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

Woo Hyoung Lee1, Cheol Young Park1, Daniela Diaz1

  • 1Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States.

Water Research
|August 21, 2022
PubMed
Summary
This summary is machine-generated.

This study developed image analysis and machine learning models to predict bilgewater emulsion stability. Cleaner type, salinity, and suspended solids significantly impact oil separation, enabling better wastewater management.

Keywords:
BilgewaterCoalescenceEmulsion stabilityImage processingMachine learningOil-in-water emulsions

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Area of Science:

  • Environmental Science
  • Chemical Engineering
  • Marine Science

Background:

  • Bilgewater is complex oily wastewater from ships, requiring effective management for regulatory compliance.
  • Understanding bilgewater emulsion stability is crucial for developing efficient treatment strategies.
  • Existing methods for assessing oil separation can be time-consuming and resource-intensive.

Purpose of the Study:

  • To develop and validate an image-based method for predicting bilgewater emulsion stability.
  • To identify key factors influencing oil separation in bilgewater.
  • To create machine learning models for classifying and predicting bilgewater emulsion stability.

Main Methods:

  • Development of 360 bilgewater emulsion samples using Navy cleaner data and prior studies.
  • Image analysis to determine Oil Value (OV), validated against gravimetric Oil Separation (OS) measurements.
  • Statistical analysis (ANOVA) to assess the impact of variables (Cleaner, Salinity, Suspended Solids, pH, Temperature) on OV.
  • Application of machine learning algorithms (Random Forest, Decision Tree) for classification and regression models.

Main Results:

  • Oil Value (OV) from image analysis showed strong agreement with experimental Oil Separation (OS) (%).
  • Cleaner type, Salinity, and Suspended Solids (SS) were statistically significant factors (p < 0.05) affecting OV.
  • Machine learning models demonstrated high accuracy: Random Forest (RF) classifier (F1-score 0.8224) and Decision Tree (DT) regressor (MAE 0.1611).
  • Turbidity also proved a good predictor using RF models (MAE 0.0559, F1-score 0.9338).
  • Variable importance analysis highlighted Salinity, SS, and Temperature as most impactful.

Conclusions:

  • Image-based OV measurement is a viable and simple method for predicting bilgewater emulsion stability.
  • Machine learning models offer accurate and efficient tools for assessing bilgewater quality and optimizing treatment.
  • This study pioneers the use of image processing and ML for marine wastewater assessment, aiding regulatory compliance.