Continuous Prediction of the Gas Dew Point Temperature for the Prevention of the Foaming Phenomenon in Acid Gas Removal Units Using Artificial Intelligence Models

Masoud Rohani,Hooshang Jazayeri-Rad,Reza Mosayebi Behbahani

doi:10.2991/ijcis.2017.10.1.12

Masoud Rohani, Hooshang Jazayeri-Rad + Show 1 more

Open Access

https://doi.org/10.2991/ijcis.2017.10.1.12

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Abstract

Acid gas removal (AGR) units are widely used to remove CO2 and H2S from sour gas streams in natural gas processing. When foaming occurs in an AGR system, the efficiency of the process extremely decreases. In this paper, a novel approach is suggested to regularly predict the gas dew point temperature (GDPT) in order to anticipate the foaming conditions. Prediction of GDPT is advantageous because the conventional methods of measuring GDPT such as: (i) using a chilled mirror device is time consuming; and (ii) the use of gas chromatograph for composition determination combined with the equation-of-state calculations involve column retention time and is expensive. New hybrid modeling algorithms based on the artificial neural network (ANN) combined with either the imperialist competitive algorithm (ICA) or particle swarm optimization (PSO) are employed to model the process. The models can then be used to prevent the foaming phenomenon. The proposed algorithms combine the local searching ability of ANN with the global searching abilities of ICA and PSO. ICA and PSO are used to optimize the initial weights of the neural networks. The resulting ICA-ANN and PSO-ANN combined algorithms are then applied to model the occurrence of foaming in the AGR plant based on a simulation data set acquired from the 6th refinery of the south Pars gas complex in Iran. The performances of the ICA-ANN, PSO-ANN and conventional ANN models are then compared against each other. It was found that the accuracies of the ICA-ANN and PSO-ANN models are better than that of the conventional ANN model. In addition, the PSO-ANN model outperformed the ICA-ANN model.

Highlights

artificial neural network (ANN) is a tool widely used in modeling and control problems
The purpose of the present study is to develop ANNbased models using imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) to estimate the conditions of the foaming phenomenon in an absorber column of a gas sweetening plant
The predicted output and the measured data for gas dew point temperature (GDPT) are given in Figs. 5(a), 5(b), and 5(c) for the ANN, ICAANN, and PSO-ANN models, respectively

Summary

Introduction

The most important factor in utilizing ANN is the determinations of its network structure and parameters. Evolutionary algorithms such as ICA2 and PSO3, 4 can be employed to achieve these objectives. The amine solution and sour gas are contacted in an absorber column to remove the acid gas. The sour gas stream enters the bottom of the absorber and is contacted to the lean amine solution entered from the top. The lean amine absorbs the acid gas and leaves the bottom of column as rich amine. The work required to expand the surface area is called the surface free energy (G) which depends on the cohesive and intermolecular forces in the liquid. Liquids with a low surface tension require less energy to expand their surfaces and tend to foam ; the lower the surface tension the more will be their tendency to create foam

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Conclusion