This
work presents a study on the transferability of simulation
parameters and the effect of local composition in a model metal–organic
framework (MOF). (FP)YEu MOF was selected as a case study of a model
MOF for modeling adsorption of polar gases, such as H2S
and H2O, and nonpolar gases, such as CH4, and
C2H6, as it is a class of MOF that is relevant
for sensor applications but that has not been widely researched. (FP)YEu
has two different rare-earth metals, whose distribution is unknown.
Results showed that taking interaction parameters from the literature
and calculating charges using the Q
eq method,
the adsorption of nonpolar gases is overestimated, but the adsorption
of polar gases is underestimated. Although this could suggest that
some pores in the material studied experimentally are inaccessible,
care must be taken when using molecular simulations as a predictive
tool for different MOF families.
Gas sweetening is a fundamental step in gas treatment processes for environmental and safety concerns. One of the most extensively used and largely recognized solvents for gas sweetening is methyl diethanolamine (MDEA). One of the most crucial metrics for measuring the effectiveness of gas treatment units is the amount of acid gas that has been treated with MDEA solution. As a result, it should be regularly monitored to avoid operational issues in downstream processes and excessive energy consumption. In this study, the artificial neural network (ANN) approach was followed to predict the H 2 S and CO 2 sour gases concentrations of sweetening process. The model was built using dataset gathered from a real operation plant in Iraq, collected from February 2019 to February 2020, and used as input to the neural network. The data include H 2 S and CO 2 concentrations of the feed gas, temperature, pressure, and flow rate of the unit. The designed ANN model showed good accuracy in modeling the process under investigation, even for a wide range of parameter variability. The testing outcomes demonstrated a high coefficient of determination (R 2 ) of greater than 0.99, while the overall training performance showed a low mean squared error (MSE) of less than 0.0003.
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