Data-driven Analysis and Experimental Studies to Optimize Packaging Operations in Petroleum Industries

Flushing operations in multiproduct lubricant oil pipelines are a critical determinant of product purity, operational efficiency, and economic performance, yet traditional approaches often rely on operator experience and trial-and-error, leading to excessive waste and frequent cross-contamination. This research presents an integrated framework combining data-driven analysis, experimental validation, and process optimization to address these challenges and optimize packaging operations in petroleum industries. Industrial data capturing both successful and failed flushes were analyzed using a suite of candidate machine learning (ML) classification models including ensemble methods, kernel-based models, and deep learning architectures to accurately classify flushing outcomes and identify the key factors driving successful flush. To mitigate the significant class imbalance between successful and unsuccessful flushes, Synthetic Minority Oversampling Technique (SMOTE) was employed, ensuring robust model performance. SHAP analysis revealed that flushing success is governed by complex, nonlinear interactions among lubricant type, viscosity contrasts, pipeline flow dynamics, flushing volume, and ambient temperature, emphasizing that no single factor dominates the process. Insights from this analysis informed the design of a bench-scale experimental rig that replicated industrial pipeline geometry and hydrodynamics, enabling systematic evaluation of improved drainage procedures, air-assisted flushing, and flow behavior. These results guided the development of a novel standardized and optimized flushing protocol, which, when implemented in a production line, reduced the failure rate from 11% to 3.6%, minimized flush volumes, and delivered significant cost savings. This study highlights the synergy between data-driven insights, experimental validation, and process optimization in achieving high-impact outcomes that are resilient, scalable, and sustainable. The framework not only enhances operational reliability in petroleum packaging operations but also provides a generalizable methodology applicable across process industries seeking to improve contamination control, efficiency, and economic performance.