Improved bilgewater treatment is necessary because of technological challenges often faced when meeting existing ocean discharge criteria regulations. Since bilgewater is a waste mixture including hydraulic oils, cleaning agents, and seawater, the success of its management is largely attributed to understanding oil-in-water emulsion characterizations. However, the study of bilgewater emulsions is complex due to the multivariate nature of real-shipboard samples. The objective of this study is to develop the relationship between parameters commonly found in bilgewater and emulsion stability. This work is a continuation of a 3-year research project supported by the Strategic Environmental Research and Development Program (SERDP). Previous research (Year 1–2) identified the governing parameters (salinity, suspended solids, pH, and temperature) for oil-in-water emulsion formation and separation using different model surfactants and commercial cleaners. An emulsion stability model with random forest regression and classification algorithms was developed using data from the previous study. This study focused on expanding the database of emulsion stability levels typically encountered in bilgewater. Four surfactants and cleaners were downselected among a list of commonly used products found in bilgewater on Armed Forces vessels. The effects of determining a surfactant's critical micelle concentration in the presence of a representative bilge oil mix, as well as the contribution of surfactant concentration and homogenization intensity on emulsion stability was investigated. Additionally, the effect of a range of environmental parameters (salinity and suspended solids) was evaluated. The work herein added to the range of available emulsion stability model input characterizations. Via nondestructive analytical methods and statistical evaluation, it was found that surfactant concentration, homogenization intensity, and salinity had a significant impact on emulsion stability. However, the newly added data representing more realistic conditions did not contribute to the emulsion stability prediction model, while adding extended interval ranges for each factor did improve the accuracy of predictions.


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Graduation Date





Lee, Woo Hyoung


Master of Science in Environmental Engineering (M.S.Env.E.)


College of Engineering and Computer Science


Civil, Environmental and Construction Engineering

Degree Program

Environmental Engineering


CFE0009313; DP0026917





Release Date

June 2022

Length of Campus-only Access


Access Status

Masters Thesis (Open Access)