An Intelligent Deep Learning Model for Adsorption Prediction

Table 1

Summary of gas adsorption simulation deep learning prediction models for different gases using density, molecular, and vacancy volume.

Reference	Gas type	Model	Prediction results
[10]	Xe	Density parameter function	The adsorption ground truth is closer to the experimental results
[28]	CO2	Dunning’s correlation sets	Moderate accuracy
[29]	CO2	Molecular simulations	Moderate accuracy
[22]	CO2	Scalable boosting tree model (SBT)	Satisfactory
[17]	CO2	The data were linearly correlated by Toth and sips equations	The sip model showed the least deviation
[30]	CO2, He, and Ar	Gradient variant decision tree model (GVD)	Accurate for the adsorbed phase
[31]	CO2	The vendor and Langmuir metric	Vendor depicts less deviation than the Langmuir metric from the ground truth
[32]	Kr and N2	Vacancy solution method	Yeilds parameter optimization
Our proposed work: Fusion matrix deep learning model (FMDL)		Adsorption modeling using the D-CNN approach	High accuracy