Removal of heavy oil from contaminated surfaces with a detergent formulation containing biosurfactants produced by Pseudomonas spp.

Industrial plants powered by heavy oil routinely experience problems with leaks in different parts of the system, such as during oil transport, the lubrication of equipment and mechanical failures. The surfactants, degreasing agents and solvents that make up detergents commonly used for cleaning grease-covered surfaces are synthetic, non-biodegradable and toxic, posing risks to the environment as well as the health of workers involved in the cleaning process. To address this problem, surfactant agents of a biodegradable nature and low toxicity, such as microbial surfactants, have been widely studied as an attractive, efficient solution to replace chemical surfactants in decontamination processes. In this work, the bacterial strains Pseudomonas cepacia CCT 6659, Pseudomonas aeruginosa UCP 0992, Pseudomonas aeruginosa ATCC 9027 and Pseudomonas aeruginosa ATCC 10145 were evaluated as biosurfactant producers in media containing different combinations and types of substrates and under different culture conditions. The biosurfactant produced by P. aeruginosa ATCC 10145 cultivated in a mineral medium composed of 5.0% glycerol and 2.0% glucose for 96 h was selected to formulate a biodetergent capable of removing heavy oil. The biosurfactant was able to reduce the surface tension of the medium to 26.40 mN/m, with a yield of approximately 12.00 g/L and a critical micelle concentration of 60.00 mg/L. The biosurfactant emulsified 97.40% and dispersed 98.00% of the motor oil. The detergent formulated with the biosurfactant also exhibited low toxicity in tests involving the microcrustacean Artemia salina and seeds of the vegetable Brassica oleracea. The detergent was compared to commercial formulations and removed 100% of the Special B1 Fuel Oil (OCB1) from different contaminated surfaces, demonstrating potential as a novel green remover with industrial applications.

The application of coupled 3d hydrodynamic and oil transport model to oil spill incident in karawang offshore, indonesia

This study presents a coupled hydrodynamic and oil transport numerical model to study the spread of Karawang oil spills at sea due to well-kick failures. This model uses the 3D configuration of ROMS-CROCO in the Java Sea. The model has a resolution of 1 km, 25 vertical layers, and runs from January 2019 to September 2019. Temperature, salinity, sea surface height, ocean currents, and harmonic tides are derived from global models and applied to open boundaries. Hourly atmospheric datasets during model simulation are forced as flux input in the surface. The 3D profile of the flow generated by the model is converted to the GNOME oil transport model format as mover type input to disperse the oil. The hydrodynamic model shows that the result has a good agreement with in-situ data and observation with mean of correlation exceeding r>0.8 for sea surface height and sea surface temperature. Compared with radar satellites, oil spill dispersion shows the same scattered trend as satellite data. Backward modelling shows oil particles returning to the initial spill location. The oil spill was moving westward, and some are stranded on the coast between Karawang and Bekasi.

Transport Behavior of Liquid Hydrocarbon in Shale Matrix with Mixed Wettability Nanopores

Shale oil is an unconventional petroleum resource which has high total organic carbon (TOC) content and abundant nanopores. The transport behavior of oil through organic rich shales cannot be described by the classical Darcy law due to its complex pore structure and the complicated distribution of organic matter, which results in nanoconfined effects. In this work, on the basis of the boundary slip phenomenon and the fractal scaling theory, a model for oil transport in shale matrix is established considering nanoconfined effects and adsorbed organic matter. The results show that it is necessary to make correction of viscosity and the boundary slip length in order to accurately describe the flow behavior of oil in shale matrix with mixed wettability nanopores. Long chain molecules are more sensitive to nanoconfined effects, especially when adsorbed organic matter is considered. Also, the oil transport capacity in organic shale matrix is greatly enhanced compared to the classical no-slip permeability model. Meanwhile is the oil transport capacity is significantly reduced in inorganic shale matrix. This work shows that the identification of higher TOC region and considering the nanoconfined effects are necessary from the concept of oil transport in shale matrix.

Hazard assessment of oil spills along the main shipping lane in the Red Sea

This study investigates the risk from oil spills along the main shipping lane in the Red Sea based upon oil spill model trajectories forced by the outputs of validated high resolution regional met-ocean data. Following the intra-annual variations in the met-ocean conditions, the results are presented by classifying the basin into three regions: northern, central and southern Red Sea. The maximum distance traveled by the slick is presented for 1, 2, 5, 10, 14 and 20 days after the commencement of a spill. Different measures of hazard assessment in terms of the concentration of beached oil alongside the corresponding probability maps are also analyzed. The volume fractions of beached, dispersed and evaporated oil, 20 days after the commencement of a spill are quantified. The Red Sea general circulation is characterized by rich mesoscale eddies, which appear to be the most prevailing dynamics in oil transport in the basin. Two case events are analyzed to closely examine the effects of the mesoscale circulations on the fate of spilled oil. The results of this study provide a comprehensive assessment of oil spill hazards in the Red Sea, stemming its main shipping lane and identifies the areas at high risk that require timely mitigation strategies.

Laser pattern-induced unidirectional lubricant flow for lubrication track replenishment

Effective oil replenishment to the lubrication track of a running bearing is crucial to its sustainable operation. Reliable practical solutions are rare despite numerous theoretical studies were conducted in the last few decades. This paper proposes the use of surface effect, wettability gradient, to achieve the goal. This method is simple and can be nicely implemented using femtosecond laser ablation. A periodic comb-tooth-shaped pattern with anisotropic wetting capability is devised and its effect on the anisotropic spreading behaviour of an oil droplet is studied. Results show that the comb-tooth-shaped pattern enables the rearrangement of oil distribution, thereby escalating oil replenishment to the lubrication track. The effect is due to the unbalanced interfacial force created by the surface pattern. The influence of the shape and the pitch of teeth, which are the two governing factors, on oil transport is also reported. The effects of the newly devised surface pattern on lubrication are experimentally evaluated under the conditions of limited lubricant supply. These results are promising, demonstrating the reduction in bearing friction and the increase in lubricating film thickness.

Exploring the Gains of Artificial Intelligence for Addressing the Menance of Pipeline Vandalization in Nigeria: A Review

Pipeline transport is a commonly utilised method for moving petroleum products from one location to another. It is considered as one of the safest, fastest, and most reliable options for transporting petroleum products. However, the use of pipelines for oil transport is associated with the risk of pipeline failure, a challenge caused by a range of factors including corrosion, ignition of natural gas, accidental damage during excavation work, lapses in maintenance as well as vandalization. Most prevalent in Nigeria is the menace of pipeline vandalization and oil thefts which is a critical factor responsible for most pipeline failures across the country. Varied efforts aimed at addressing the problem of vandalization and oil thefts in Nigeria have, over the years, yielded minimal benefits. Therefore, this review investigates the usefulness and suitability of artificial intelligence (AI) for securing Nigeria’s pipeline network. The review focuses on summarizing available evidence on the use of some relevant AI components such as Image Analytics, Convolutional Neural Network (CNN) as well as Edge-Based AI Solutions, for securing oil pipelines. Based on the findings of case studies and other primary research materials utilized in this review, this paper concludes that while there is need for further research on the subject, AI offers a promising and useful solution to Nigeria’s endemic challenge of pipeline vandalization and oil theft. This is as AI promotes early detection of illicit activities on pipelines and can relay signals to appropriate authorities on the need for urgent action. The use of AI in securing Nigeria’s vast pipeline network will not only minimize the economic losses caused by vandalization but will equally contribute towards mitigating the adverse environmental impact of oil pipeline vandalization in Nigeria.