Response Surface Methodology and Artificial Neural Networks Optimisation of CO2 Methanation Simulation using Ni/MgAl2O4 Catalyst in a Multi-Tubular Fixed-Bed Reactor
DOI:
https://doi.org/10.62638/ZasMat1295Apstrakt
This study investigated the simulation and optimization of synthetic methane production over Ni/MgAl2O4 in a multi-tubular fixed-bed reactor. The study comprises process simulation conducted using Aspen Hysys software, modeling and optimization using response surface methodology (RSM) and artificial neural network (ANN) performed using Design Experts and MATLAB software respectively. In the process simulation for the CO2methanation, sensitivity analyses were performed to determine the effects of temperature, pressure, H2/CO2 ratio, and CO fraction in the feedstock on CO2 conversion,CH4 yield, and CH4 selectivity. RSM and ANN models were built using datapoints provided by the process simulation results to model the relationship between input variables and output responses and perform optimisation for RSM model and ANN model coupled with genetic algorithm (GA). The process simulation results profoundly highlighted theimpact of temperature in enhancing CO2 conversion and CH4 yield. Higher temperatures favoured the endothermic reversed water-gas shift (RWGS) reaction, leading to increased CO2 conversion and CH4 yield. Both CO2 conversion, CH4 selectivity, and yield were found to be minimally affected by pressure. CO fraction in the feed was found to exert a delicate influence on the CO2 conversion and CH4 yield. Excessive CO fractions hindered the methanation process, reducing both CO2 conversion and CH4 yield. Additionally, the H2/CO2 ratio proved critical as higher ratios facilitated higher CO2 conversion, CH4 selectivity, and yield, emphasizing the significance of optimal hydrogen to CO2 ratio for efficient methanation which was proposed to be at values higher than the stoichiometric value of 4:1. Furthermore, the ANN-GA model outperformed RSM in terms of prediction accuracy and optimization. The ANN model demonstrated superior capabilities in capturing the complex relationships between the input variables and output responses demonstrated by the performance metrics including R2 values, MSE, RMSE etc. The optimisation results of the ANN-GA model provided more precise and efficient predictions when compared with RSM, offering a deeper understanding of the intricate interactions within the methanation process.
Ključne reči:
Artificial Neural Networks, CO2 Methanation, HYSYS Modelling, Response Surface Methodology, Reverse Water Gas Shift, Langmuir-Hinshelwood-Hougen-Watson Rate ExpressionReference
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