Utilization of Oil Properties to Develop a Spreading Rate Regression Model for Nigerian Crude Oil

Abstract

The target of this study is to develop a spreading rate regression model capable of predicting rate of spread of Nigerian crude oil spills on water. The major factors responsible for spreading rate of crude oil on water were considered, namely surface tension, viscosity, and specific gravity/American Petroleum Institute degree (0 API), all at specified temperature values. The surface tension, viscosity and density parameters were interactively measured under controlled factorial analysis. The spreading rate of each crude oil was determined by artificially spilling them on laboratory calm/stagnant water in a rectangular tank and their averages were also computed. These averages were used to develop a regression model equation for spreading rate. The model developed indicated that an average spreading rate was 3.3528 cm/s at 37.5˚C and the predictive regression model is evaluated with the interactions of specific gravity, viscosity and surface tension. It is convenient to state that the model will predict the spread rate of crude oils which possess imputed physicochemical properties having pour point averaged 15.5˚C on calm seawater.

Physical, Mechanical and Antibacterial Properties of Biodegradable Bioplastics from Polylactic Acid and Corncob Fibers with Added Nano Titanium Dioxide

“…This toughness may be evaluated by the physico-tensile properties of bioplastic such as tensile strength. Tensile strength is the maximum force that a material specimen can resist under tension [38]. Based on Figure 1, on one hand, the tensile strength was increased significantly with increased PLA as the 100 %PLA: 0 %CF bioplastic exhibited the highest tensile strength (60 MPa).…”

Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

“…However, the effects of the IFT and viscosity are generally not negligible, as observed from the results for Locations 1 and 2. Empirical findings have suggested that more-viscous oils spread less than less-viscous oils (Olugbenga et al, 2020), explaining the observed sensitivity of the slick deformation to the oil viscosity. Moreover, the IFT directly influences surface oil spreading, ultimately affecting the shape of the oil slick and how it stretches under the action of surface currents (Winoto et al, 2014;Speight, 2020).…”

Modeling of the transport and diffusion phenomena for petroleum aromatic hydrocarbons from spilled crude oil in a stagnant mangrove water body

“…Over the years, there have been focused efforts on in-depth reviews as well as mathematical model development that have now improved the understanding of the transport and trajectory forecasting of oil spill in water. These intellectual strides have enhanced the proper evaluation of oil spill management practice as well as the identification of the existing knowledge gaps (Chao et al, 2001;French-McCay, 2004;Garcia-Martinez & Flores-Tover, 1999;Giwa & Jimoh, 2010;Jimoh & Alhassan, 2006;Keramea et al, 2021;Kvočka et al, 2021;Li et al, 2018;Lonin, 1999;Njobuenwu, 2014;Njobuenwu & Abowei, 2008;Olugbenga et al, 2020;Susu et al, 1997;Tkalich, 2006;Wang et al, 2005). However, the existing models did not cover oil spill pollution in all types of marine environment particularly stagnant water bodies.…”