Abstract
Asphaltenes deposition is considered a serious production problem. The literature does not include enough comprehensive studies on adsorption phenomenon involved in asphaltenes deposition utilizing inhibitors. In addition, effective protocols on handling asphaltenes deposition are still lacking. In this study, three efficient artificial intelligent models including group method of data handling (GMDH), least squares support vector machine (LSSVM), and artificial neural network (ANN) are proposed for estimating asphaltenes adsorption onto NiO/SAPO-5, NiO/ZSM-5, and NiO/AlPO-5 nanocomposites based on a databank of 252 points. Variables influencing asphaltenes adsorption include pH, temperature, amount of nanocomposites over asphaltenes initial concentration (D/C0), and nanocomposites characteristics such as BET surface area and volume of micropores. The models are also optimized using nine optimization techniques, namely coupled simulated annealing (CSA), genetic algorithm (GA), Bayesian regularization (BR), scaled conjugate gradient (SCG), ant colony optimization (ACO), Levenberg–Marquardt (LM), imperialistic competitive algorithm (ICA), conjugate gradient with Fletcher-Reeves updates (CGF), and particle swarm optimization (PSO). According to the statistical analysis, the proposed RBF-ACO and LSSVM-CSA are the most accurate approaches that can predict asphaltenes adsorption with average absolute percent relative errors of 0.892% and 0.94%, respectively. The sensitivity analysis shows that temperature has the most impact on asphaltenes adsorption from model oil solutions.
Highlights
Depending on oil composition and process conditions, asphaltenes deposition may pose a serious concern during the production of light and heavy oils [1]
Among the ten proposed models, radial basis function (RBF)-ant colony optimization (ACO) and least squares support vector machine (LSSVM)-coupled simulated annealing (CSA) with AAPRE% values under 1% appear the most accurate, whereas the group method of data handling (GMDH) has the least accuracy
It was found that LSSVM, artificial neural network (ANN), and GMDH optimized by LM, Bayesian regularization (BR), conjugate gradient with Fletcher-Reeves updates (CGF), scaled conjugate gradient (SCG), genetic algorithm (GA), particle swarm optimization (PSO), CSA, imperialistic competitive algorithm (ICA), and ACO sufficiently simulate asphaltenes adsorption data onto nanocomposites
Summary
Depending on oil composition and process conditions, asphaltenes deposition may pose a serious concern during the production of light and heavy oils [1]. The physical and chemical nature, and amount of asphaltenes extracted from a crude oil depend on many factors (e.g., solvent, contact time, dilution proportion, extraction procedure, and temperature) [6,7,8]. Such a disparity among the characteristics of asphaltenes molecules causes a challenge when dealing with these complicated molecules. Asphaltenes self-association and adsorption result in a number of problems during crude oil production and upgrading; including pipeline plugging, wettability alteration, pore blockage, and catalyst coking [22,23,24,25,26]
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