Estimation of asphaltene precipitation in light, medium and heavy oils : experimental study and neural network modeling

Asphaltene can precipitate in oil reservoirs as a result of natural depletion and/or gas injection crippling the oil production performance. Most of the conventional models for asphaltene precipitation cannot precisely capture the asphaltene precipitation at a wide pressure range and for different oil types. To have a precise model that can be used for various oil types at a wide range of pressure conditions, a comprehensive artificial neural network (ANN) model was proposed to estimate the weight percent of precipitated asphaltene in different oil types (three oil types, namely light, medium and heavy). The dilution ratio, pressure, molecular weight of solvent, API gravity and resin-to-asphaltene ratio were considered as the model input parameters. The oil samples were thus categorized based on the differences in their API gravity and resin-to-asphaltene ratio. Five hundred and fifty experimental precipitation datapoints were obtained from our experimental apparatus in a wide range of pressure, dilution ratio and injected fluid molecular weight, and used to make a comprehensive databank for model calibration and verification. At the test stage, the coefficient of correlation (R2) was higher than 0.98 and mean square error was less than 0.04 indicating the good performance of the proposed model. Furthermore, a comparison between the prediction of ANN model and two types of alternative approaches, namely the thermodynamic and the fractal/aggregation approaches, was performed. For this purpose, the prediction of two of the widely used solubility models, Flory–Huggins and Modified Flory–Huggins and also a polydisperse thermodynamic model was compared to the prediction of the proposed ANN model. In addition to those, as a fractal/aggregation model, a scaling model was also selected and employed to compare its performance against that of the proposed ANN model. The ANN model showed a better performance as compared to the other conventional models. The results demonstrated that the proposed model provides acceptable prediction for different oil types over a wide range of pressure which is a difficult task for most of the conventional techniques

Prediction of hydrate formation conditions to separate carbon dioxide from fuel gas mixture in the presence of various promoters

In this study estimation of hydrate formation conditions to separate carbon dioxide (CO2) from fuel gas mixture (CO2+H2) was investigated in the presence of promoters such as tetra-n-butylammonium bromide (TBAB), tetra-n-butylammonium fluoride (TBAF), and tetra-n-butyl ammonium nitrate (TBANO3). The emission of CO2 from the combustion of fuels has been considered as the dominant contributor to global warming and environmental problems. Separation of CO2 from fuel gas can be an effective factor to prevent many of environmental impacts. Gas hydrate process is a novel method to separate and storage some gasses. In this communication, a feed-forward artificial neural network algorithm has been developed. To develop this algorithm, the experimental data reported in the literature for hydrate formation conditions in the fuel gas system with different concentrations of promoters in aqueous phase have been used. Finally, experimental data compared with estimated data and with calculation of efficiency coefficient, mean squared error, and mean absolute error show that the experimental data and predicted data are in acceptable agreement which demonstrate the reliability of this algorithm as a predictive tool

Prediction of hydrate formation temperature based on an improved empirical correlation by imperialist competitive algorithm

Formation of gas hydrates is an important problem in production and transmission of natural gases. Hence, it is a particular interest and also vital to estimate the hydrate formation conditions. To do so, the Katz gas gravity method can be used as one of the simplest techniques. Numerous correlations have been presented so far, among which one of the most popular one with very good accuracy is the so called Ghiasi model. In this study, the imperialist competitive algorithm is applied to improve the Ghiasi correlation by using the Katz data. Error analysis of the results demonstrated that the improved equation had the highest precision in comparison with the Ghiasi correlation and also the other models reported by researchers

Reliable Tools to Forecast Sludge Settling Behavior: Empirical Modeling

In water- and wastewater-treatment processes, knowledge of sludge settlement behavior is a key requirement for proper design of a continuous clarifier or thickener. One of the most robust and practical tests to acquire information about rate of sedimentation is through execution of batch settling tests. In lieu of conducting a series of settling tests for various initial concentrations, it is promising and advantageous to develop simple predictive models to estimate the sludge settlement behavior for a wide range of operating conditions. These predictive mathematical model(s) also enhance the accuracy of outputs by eliminating measurement errors originated from graphical methods and visual observations. In the present study, two empirical models were proposed based on Vandermonde matrix (VM) characteristics as well as a Levenberg–Marquardt (LM) algorithm to predict temporal height of the supernatant–sludge interface. The novelty of our modeling approach is twofold: the proposed models in this study are more robust and simpler compared to other models in the literature, and the initial sludge concentration was considered as a key independent variable in addition to the more-customarily used settling time. The prediction performance of the VM-based model was better than the LM-based model considering the statistical parameters associated with the fitting of the experimental data including coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The values of R2, RMSE, and MAPE for the VM- and LM-based models were obtained at 0.997, 0.132, and 5.413% as well as 0.969, 0.107, and 6.433%, respectively. The proposed predictive models will be useful for determination of the sedimentation behavior at pilot- or industrial-scale applications of water treatment, when the experimental methods are not feasible, time is limited, or adequate laboratory infrastructure is not available

Prediction of hydrate equilibrium conditions using k-nearest neighbor algorithm to CO2 capture

Gas or clathrate hydrates are an important issue when they form in the oil and gas pipelines. Since the determination of the hydrate formation temperature and pressure is very difficult experimentally for every gas system and it is impossible in terms of cost and time approximately, mathematical models can be useful tools to overcome these difficulties. In this study, k-nearest neighbor model was used to predict the equilibrium conditions of hydrate formation in absorption and separation of carbon dioxide from flue gas mixture, containing carbon dioxide and nitrogen. At the training phase, temperature and composition data of nitrogen and carbon dioxide in the flue gas mixture at equilibrium conditions and the equilibrium pressures of hydrate formation were used as input and output, respectively. The error percentage less than 0.38% indicates the high accuracy of the proposed model. In this study, 80%, 85%, and 90% of the training data are examined for three numbers of nearest. For three numbers of used nearest (k = 1, k = 2 and k = 3), the value of k = 1 leads to the lowest error; so, it is selected as the best nearest in the presented model

New Modeling Strategies Evaluate Bubble Growth in Systems of Finite Extent: Energy and Environment Implications

Growth of a new phase is experienced in several chemical engineering processes/operations such as polymer foaming, oil/gas transportation, bubbly columns, distillation towers, and petroleum recovery. Thus, it seems vital to understand the dynamics of transport phenomena and new phase (e.g., bubbles) evolution over the corresponding processes to efficiently design and operate the plant equipment. The present study introduces new analytical and approximate solutions for the growth of a new phase in a medium of finite extent. The governing conservation equations are solved by an effective mathematical approach through combination of variables and enhanced homotopy perturbation method (EHPM) where it is assumed that the mass transfer controls the rate of growth through both convection and diffusion mechanisms. The modeling outcome confirms the importance of the convection mass transfer in growth of the new phase in finite extent. The results show that the radius of the new phase is strongly dependent on the diffusivity, temperature, and initial void fraction. It is also found that the bubble evolution follows the power-law model. To examine the practical effectiveness of the developed mathematical models, the growth of water bubbles, hydrate particles (as a new phase), and bubbles (or gas) growth in oil reservoirs are studied. The models’ results are compared with some experimental data, exhibiting a very good agreement (e.g., error percentage of <5%). This research work offers systematic strategies for investigation of bubble expansion/shrinkage phenomena where the bubble growth dynamics are observed in various processes/systems

Prediction of carbon dioxide separation from gas mixtures in petroleum industries using the Levenberg–Marquardt algorithm

In this study, two mathematical models for hydrate formation process to separate carbon dioxide by a combination of two different kinds of organic and surfactant promoters are presented. Promoters such as sodium dodecyl sulfate, sodium dodecyl benzene sulfonate, and dodecyl trimethyl ammonium chloride as surfactant promoters; also, tetrahydrofuran, cyclopentane, 1,3-dioxolane, and 2-methyl tetrahydrofuran as organic promoters have been used in recent years. The results showed that a combination of 3000 ppm of surfactant promoters and 4 wt% organic promoters had the highest separation rate of carbon dioxide and; consequently, the investigated models were based on this optimum condition. As a matter of fact, by using these simulations the hydrate formation behavior was predicted with high accuracy; moreover, conducting consuming experiments is not essential anymore. To sum up, in the present research both Vandermonde matrix model and Levenberg-Marquardt algorithm were applied to predict the hydrate formation behavior; in addition, their results were precisely considered and validated with experimental data