Abstract

In situ burning (ISB) is an OSR (Oil Spill Response) technique generally not accepted as a “standard” response by most regulators. Main reluctance generally comes from the occurrence of large and dark smokes plumes as well as possible heavy burn residues likely to sink and reach the seafloor. However, ISB has been widely used during the Macondo oil spill in 2010 and highlighted as a promising operational technique. Additionally, standard OSR modeling tools do not consider ISB, as there is a lack of knowledge in the capacity to predict accurately the physico-chemical characteristics, fate and ecotoxicity, of the burn products from large varieties of crude oils. In order to provide dispassionate arguments, we have conducted a study to gain knowledge and try to develop predictive capabilities to infer composition and fate of burn products from characteristics of starting crude oils. The study comprises both large literature review and experimental work where six crude oils of different types (along with corresponding weathered oils) were burnt, and all products quantified and analyzed (physico-chemistry), at both laboratory scale (burning bench) and pilot scale (fire platform). Additionally, ecotoxicity testing on Vibrio fischeri bacteria and Phaeodactylum tricornutum algae were conducted on crude and weathered oils, burn residues, water and soot. The fate of burn residues was also investigated through dispersibility, emulsification and biodegradability testing. The experimental work led to a very large set of data which was subject to multivariate regression statistical treatments to build predictive models. Main data investigated were burn efficiency, density, viscosity, SARA (Saturates, Aromatics, Resins, Asphaltenes) composition, n-alkanes and PAHs (polycyclic aromatic hydrocarbons) distributions in burn residues, gas emissions composition, soot' PAHs distribution in the smokes, and ecotoxicity data of burn residues, soot and water. The paper describes the main results of this study, with promising outcomes to develop predictive ISB modeling and help addressing objectively the relative impact of ISB within the SIMA process in comparison with other cleaning responses.

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