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Proposing a novel theoretical optimized model for the combined dry and steam reforming of methane in the packed-bed reactors

机译:提出一种新型理论优化模型,用于填充床反应器中甲烷的组合干燥和蒸汽重整

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A packed-bed reactor was modeled for combined dry and steam reforming (CDSR) and simulated using a two-dimensional heterogeneous model at steady-state condition. The model outputs showed a good agreement with experimental data. The effects of important operational parameters such as feed temperature, pressure, molar flow, and CO_2/ CH_4 and H_2O/ CH_4 ratios on methane conversion and H_2/ CO ratio in synthesis gas were also evaluated. Afterward, the modified artificial neural network (ANN) model was used for approximating the results of a simulation with high accuracy. The outputs of ANN model show that the predicted values of ANN model are in good agreement with those of the heterogeneous model, suggesting that the model was successfully developed to capture the correlation between operation conditions, methane conversion, and H_2/ CO ratio in the synthesis gas. Finally, a multi-objective optimization based on the hybrid of ANN and non-dominated sorting genetic algorithm-II (NSGA-II) was carried out to find the best-operating conditions for the methanol production and Fischer- Tropsch synthesis reaction with the desired H_2/ CO molar ratio of about two in synthesis gas. So, the main objectives for CDSR are providing a high methane conversion and also H_2/ CO ratio of two in the output synthesis gas.
机译:为组合干燥和蒸汽重整(CDSR)的建模并在稳态条件下使用二维异质模型进行模拟。模型输出显示与实验数据很好。还评价了重要操作参数如进料温度,压力,摩尔流动和CO_2 / CH_4和H_2O / CH_4和合成气中H_2 / CH_4比的影响。之后,改进的人工神经网络(ANN)模型用于近似以高精度估计模拟的结果。 ANN模型的输出表明,ANN模型的预测值与异构模型的吻合吻合良好,表明该模型被成功开发,以捕获合成中操作条件,甲烷转化和H_2 / CO比之间的相关性气体。最后,进行了基于ANN的杂种和非主导的分选遗传算法-II(NSGA-II)的多目标优化,以找到甲醇生产的最佳操作条件和与所需的Fischer-Tropsch合成反应H_2 / CO摩尔比在合成气中约两个。因此,CDSR的主要目的是在输出合成气中提供高甲烷转化率和两者的H_2 / CO比。

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