Predicting bilgewater emulsion stability by oil separation using image processing and machine learning

Woo Hyoung Lee; Cheol Young Park; Daniela Diaz; Kelsey L Rodriguez; Jongik Chung; Jared Church; Marjorie R Willner; Jeffrey G Lundin; Danielle M Paynter

doi:10.1016/j.watres.2022.118977

Predicting bilgewater emulsion stability by oil separation using image processing and machine learning

Water Res. 2022 Sep 1:223:118977. doi: 10.1016/j.watres.2022.118977. Epub 2022 Aug 14.

Authors

Woo Hyoung Lee¹, Cheol Young Park², Daniela Diaz², Kelsey L Rodriguez², Jongik Chung³, Jared Church⁴, Marjorie R Willner⁴, Jeffrey G Lundin⁵, Danielle M Paynter⁴

Affiliations

¹ Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States. Electronic address: woohyoung.lee@ucf.edu.
² Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States.
³ Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816-2370, United States.
⁴ Environmental Engineering, Science, and Technology Branch, Naval Surface Warfare Center, Carderock Division, West Bethesda, MD, United States.
⁵ Chemistry Division, United States Naval Research Laboratory, Washington, D.C., United States.

PMID: 35988334
DOI: 10.1016/j.watres.2022.118977

Abstract

Bilgewater is a shipboard multi-component oily wastewater, combining numerous wastewater sources. A better understanding of bilgewater emulsions is required for proper wastewater management to meet discharge regulations. In this study, we developed 360 emulsion samples based on commonly used Navy cleaner data and previous bilgewater composition studies. Oil value (OV) was obtained from image analysis of oil/creaming layer and validated by oil separation (OS) which was experimentally determined using a gravimetric method. OV (%) showed good agreement with OS (%), indicating that a simple image-based parameter can be used for emulsion stability prediction model development. An ANOVA analysis was conducted of the five variables (Cleaner, Salinity, Suspended Solids [SS], pH, and Temperature) that significantly impacted estimates of OV, finding that the Cleaner, Salinity, and SS variables were statistically significant (p < 0.05), while pH and Temperature were not. In general, most cleaners showed improved oil separation with salt additions. Novel machine learning (ML)-based predictive models of both classification and regression for bilgewater emulsion stability were then developed using OV. For classification, the random forest (RF) classifiers achieved the most accurate prediction with F1-score of 0.8224, while in regression-based models the decision tree (DT) regressor showed the highest prediction of emulsion stability with the average mean absolute error (MAE) of 0.1611. Turbidity also showed a good emulsion prediction with RF regressor (MAE of 0.0559) and RF classifier (F1-score of 0.9338). One predictor variable removal test showed that Salinity, SS, and Temperature are the most impactful variables in the developed models. This is the first study to use image processing and machine learning for the prediction of oil separation for the application of bilgewater assessment within the marine sector.

Keywords: Bilgewater; Coalescence; Emulsion stability; Image processing; Machine learning; Oil-in-water emulsions.

MeSH terms

Emulsions / chemistry
Machine Learning
Oils*
Temperature
Wastewater*

Substances

Emulsions
Oils
Waste Water