Development of Coordinated Methodologies for Modeling CO2-Containing Systems in Petroleum Industry.

Masters Degree. University of KwaZulu-Natal, Durban.Clathrate hydrates formation in natural gas processing facilities or transportation pipelines may lead to process and/or safety hazards. On the other hand, a number of applications are suggested on the basis of promoting the gas hydrate formation. Some researchers have investigated separation and purification processes through gas hydrate crystallization technology. Some works report that the hydrate formation is applicable to the gas transportation and storage. Gas hydrate concept is also studied as a potential method for CO2 capture and/or sequestration. Water desalination/sweetening, and refrigeration and air conditioning systems are other proposed uses of hydrates phenomenon. In the realm of food processing and engineering, several studies have been done investigating the application of gas hydrate technology as an alternative to the conventional processes. Accurate knowledge of phase equilibria of clathrate hydrates is crucial for preventing or utilizing the hydrates. It is believed that energy production or extraction from different fossil fuels is responsible for considerable emissions of CO2, as an important greenhouse gas, into the atmosphere. Furthermore, CO2 removal from the streams of natural gas is important for enhancing the gaseous streams’ heating value. Employment of solvent-based processes and technologies for removing the CO2 is a widely employed approach in practical applications. Amine-based or pure amine solutions are the most common choice to remove the produced CO2 in numerous carbon capture systems. Further to the above, ionic liquids (ILs) are capable to be utilized to capture CO2 from industrial streams. Other potential solvent are sodium piperazine (PZ) and glycinate (SG) solutions. Equilibrium absorption of carbon dioxide in the aqueous phase is a key parameter in any solvent-based CO2 capture process designing. The captured CO2, then, can be injected into the hydrocarbon reservoirs. In addition to the fact that injection of CO2 into potential sources is one of the most reliable methodologies for enhanced hydrocarbon recovery, utilizing this process in conjunction with the CO2 capture systems mitigates the greenhouse effects of CO2. One of the most significant variables determining the success of CO2 injection is known to be the minimum miscibility pressure (MMP) of CO2-reservoir oil. This research study concerns implementation of computer-based methodologies called artificial neural networks (ANNs), classification and regression trees (CARTs)/AdaBoost-CART, adaptive neuro-fuzzy inference systems (ANFISs) and least squares support vector machines (LSSVMs) for modeling: (a) phase equilibria of clathrate hydrates in: 1- pure water, 2- aqueous solutions of salts and/or alcohols, and 3- ILs, (b) phase equilibria (equilibrium) of hydrates of methane in ILs; (c) equilibrium absorption of CO2 in amine-based solutions, ILs, PZ solutions, and SG solutions; and (d) MMP of CO2-reservoir oil. To this end, related experimental data have been gathered from the literature. Performing error analysis, the performance of the developed models in representing/ estimating the independent parameter has been assessed. For the studied hydrate systems, the developed ANFIS, LSSVM, ANN and AdaBoost-CART models show the average absolute relative deviation percent (AARD%) of 0.04-1.09, 0.09-1.01, 0.05-0.81, and 0.03-0.07, respectively. In the case of hydrate+ILs, error analysis of the ANFIS, ANN, LSSVM, and CART models showed 0.31, 0.15, 0.08, and 0.10 AARD% of the results from the corresponding experimental values. Employing the collected experimental data for carbon dioxide (CO2) absorption in amine-based solutions, the presented models based on ANFIS, ANN, LSSVM, and AdaBoost-CART methods regenerated the targets with AARD%s between 2.06 and 3.69, 3.92 and 8.73, 4.95 and 6.52, and 0.51 and 2.76, respectively. For the investigated CO2+IL systems, the best results were obtained using CART method as the AARD% found to be 0.04. Amongst other developed models, i.e. ANN, ANFIS, and LSSVM, the LSSVM model provided better results (AARD%=17.17). The proposed AdaBoost-CART tool for the CO2+water+PZ system reproduced the targets with an AARD% of 0.93. On the other hand, LSSVM, ANN, and ANFIS models showed AARD% values equal to 16.23, 18.69, and 15.99, respectively. Considering the CO2+water+SG system, the proposed AdaBoost-CART tool correlated the targets with a low AARD% of 0.89. The developed ANN, ANFIS, and LSSVM showed AARD% of more than 13. For CO2-oil MMP, the proposed AdaBoost-CART model (AARD%=0.39) gives better estimations than the developed ANFIS (AARD%=1.63). These findings revealed the reliability and accuracy of the CART/AdaBoost-CART methodology over other intelligent modeling tools including ANN, ANFIS, and LSSVM

Extreme learning machine-based model for solubility estimation of hydrocarbon gases in electrolyte solutions

Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants

Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artifcial intelligence

The surface tension (ST) of ionic liquids (ILs) and their accompanying mixtures allows engineers to accurately arrange new processes on the industrial scale. Without any doubt, experimental methods for the specification of the ST of every supposable IL and its mixtures with other compounds would be an arduous job. Also, experimental measurements are effortful and prohibitive; thus, a precise estimation of the property via a dependable method would be greatly desirable. For doing this task, a new modeling method according to artificial neural network (ANN) disciplined by four optimization algorithms, namely teaching�learning-based optimization (TLBO), particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA), has been suggested to estimate ST of the binary ILs mixtures. For training and testing the applied network, a set of 748 data points of binary ST of IL systems within the temperature range of 283.1�348.15 K was utilized. Furthermore, an outlier analysis was used to discover doubtful data points. Gained values of MSE & R2 were 0.0000007 and 0.993, 0.0000002 and 0.998, 0.0000004 and 0.996 and 0.0000006 and 0.994 for the ICA-ANN, TLBO-ANN, PSO-ANN and GA-ANN, respectively. Results demonstrated that the experimental data and predicted values of the TLBO-ANN model for such target are wholly matched

The determination of petroleum reservoir fluid properties : application of robust modeling approaches.

Doctor of Philosophy in Chemical Engineering. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file

CO₂ capture using ionic liquids: thermodynamic modeling and molecular dynamics simulation

Global climate change is happening now, and the average temperature of Earth is rising. Several evidences show that one of the main reasons for global warming is the increased concentration of greenhouse gases (GHGs) in the atmosphere, particularly carbon dioxide (CO₂). CO₂ is mostly producing from burning fossil fuels. One of the effective strategies to reduce CO₂ emissions is implementing carbon capture in fossil fuel power plants. Current post-combustion carbon capture techniques typically employ amine-based solvents, such as monoethanolamine (MEA), for the absorption of CO₂. Although alkanol amines have an acceptable absorption capacity, their high vapor pressure, solvent loss during desorption, and high corrosion rate make amines absorption plants energy-intensive. In recent years, Ionic Liquids (ILs) have been emerged as promising alternative solvents for physisorption and chemisorption of acid gases due to their unique physiochemical properties, including negligible vapor pressure, high thermal stability, tunability, and being environmentally safe. ILs require to be screened based on technical, economical, and environmental aspects. The main challenges of using ILs are increasing CO₂ capture capacity of ILs, and detailed understanding of the diffusivity of CO₂ in ILs, the effect of additives in solubility, selectivity features of ILs, phase behavior of gas-IL systems, and absorption mechanism. These challenges can be addressed using either experiment, thermodynamic modeling, and/or molecular simulations. In this study, the potential of the screened imidazolium-based ILs is investigated using thermodynamic modeling. The extended Peng–Robinson (PR) and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) EOSs are implemented to evaluate the solubility and selectivity of CO₂ in pure ILs and their mixture with water and toluene. The effects of water and toluene on solubility and viscosity of ILs are investigated. Low concentrations of water (< 1 wt%) do not affect solubility; however, with increasing water concentration, the solubility of CO2 is decreased. On the other hand, with increasing water content, the IL viscosity significantly decreases, which is in the favor of using viscous ILs for CO₂ separation. In this thesis, Molecular Dynamics (MD) simulation is performed to determine the properties of ILs ([Bmim][BF₄] and [Bmim][Ac]), their structures, and molecular dynamics. A great agreement is noticed between the density and viscosity of the studied ILs from MD simulations and experimental data, indicating the accuracy of our simulation runs. This study also includes the effect of temperature and anion type on the structuring of ions and their self-diffusivities. Bulk systems of ILs and CO₂ are studied to evaluate the influence of temperature and types of ions on the diffusivity of CO₂ in the solvent as well as structural characteristics. A comprehensive analysis of the characteristics of the interface of IL/CO₂ is performed to explore species distribution, gas behavior at the interface, and molecule orientation. At the interface, CO₂ creates a dense layer which interrupts the association of cations and anions, leading to a decrease in the surface tension. In addition, a comprehensive study on hydrophilic IL, 1-Butyl-3-methylimidazolium acetate or [Bmim][Ac], is conducted to evaluate the thermophysical properties, excess energy, structure, and dynamic characteristics of IL/Water and IL/Water/CO2 systems, using MD simulation approach. The effect of water on radial distribution functions, coordination numbers, water clusters, hydrogen bonding, and diffusivity coefficients of the ions is assessed. The presence of water in IL mixture, even at high concentrations of water (>0.8 mole fraction), increases the diffusivity of cation, anion, water, and CO2 molecules in the mixture due to hydrophilicity of [Bmim][Ac] IL. MD simulations generate reliable and accurate results while dealing with systems including water, CO₂, and IL for carbon capture. In this thesis, novel and robust computational approaches are also introduced to estimate the solubility of CO₂ in a large number of ILs within a wide range of temperatures and pressures. Four connectionist tools- Least Square Support Vector Machine (LSSVM), Decision Tree (DT), Random Forest (RF), and Multilinear Regression (MLR)- are employed to obtain CO₂ solubility in a variety of ILs based on thermodynamic properties and Quantitative Structure-Activity Relationship (QSPR) model. Among different types of descriptors, the most important input variables (e.g., Chi_G/D 3D and Homo/Lumo fraction (anion); SpMax_RG and Disps (cation)) are selected using Genetic Algorithm (GA)-MLR method. A great agreement between the predicted values and experimental measurements is attained while using RF and DT techniques developed based on descriptors and thermodynamics properties. The structural descriptors-based models are more accurate and robust than those built on critical properties

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system

Mathematical modeling and simulation of water-alternating-gas (WAG) injection

Water-alternating-gas (WAG) injection is a relatively mature oil recovery technique in hydrocarbon reservoirs that has attracted the interest of the oil and gas industry due to its successful performance. The main goal of a WAG process is to control the mobility and to decrease the problem of viscous fingering, leading to improved oil recovery by combining the benefits of gas injection (GI) and waterflooding (WF). Mathematical modeling and simulation of three-phase flow in porous media involve complexities related to the three-phase relative permeability, capillary pressure, and hysteresis effects that are cycle-dependent. Extensive theoretical studies are available in the literature, simulating immiscible and miscible WAG processes; however, the simulation study on near-miscible WAG is overlooked. Also, the majority of WAG simulation studies lack the cycle-dependent three-phase hysteresis that appears in the relative permeability and capillary pressure models. Production from naturally fractured reservoirs (NFRs) is more complicated (compared to homogeneous reservoirs) due to the flow communication between the matrix and fracture in fractured porous media. The implementation of water-alternating-gas (WAG) injection in NFRs also features inherent complexities related to the three-phase flow, the saturation history, and cycle-dependent hysteresis of the individual phases. Moreover, the experimental evaluation of WAG injection in a fractured system is expensive and time-consuming, if not impractical. In this research work, the three-phase flow modeling of near-miscible WAG process for enhanced oil recovery (EOR) implication is studied, using implicit pressure explicit saturation (IMPES) method. The mathematical model simulates a WAG case study in a strongly water-wet Berea core, using synthetic oil and brine at 38ﾟC and 12.7 MPa. The recovery data from the mathematical model is in excellent agreement with the experimental data of near-miscible WAG process. For instance, the absolute relative error is less than 1.7% while estimating the ultimate recovery factor of the oil in WF and GI stages of all three cycles. The effects of main variables such as injection rate, WAG ratio, slug size (PV) injection, crude oil viscosity, and core absolute permeability on the WAG performance are also studied. The findings from this study can help for better understanding of WAG injection at near-miscible condition for various scenarios under various conditions in terms of operational condition and rock and fluid’s characteristics. This work is also intended to simulate WAG injection in a fractured system through a computational fluid dynamics (CFD) approach. We evaluate the impacts of hysteresis, fracture characteristics (aperture, orientation, and fracture density in the network), and the three-phase relative permeability of phases during the WAG injection using COMSOL Multiphysics®. The model simulates an immiscible WAG injection, and the modeling results are compared to the experimental data in a strong water-wet sand-pack. Similar to the experiments, we simulate Maroon crude as the oil phase, synthetic brine, and pure CO₂ at 100ﾟC and atmospheric pressure. The results from our model are in excellent agreement with the experimental data. The absolute relative error is less than 12 % while predicting the ultimate recovery factors (RF) of the oil in water flooding (WF) and gas injection (GI) cycles. Including three-phase hysteresis significantly increases the accuracy of a WAG process simulation. Excluding the hysteresis remarkably decreases the instantaneous RFs at each cycle (especially GI cycles) and also the ultimate RF by 4%. The simulation results can help to manage and design the optimum operation of immiscible WAG in fractured reservoirs. In the fourth phase of the work, a total number of 1457 data points to predict three sets of two-phase relative permeabilities involved in the WAG injection process, and in a strongly water-wet sandstone core where smart tools such as least squares-support vector machine (LSSVM) and adaptive network-based fuzzy inference system (ANFIS) are employed. The statistical parameters including coefficient of determination, root mean square error, mean error, and standard deviation are used to examine the predictive models. The LSSVM shows a better performance compared to ANFIS in estimating relative permeabilities. The analysis based on relative importance of parameters shows that for the LSSVM model, water saturation is the most influencing input for gas-water and oil-water systems, while gas saturation is the most important input parameter in the gas-oil system. Final RF of WAG process after three cycles of water-and gas injection is 93.6%. Forecasting WAG flooding performance using fast and robust models is of great importance to obtain a better understanding of the process future, optimize the operational design procedure, and avoid high-cost blind tests in laboratory or pilot scales. In the last phase of this work, a novel correlation to predict the performance of near-miscible WAG injection is presented in strongly water-wet sandstones. An analytical correlation using gene expression programming (GEP) technique is developed. Dimensional analysis technique is applied and generated dimensionless numbers using eight key parameters with the aid of the Buckingham’s π theorem. Based on the error analysis, the newly developed GEP-based correlation leads to the predictions, which are in a good match with the target data so that R²= 92.85 % and MSE=1.38e-3 are obtained for the training phase; and the testing phase results in R²= 91.93 % and MSE=4.30e-3. The correlation proposed in this phase can be used to forecast the RF of a WAG injection process before committing to expensive and time-consuming laboratory and pilot tests

Modeling pan evaporation using Gaussian Process Regression, K-Nearest Neighbors, Random Forest, and Support Vector Machines: Comparative analysis

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters

Developing magnetic functionalized multi-walled carbon nanotubes-based buckypaper for the removal of Furazolid

Magnetic f-MWCNTs-based BP/PVA membrane was fabricated and utilized for the elimination of furazolidone (FZD) from aqueous solution. Characterisation and adsorption studies were performed to evaluate the performance and adsorptive efficiency, respectively of the membrane. Furthermore, statistical and machine learning technique were also applied to predict the removal efficiency of FZD on the membrane. The results revealed that magnetic f-MWCNTs-based BP/PVA membrane has the potential to be used as an efficient membrane for practical applications