Engineering and Technology Journal

The transportation of crude oil naturally, including emulsion from the wellhead to the processing facility, presents a challenge in the oil industry, particularly as wells age and the production of associated water increases. To improve the flowability of the emulsified oil, traditional methods for reducing the viscosity, such as dilution and heating, are costly and energy-intensive. However, nanotechnology offers a potential solution to improve flowability and crude oil behavior. This paper examines how adding 3% wt of surface-modified silicon dioxide (SiO2) and magnesium oxide (MgO) nanoparticles impacts the flow properties of an emulsion containing East Baghdad crude oil. The investigation is conducted across different water cut levels (5%, 35%, 50%, and 75% v/v) within a horizontal pipe 0.0145 m inner diameter and 13m in length. The effect of these nanoparticles on emulsion stability, rheology, viscosity, pressure drop, and energy consumption was studied. The rheology study found that the best results were achieved by adding surface-modified nano silica at 3%, which significantly reduced viscosity with shear thinning behavior. Adding 3% nano-silica obtained a highly stable emulsion and a higher reduction of 69% in power consumption for pumping the fluid. In comparison, a 25% increase in power consumption was achieved by adding the same concentration of MgO.

This study aims to optimize the operational variables influencing the incorporation of Hesperidin nanoparticles (HSP NPs) into poly(lactic acid)/poly(butylene adipate-co-terephthalate) (PLA/PBAT) for manufacturing mixed matrix membranes (MMMs) for oily wastewater treatment. An optimization method was employed to determine the optimal values for key process factors to achieve specific flux and rejection rates exceeding required levels. Statistical techniques such as response surface methodology (RSM) and analysis of variance (ANOVA) were used to enhance performance on a larger scale. This research investigated the impact of operating parameters on the flux and oil rejection of PLA/PBAT/HSP membranes across all samples. The variables studied included HSP NPs content (0-0.05 wt.%), oil concentration (100-300 ppm), and transmembrane pressure (1.5-3.5 bar). A mathematical model for calculating flux and rejection (%) was developed. The findings indicated that the PLA/PBAT/HSP-based MMMs demonstrated optimal efficiency, achieving a flux of 121 LMH and oil rejection of 98.53%. The optimal conditions for the HSP MMMs were 0.03 wt.% HSP, an oil concentration of 158.28 ppm, and a pressure of 3.5 bar yielded the best response. The results show that the PLA/PBAT/HSP membranes exhibited enhanced flux and separation properties, making them suitable for treating oily wastewater in various applications.

Artificial intelligence (AI) is emerging as a prominent technological advancement. It is the act of replicating human intelligence for many purposes. In contrast to conventional methodologies, artificial intelligence (AI) is undergoing tremendous advancements. The present state of artificial intelligence (AI) technology enables them to effectively address numerous intricate difficulties with proficiency comparable to a human's. The significance of advancements in AI is particularly evident in machine learning, where the techniques and algorithms are effectively applied to address many problems, including those in nanotechnology. In contemporary nanotechnology, it is crucial to expedite the search for the most favorable synthesis parameters while developing novel nanomaterials. The convergence of machine learning and nanotechnology necessitates a comprehensive examination of existing data on the application of artificial intelligence (AI) in addressing challenges in the nanomaterials science field. This review should encompass various stages, including computer design, chemical synthesis, and diagnostics of the resultant nanomaterials. Significant emphasis is placed on employing machine-learning technologies to investigate the thermal and dynamic characteristics of nanofluids, the sorption processes of nanocomposites, the catalytic activity of nanoparticles, and the toxicity of nanoparticles. Additionally, these technologies are utilized to address nanosensor issues and process experimental data acquired during the diagnostics of different nanomaterial properties