Investigating the mechanism of interfacial tension reduction through the combination of low-salinity water and bacteria.

In the enhanced oil recovery (EOR) process, interfacial tension (IFT) has become a crucial factor because of its impact on the recovery of residual oil. The use of surfactants and biosurfactants can reduce IFT and enhance oil recovery by decreasing it. Asphaltene in crude oil has the structural ability to act as a surface-active material. In microbial-enhanced oil recovery (MEOR), biosurfactant production, even in small amounts, is a significant mechanism that reduces IFT. This study aimed to investigate fluid/fluid interaction by combining low biosurfactant values and low-salinity water using NaCl, MgCl2, and CaCl2 salts at concentrations of 0, 1000, and 5000 ppm, along with Geobacillus stearothermophilus. By evaluating the IFT, this study investigated different percentages of 0, 1, and 5 wt.% of varying asphaltene with aqueous bulk containing low-salinity water and its combination with bacteria. The results indicated G. Stearothermophilus led to the formation of biosurfactants, resulting in a reduction in IFT for both acidic and basic asphaltene. Moreover, the interaction between asphaltene and G. Stearothermophilus with higher asphaltene percentages showed a decrease in IFT under both acidic and basic conditions. Additionally, the study found that the interaction between acidic asphaltene and G. stearothermophilus, in the presence of CaCl2, NaCl, and MgCl2 salts, resulted in a higher formation of biosurfactants and intrinsic surfactants at the interface of the two phases, in contrast to the interaction involving basic asphaltene. These findings emphasize the dependence of the interactions between asphaltene and G. Stearothermophilus, salt, and bacteria on the specific type and concentration of asphaltene.

Novel hybrid neuro-fuzzy model to anticipate the heat transfer in a heat exchanger equipped with a new type of self-rotating tube insert.

In this investigation, a combination of the wingsuit flying search (WFS) and teaching-learning-based optimization (TLBO) algorithms is developed as a new combinatorial optimization algorithm. The proposed combinatorial algorithm is tested over some well-known benchmark functions and then integrated with the artificial neural network (ANN) to construct a novel hybrid model. After that, the obtained hybrid model is employed to anticipate the experimentally obtained values of the average Nusselt number (Nu), average friction coefficient (f) as well as thermal-hydraulic performance ratio (η), in a heat exchanger equipped with a new type of self-rotating tube insert, against governing parameters. The insert is placed in the tube side of the water heater to heat natural gas. The proposed insert consists of various numbers of self-rotating modules. Indeed, the rotating insert is introduced to create effective secondary sweeping flow on the inner side of the tube. Since this type of tube insert simultaneously provides heat transfer enhancement and undesired pressure drop, a thermal-hydraulic performance ratio is defined to consider both of them. The governing parameters are the number of inserts (0 ≤ N ≤ 30), reservoir's temperature (40 °C  ≤  TR ≤ 50 °C) as well as Reynolds number (6 × 103 ≤ Re ≤ 18 × 103). It was found that the WFS-TLBO enhances the effectiveness of the main ANN in anticipating the Nusselt number (Nu), average friction coefficient (f) as well as performance ratio (η). Moreover, introducing the WFS-TLBO algorithm into the neural network provides an enhancement in the effectiveness of the hybrid models based on the single WFS and TLBO algorithms in anticipating the same parameters.