A new approach to identify and diagnose the quality of extensive and multivariate data is presented, using the gage repeatability and reproducibility (GR&R) study through the weighting of rotated factor scores. The proposal uses axis rotation to improve the explanation and interpretations of latent information, providing a statistically appropriate alternative when dealing with two or more correlated data sets. To analyze data with a significant variance-covariance structure, factor analysis (FA) is applied for calculating the eigenvalues and extracting of the rotated scores. Once obtained, these scores are then weighted with their respective eigenvalue for each factor. This procedure results in a single response vector, which is capable of properly interpreting all of the quality responses analyzed. To illustrate an application of the method, a real data set from a resistance spot welding process is selected, and two different types of rotation are compared. The proposed method provided an output that contemplated all of the significant variability of the data in a unique and significant way. In addition, the method enabled a reduction in the data dimensionality, thus minimizing the time for analysis and computational effort.

The presence of oil films in the ocean poses a significant environmental challenge for companies engaged in primary oil processing on offshore platforms. During crude oil treatment, residual water containing oils and greases (TOG) is discharged back into the ocean, potentially leading to the formation of oil films. The interaction between TOG levels and meteoceanographic variables — including wind direction (WD), wind speed (WS), current direction (CD), current speed (CS), wind wave direction (WWD), and peak period (PP) — influences the formation and satellite detection of these films. This study investigates the impact of these factors on the occurrence, detection, and extent of oil films using various statistical classifiers. Key findings reveal that Random Forest outperformed other classifiers, achieving an area under the ROC curve of 0.93. The combined use of classifiers, multivariate techniques, desirability analysis, and Design of Experiments (DOE) proved highly effective. Higher values of WS, WD, and CS were associated with a lower likelihood of oil film occurrence and detection, whereas higher TOG, PP, WWD, and CD values increased this probability. CS and TOG positively contributed to the extent of oil films, while high WS values reduced it. These results provide a robust decision-support framework for monitoring and mitigating the environmental impacts of offshore oil processing operations.

The presence of oil slicks in the ocean presents significant environmental and regulatory challenges for offshore oil processing operations. During primary oil–water separation, produced water is discharged into the ocean, carrying residual oil, which is measured using the total oil and grease (TOG) method. The formation and spread of oil slicks are influenced by metoceanographic variables, including wind direction (WD), wind speed (WS), current direction (CD), current speed (CS), wind wave direction (WWD), and peak period (PP). In Brazil, regulatory limits impose sanctions on companies when oil slicks exceed 500 m in length, making accurate prediction of their occurrence and extent crucial for offshore operators. This study follows three main stages. First, the performance of five machine learning classification algorithms is evaluated, selecting the most efficient method based on performance metrics from a Brazilian company’s oil slick database. Second, the best-performing model is used to analyze the influence of metoceanographic variables and TOG levels on oil slick occurrence and detection probability. Finally, the third stage examines the extent of detected oil slicks to identify key contributing factors. The prediction results enhance decision-support frameworks, improving monitoring and mitigation strategies for offshore oil discharges.