Preprint Article Version 1 This version is not peer-reviewed

Gaussian Process Prediction Model to Estimate Excess Adsorption Capacity of Supercritical CO2

Version 1 : Received: 4 February 2020 / Approved: 5 February 2020 / Online: 5 February 2020 (14:09:33 CET)

How to cite: Nabipour, N.; Qasem, S.N.; Mosavi, A.; Shamshirband, S. Gaussian Process Prediction Model to Estimate Excess Adsorption Capacity of Supercritical CO2. Preprints 2020, 2020020069 (doi: 10.20944/preprints202002.0069.v1). Nabipour, N.; Qasem, S.N.; Mosavi, A.; Shamshirband, S. Gaussian Process Prediction Model to Estimate Excess Adsorption Capacity of Supercritical CO2. Preprints 2020, 2020020069 (doi: 10.20944/preprints202002.0069.v1).

Abstract

Deep coal beds have been suggested as possible usable underground geological locations for carbon dioxide storage. Furthermore, injecting carbon dioxide into coal beds can improve the methane recovery. Due to importance of this issue, a novel investigation has been done on adsorption of carbon dioxide on various types of coal seam. This study has proposed four types of Gaussian Process Regression (GPR) approaches with different kernel functions to estimate excess adsorption of carbon dioxide in terms of temperature, pressure and composition of coal seams. The comparison of GPR outputs and actual excess adsorption expresses that proposed models have interesting accuracy and also the Exponential GPR approach has better performance than other ones. For this structure, R2=1, MRE=0.01542, MSE=0, RMSE=0.00019 and STD=0.00014 have been determined. Additionally, the impacts of effective parameters on excess adsorption capacity have been studied for the first time in literature. According to these results, the present work has valuable and useful tools for petroleum and chemical engineers who dealing with enhancement of recovery and environment protection.

Subject Areas

coal; supercritical CO2; Gaussian process regression; machine learning; adsorption model

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