Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO 2 -crude oil minimum miscibility pressure

Karkevandi-Talkhooncheh, Abdorreza; Hajirezaie, Sassan; Hemmati-Sarapardeh, Abdolhossein; Husein, Maen M.; Karan, Kunal; Sharifi, Mohammad

doi:10.1016/j.fuel.2017.05.026

Cited by 96 publications

(37 citation statements)

References 65 publications

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“…The MMP of CO 2 -Bakken live oil system determined by MD modeling agrees well with the slim tube test result at 129°C [7], and MMPs of CO 2 -Bakken dead oil system from this study are consistent with the VIT method data at two different temperatures [7]. Furthermore, it can be seen that the MMP increases with the temperature increasing for CO 2 -crude oil system, which has been reported by previous work [40][41][42]. MMPs of CO 2 -live oil system are higher than of CO 2 -dead oil system at the same temperature, coinciding with experimental results from Menouar et al [43], which may be due to the presence of C 1 in the former.…”

Section: Interfacial Properties and Mmpsupporting

confidence: 90%

Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO₂

et al. 2018

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CO 2 enhanced oil recovery (CO 2 -EOR) has gained great attention worldwide for increasing oil production and reducing greenhouse emissions. In CO 2 -EOR project, the minimum miscibility pressure (MMP) is the key factor to determine the possibility of CO 2 miscible-phase displacement. In this study, we used molecular dynamics (MD) simulation to estimate the MMP of pure and impure CO 2 with oil by obtaining interfacial tensions (IFTs) based on the vanishing interfacial tension technique. To get accurate IFTs, modified Lorentz-Berthelot rules were developed for all simulations. Then, we performed MD simulations for CO 2 +n-hexane binary system, CO 2 +n-hexane+n-decane ternary system and complex CO 2 +crude oil system to calculate the MMPs. Firstly, MMPs predicted by MD simulation show good agreement with previous experimental data. Secondly, the effects of injected gas components, oil components and reservoir temperature on the MMP are investigated. The results demonstrate that methane and N 2 will increase the MMP of CO 2 -oil system significantly; the effects of a certain oil component on the MMP depend on the properties of crude oil, generally speaking, heavy oil components will increase the MMP while light oil components decrease it; temperature will increase the MMP of CO 2 -oil system, while it will decrease the MMP of N 2 -oil system, which is due to their different solubility in oil. Thirdly, sensitivity analysis is conducted to evaluate the effects of those influence parameters on the MMP of CO 2 -oil system, finding that N 2 concentration in CO 2 and reservoir temperature have more significant impacts on the MMP.

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Section: Interfacial Properties and Mmpsupporting

confidence: 90%

Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO₂

et al. 2018

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“…In this research, TSK FIS model was employed, which is more powerful at handling non-linear input-output relationships [26]. TSK uses the pattern of input and output data to create if-then rules.…”

Section: Anfismentioning

confidence: 99%

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

et al. 2019

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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.

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“…[96][97][98] More details about the LM, BR, SCG, PSO, and GA algorithms can be found in our previous papers. [69,99] However, for ICA, interested readers can refer to Atashpaz-Gargari and Lucas [97] and Haykin. [101]…”

Section: Optimization Techniquesmentioning

confidence: 99%

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

Hemmati‐Sarapardeh¹,

Dabir²,

Ahmadi³

et al. 2018

Can J Chem Eng

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Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.

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Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO 2 -crude oil minimum miscibility pressure

Cited by 96 publications

References 65 publications

Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO₂

Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO₂

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

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Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO 2 -crude oil minimum miscibility pressure

Cited by 96 publications

References 65 publications

Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO2

Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO2

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling

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Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO₂

Molecular dynamics simulation to estimate minimum miscibility pressure for oil with pure and impure CO₂