Articles

The Effect of Alpha Olefin Sulphonate (AOS) Surfactant Injection on Sandstone Rock on Increasing Oil Recovery with Variations in Salinity, Concentration, and Temperatures: A Laboratory Study

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In this study, Alpha Olefin Sulphonate (AOS) surfactant was injected. This laboratory test aims to verify the correlation between a surfactant solution’s low IFT value and its ability to yield a high oil recovery value. The salinity used in this research was 5000 ppm and 15000 ppm. Then it was mixed with AOS surfactant at concentrations of 0.5%, 1%, and 2%. At the same time, the temperatures used are 30 °C and 60 °C. After testing, it was found that a solution with a salinity of 15,000 ppm and a surfactant concentration of 2% had the lowest IFT value and was proven to have a total RF of 73%.

Advanced TRST01 ESG Scoring Model with Beta Based Financial Metrics and Machine Learning Techniques

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In the current corporate world, assessing a company’s sustainability performance is very important for investors, stakeholders, and policymakers. The TRST01’s ESG (Environmental. Social and Governance) Scoring Model introduces an innovative approach integrating beta-based financial metrics with advanced machine learning techniques to comprehensively evaluate ESG credentials. This study demonstrates the development and application of the TRST01’s ESG scoring model, which leverages data from the most reputable sources such as MSCI and S&P Global to ensure its reliability and accuracy. The model’s unique methodology involves calculating country-specific beta values to normalize carbon emission data, thereby providing a standardized metric for meaningful comparisons across countries. Further, ESG scores are adjusted using both country and company beta values to reflect specific risk exposures, enhancing the precision and relevance of the assessments. The model ensures robust input data quality, by taking Market capital, Scope 1, Scope 2, industry wise data and beta values as predictors through extensive data preprocessing and encoding categorical variables for top 1000 listed companies. A comparative analysis of Traditional model such as Simple Linear Regression (SLR) and multiple Machine Learning (ML) models, including Gradient Boosting (GB), Support Vector Regression (SVR), and Random Forest (RF), demonstrates that the Gradient Boosting model achieves superior performance with minimal overfitting and consistent prediction accuracy. The study employs a comprehensive evaluation framework using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, supplemented by detailed visualizations of actual vs. predicted values, residuals, and error distributions. This research underscores the significance of incorporating advanced financial metrics and machine learning techniques in ESG assessments, providing a reliable, accurate, and holistic framework for understanding corporate sustainability. The TRST01 ESG Scoring Model sets a new standard in sustainability evaluation, offering valuable insights for stakeholders committed to integrating sustainability into core business strategies.