Inclusion of shear rate effects in the kinetics of a discretized population balance model: Application to struvite precipitation

Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery

The precipitation and deposition of asphaltene on solid surfaces present a significant challenge throughout all stages of petroleum recovery, from hydrocarbon reservoirs in porous media to wellbore and transfer pipelines. A comprehensive understanding of asphaltene aggregation phenomena is crucial for controlling deposition issues. In addition to experimental studies, accurate prediction of asphaltene aggregation kinetics, which has received less attention in previous research, is essential. This study proposes an artificial intelligence-based framework for precisely predicting asphaltene particle aggregation kinetics. Different techniques were utilized to predict the asphaltene aggregate diameter as a function of pressure, temperature, oil specific gravity, and oil asphaltene content. These methods included the adaptive neuro-fuzzy interference system (ANFIS), radial basis function (RBF) neural network optimized with the Grey Wolf Optimizer (GWO) algorithm, extreme learning machine (ELM), and multi-layer perceptron (MLP) coupled with Bayesian Regularization (BR), Levenberg-Marquardt (LM), and Scaled Conjugate Gradient (SCG) algorithms. The models were constructed using a series of published data. The results indicate the excellent correlation between predicted and experimental values using various models. However, the GWO-RBF modeling strategy demonstrated the highest accuracy among the developed models, with a determination coefficient, average absolute relative deviation percent, and root mean square error (RMSE) of 0.9993, 1.1326%, and 0.0537, respectively, for the total data.

A comparison of struvite precipitation thermodynamics and kinetics modelling techniques

Solution thermodynamics and kinetic modelling applied to struvite crystallisation-precipitation were reviewed from diverse references to determine proximity between predicted and cited experimental measurements. These simulations show the expected variability range of struvite saturation calculation when only limited solution compositional information is given, showing acceptable agreement between predicted and experimental struvite mass. This work also compares results from struvite crystallisation kinetic studies on liquid phase species depletion, crystallisation induction time, primary nucleation, secondary nucleation, crystal growth, and crystal aggregation. Large inconsistencies between reported kinetics were observed in many scenarios. Variations in species depletion models highlighted that they are only suitably applied to the specific system from which they were regressed. Spontaneous primary nucleation was predicted to occur in the range of SI = 0.237-0.8. Predicted primary nucleation rates vary over at least 10 orders of magnitude (depending on supersaturation) because of uncertainties in interfacial tension and maximum achievable nucleation rate. Secondary nucleation rates are more agreeable, varying over approximately two orders of magnitude. Growth rates varied over five orders of magnitude due to variations in experimental conditions. Aggregation rates are not thoroughly examined enough to make any inferences.