formed and grown via side reactions, methane cracking (Equation (2)), and the Boudouard reaction (Equation (3)). Moreover, the side reaction that consumes H 2 and causes the low H 2 /CO ratio in the syngas product is the reverse water–gas shift (RWGS, Equation (4)) [ 10 , 11 ]. CH 4 + CO 2 → 2H 2 + 2CO △H o 298 K = +247 kJ/mol CH 4 → 2H 2 + C △H o 298 K = +75 kJ/mol 2CO → CO 2 + C △H o 298 K = +173 kJ/mol H 2 + CO 2 → H 2 O + CO △H o 298 K = +41 kJ/mol …”

Combined Steam and CO2 Reforming of Methane over the Hierarchical Ni-ZrO2 Nanosheets/Al2O3 Catalysts at Ultralow Temperature and under Low Steam

“…For the Ni/Al 2 O 3 parent, zirconium oxide (ZrO 2 ) was examined as an effective structural and electronic promoter. In addition, ZrO 2 provides the great mechanical strength and high thermal stability. − Meanwhile, ZrO 2 creates the oxygen mobility from redox properties and presents high Lewis basicity. − Our previous work studied a ZrO 2 -promoted Ni-based catalyst for CSCRM. ZrO 2 enhances H 2 production by promoting the steam adsorption-dissociation via oxygen vacancies .…”

“…Coke majorly formed via the Boudouard reaction (eq ) and CH 4 decomposition (eq ). , 2 CO ( g ) → normalC ( s ) + CO 2 ( g ) , normalΔ H 298 ° = prefix− 173 0.25em kJ / mol CH 4 ( g ) → normalC ( s ) + 2 H 2 ( g ) , normalΔ H 298 ° = prefix+ 75 0.25em kJ / mol …”

“…In addition, ZrO 2 provides the great mechanical strength and high thermal stability. 18 − 20 Meanwhile, ZrO 2 creates the oxygen mobility from redox properties and presents high Lewis basicity. 21 − 23 Our previous work studied a ZrO 2 -promoted Ni-based catalyst for CSCRM.…”