Machine Learning Models for Predicting Surfactant-Enhanced Oil Removal from Contaminated Soil

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The remediation of oil-contaminated soil is a critical environmental challenge, and surfactant-enhanced oil recovery (SEOR) has emerged as an effective method for improving hydrocarbon removal. Recent advances in machine learning (ML) have enabled more accurate predictions of SEOR efficiency, allowing researchers to optimize surfactant type, concentration, and soil conditions for maximum oil extraction. ML models, including decision trees, random forests, support vector machines, and neural networks, analyze complex interactions between soil properties, contaminant characteristics, and surfactant behavior. By leveraging historical data and experimental results, these models can forecast removal rates under various scenarios, reducing the need for costly and time-consuming laboratory trials. The integration of ML in SEOR not only enhances remediation performance but also contributes to sustainable soil management by minimizing chemical usage and environmental impact.

Hashtags:
#MachineLearning #SoilRemediation #OilContamination #SurfactantEnhancedRecovery #EnvironmentalEngineering #DataDrivenSolutions #PollutionCleanup #SustainableRemediation #SoilScience #AIinEnvironment




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