Circular Re-refining of Spent Lubricants via Supercritical CO₂ Extraction: Process Optimization and Life-Cycle Assessment
Abstract
The growing volume of spent industrial lubricants from petrochemical refineries poses environmental and regulatory challenges, with conventional re-refining methods such as vacuum distillation and hydrotreating being energy-intensive and producing lower-quality Group I base oils. This study proposes a circular re-refining framework using supercritical carbon dioxide (scCO₂) extraction integrated with machine learning (ML) optimization to recover high-quality Group II base oils from spent lubricants while minimizing environmental impact. The methodology combines scCO₂ extraction at optimized conditions with a feedforward neural network model to dynamically adjust extraction parameters, maximizing yield and quality. A life-cycle assessment (LCA) following ISO 14040 guidelines was conducted to evaluate energy efficiency, carbon footprint, and economic viability. Experimental results demonstrate that the optimized scCO₂ process recovers 85.2 ± 2.1% of Group II base oils with properties closely matching virgin lubricants, including a kinematic viscosity of 5.8 cSt at 100°C, sulfur content of 0.028 wt%, and PAH levels below 10 ppm, significantly outperforming conventional methods. The Gaussian Process Regression model achieved 94% predictive accuracy, with pressure identified as the most influential parameter, contributing 65% to yield variance as determined by automatic relevance determination. The process reduces waste disposal by 70% and achieves a 60.5% reduction in global warming potential compared to vacuum distillation combined with hydrotreating, while human toxicity potential and resource depletion are reduced by 65.7% and 64.9%, respectively. The proposed framework addresses regulatory pressures for zero-waste systems, offering a scalable and environmentally benign solution for lubricant recycling with potential applications across the petrochemical sector, thereby contributing to the global transition toward circular economy practices.
Keywords:
Circular economy, Spent lubricants, Supercritical CO₂ extraction, Base oil recovery, Machine learning, Life-cycle assessmentReferences
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