The effect of shear rate on aggregation and breakage of asphaltenes flocs: Experimental study and model-based analysis

Unraveling the Impact of Ethylenediaminetetraacetic Acid Chelating Agents on the Oil Recovery Processes: An Insight into Its Role in Fluid-Fluid and Rock-Fluid Interactions

In recent years, chelating agents have garnered increasing attention due to their unique capabilities in addressing challenges associated with chemically enhanced oil recovery materials. Most previous studies have primarily focused on examining the influence of chelating agents on rock-fluid interactions. However, their effects on fluid-fluid interactions, specifically on interfacial tension (IFT) characteristics under various conditions, remain relatively unknown. This study investigates the effect of crucial reservoir parameters on the performance of ethylenediaminetetraacetic acid (EDTA) chelating agents in the IFT between crude oil and brine. Furthermore, the study evaluated the impact of varying concentrations of this agent on rock wettability alteration, zeta potential, and spontaneous imbibition. The results illustrated that introducing EDTA at high concentration into seawater (SW) solution reduced the IFT by 85%. Additionally, while higher pH levels contributed to IFT reduction due to increased hydroxyl ions, excessive salinity levels resulted in elevated IFT. Raising the temperature from 30 to 75 °C further decreased the IFT, changing the IFT for optimal EDTA concentration from 20.43 to 2.56 mN/m (∼88% reduction). The changes in zeta potential and contact angle measurements indicated that solutions of 5 and 7 wt % EDTA shifted rock wettability from oil-wet to strongly water-wet, resulting in the wettability alteration index of 1.01 and 1.02, respectively. Inductively coupled plasma analysis, on the other hand, revealed substantial chelation of metal ions from both the solution and rock when EDTA was added to the rock/SW system. This resulted in a significant increase in Ca2+ ions and a decrease in Mg2+ ions, attributed to the multi-ion exchange phenomenon.

Application of Artificial Intelligent Approach to Predict the Normal Boiling Point of Refrigerants

Due to the desirable and interesting applications of refrigerants in organic Rankine cycles, heat pumps, and refrigeration, engineers and researchers are becoming more interested in refrigerant properties. One of the most dominant thermophysical properties of these fluids is their normal boiling point (Tb). In the current study, a novel extreme learning method (ELM) and ensemble decision tree boosted algorithm (EDT Boosted) are proposed to forecast the normal boiling point from 16 different molecular groups and one topological index. To this end, a total of 334 data points of Tb are gathered to prepare and test ELM and EDT boosted algorithms. The visual and mathematical comparisons of model outputs and real Tb express that proposed models have great potential to predict Tb of refrigerant. Moreover, sensitivity analysis is applied to explain the effectiveness of input parameters on the determination of Tb for refrigerants.

Comprehensive Modeling in Predicting Biodiesel Density Using Gaussian Process Regression Approach

In this study, four Gaussian process regression (GPR) approaches by various kernel functions have been proposed for the estimation of biodiesel density as the functions of pressure, temperature, molecular weight, and the normal melting point of fatty acid esters. Comparing the actual values with GPR outputs shows that these approaches have good accuracy, but the performance of the rational quadratic GPR model is better than others. In this GPR model, RMSE = 0.47, MSE = 0.22, MRE = 0.04, R 2 = 1, and STD is equal to 0.3. In addition, for the first time, this study shows that the effective parameters affect the biodiesel density. According to this analysis, it was shown that among the input parameters, pressure has the greatest effect on the target values with a relevancy factor of 0.59. This study can be used as a suitable and valuable work/tool for chemical and petroleum engineers who attempt environment protection and recovery improvement.

Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties

Biodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadratic, Squared Exponential, Matern, and Exponential, to estimate biodiesel properties. These properties include kinematic viscosity (KV), pour point (PP), iodine value (IV), and cloud point (CP) as a function of fatty acid composition. In order to develop this model, some variables are assumed, such as molecular weight, carbon number, double bond numbers, monounsaturated fatty acids, polyunsaturated fatty acid, weight percent of saturated acid, and temperature. The performance and efficiency of the GPR model are measured through several statistical criteria and the results are summarized in root mean square error (RMSE) and coefficients of determination ( $R^2$ ). $R^2$ and RMSE are sorted as 0.992 & 0.15697, 0.998 & 0.96580, 0.966 & 1.38659, and 0.968 & 1.56068 for four properties such as KV, IV, CP, and PP, respectively. It is worth to mention this point that the kernel function Squared Exponential shows a great performance for IV and PP and kernel functions Exponential and Matern indicate appropriate efficiency for CP and KV properties, respectively. On the other hand, the results of the offered GPR models are compared with those of the previous models, LSSVM-PSO and ANFIS. The outcomes proved the superiority of this model over two former models in point of estimating the biodiesel properties.