A computational simulation study for techno-economic comparison of conventional and stripping gas methods for natural gas dehydration

In the present work,the conventional natural gas dehydration method(CDM)and stripping gas method(SGM)are technically and economically analyzed,utilizing Aspen HYSYS and Aspen Process Economic Analyzer(APEA),respectively.To optimize the CDM and SGM,the sensitivities of the water content of dry gas,reboiler duty and raw material loss are analyzed against solvent rate and stripping gas rate.The optimized processes are set to achieve a targeted value of water content in dry gas and analyzed at optimized point.The analysis shows that SGM gives 46%lower TEG feed rate,42%lower reboiler duty and 99.97%pure regenerated TEG.Moreover,economic analysis reveals that SGM has 38%lower annual operating cost compared to CDM.According to results,from both technical and economic point of view,SGM is more feasible for natural gas dehydration compared to CDM.

Evaluation of influential parameters for supersonic dehydration of natural gas: Machine learning approach

The supersonic dehydration of natural gas is gaining more attention due to its numerous advantages over the conventional natural gas dehydration technologies.However,supersonic separators have seen minimal field applications despite the multiple benefits over other gas dehydration techniques.This has been mostly attributed to the uncertainty in ascertaining the design and operating parameters that should be monitored to ensure optimum dehydration of the supersonic separation device.In this study,the decision tree machine learning model is employed in investigating the effects of design and operating parameters(inlet and outlet pressures,nozzle length,throat diameter,and pressure loss ratio)on the supersonic separator performance during dehydration of natural gas.The model results show that the significant parameters influencing the shock wave location are the pressure loss ratio and nozzle length.The former was found to have the most significant effect on the dew point depression.The dehydration efficiency is mainly dependent on the pressure loss ratio,nozzle throat diameter,and the nozzle length.Comparing the machine learning model-accuracy with a 1-D iterative model,the machine learning model outperformed the 1-D iterative model with a lower mean average percentage error(MAPE)of 5.98 relative to 15.44 as obtained for the 1-D model.