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

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.

Identification and characterization of bilgewater emulsions

Literature on bilgewater focuses on empirically determined treatment methods and lacks specific information on emulsion characteristics. Therefore, this review discusses potential emulsion stabilization mechanisms that occur in bilgewater and evaluates common approaches to study their behavior. Current knowledge on emulsion formation, stabilization, and destabilization is outlined to provide researchers and bilgewater treatment operators with the knowledge needed to determine emulsion prevention and treatment strategies. Furthermore, a broad assessment of bilgewater emulsion characterization techniques, from general water quality analysis to advanced droplet stability characterization methods are discussed in detail. Lastly, a survey of typical bilgewater characteristics and information on standard synthetic bilgewater mixtures used in the testing of oil pollution abatement equipment are presented. Overall, the goal of this article is to provide a better understanding of physical and thermodynamic properties of emulsions to help improve bilgewater treatment and management.

In Situ Characterization of Oil-in-Water Emulsions Stabilized by Surfactant and Salt Using Microsensors

Chemically stabilized emulsions are difficult to break because of micelle stability. Many physical and chemical processes have been used for emulsion breaking/separation; however, most operational parameters are based on empirical data and bulk analysis. A multiscale understanding of emulsions is required before these processes can advance further. This study utilized needle-type microsensors and confocal laser scanning microscopy (CLSM) for characterizing simulated bilge water emulsions with different types of surfactants (Triton X-100 and sodium dodecyl sulfate [SDS]) under various NaCl concentrations at microscale. Using microsensors, a diffusion process was clearly visualized across the oil/water interface which appears to be related to emulsion formation kinetics and mass transfer. While emulsion stability decreased with NaCl concentrations, SDS (anionic surfactant) is more likely to form emulsion as salinity increases, requiring more salinity to coalesce SDS emulsions than Triton X-100 (nonionic surfactant) emulsions. Triton X-100 emulsions showed the potential to exhibit particle stabilized emulsions with NaCl concentration below 10^-2.5 M. The research demonstrated that the use of nonionic surfactant allows better oil-in-water separation than anionic surfactant. Significant pH changes of emulsions from unknown additives have implications when operating pH-sensitive emulsion breaking/separation processes (e.g., electrocoagulation).

Effect of prolotherapy on cellular proliferation and collagen deposition in MC3T3-E1 and patellar tendon fibroblast populations

Proliferative therapy, or prolotherapy, is a treatment for damaged connective tissues involving the injection of a solution (proliferant) which causes local cell death and triggers the body's wound healing cascade. Physicians vary in their use of this technique; it is employed for ligaments but has also been investigated for tissues such as cartilage. Physicians also vary in treatment regiments using different dosses of the proliferant. This study evaluates several proliferant dosages develop an optimal dosage that maximizes cell and collagen regeneration. This study also looks at cell and collagen regeneration in response to proliferant exposure outside of the healing cascade. MC3T3-E1 cells and patellar tendon fibroblasts were exposed to various amounts of the proliferant P2G and monitored over several weeks. The results showed an inverse relationship between proliferant concentration and cell viability and collagen production in MC3T3-E1 cells. Following exposure, cell populations experienced an initial decrease in cell number followed by increased proliferation. Trichrome staining over 4 weeks showed an increase in collagen production after proliferant exposure. However the cell numbers and amounts of collagen from the treated groups never surpassed those of the untreated groups, although collagen production was comparable in fibroblasts. The results of this basic study show that there is an effective proliferant dosage and point to a local response to the proliferant that increases cell proliferation and collagen production separate from the wound healing cascade. This local response may not be adequate for complete healing and assistance from the body's healing cascade may be required.