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

Abstract

Acid gas removal (AGR) units are widely used to remove CO 2 and H 2 S 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 6 th 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.

Testing and modeling of constant magnetic field cylindrical magnetoelectric sensors output characteristics

“…The advantage of the heuristic regression method is the automated extraction of input-output relations. Among multiple approaches (such as support vector machines [41], kriging or M5 model Tree [42]), the MLP was selected. This type of ANN is well established in industrial applications and widely used for tasks where the modeled data are not affected by the extensive amount of noise (as experiments in the presented case show).…”

Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients

“…The numbers of the three nodes in the hidden layer of AEs in SAE Figure 9 Flow chart of the atmospheric column. (5) 0.075694 x (9) 0.053006 x (13) 0.072433 x (2) 0.069409 x (6) 0.103963 x (10) 0.054964 x (14) 0.054684 x (3) 0 x (7) 0.050637 x (11) 0.084609 x (15) 0.064075 x (4) 0 x (8) 0.034854 x (12) 0.085078 x (16) 0.124650 are 9, 6, and 4. Figure 12 gives the absolute error trend along with the test sample number in the naphtha dry point dataset by our proposed method.…”

“…A number of data-driven modeling methods are available, and these methods are mainly divided into two categories. One is based on multivariate statistical algorithms, including principal component regression (PCR) [5] and partial least-squares regression (PLS) [6], and the other is based on statistical machine learning algorithms, such as support vector regression (SVR) [7], genetic algorithm (GA) [8], and artificial neural network (ANN) [9]. Although these algorithms can be applied to various fields, some problems, such as those related to robustness and accuracy, still exist in the soft sensor modeling process.…”

Selection Criteria for Antifoams Used in the Acid Gas Sweetening Process

“…One of the main foaming agents in an acid gas sweetening unit, primarily in an absorber and regenerator, is the presence of condensed liquid hydrocarbon. The presence of these hydrocarbons tends to reduce the surface tension of the solution, which results in foaming . In addition, foaming can be caused by the presence of heat stable salts, iron sulfide, organic acids, corrosion inhibitors, makeup water impurities, and also degraded products.…”

“…The presence of these hydrocarbons tends to reduce the surface tension of the solution, which results in foaming. 11 In addition, foaming can be caused by the presence of heat stable salts, iron sulfide, organic acids, corrosion inhibitors, makeup water impurities, and also degraded products.…”