Circular Re-refining of Spent Lubricants via Supercritical CO₂ Extraction: Process Optimization and Life-Cycle Assessment

Authors

  • Kiarash Zebarjad Master of Industrial Engineering - Engineering Management, Department of Industrial Engineering, College of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran https://orcid.org/0000-0003-2147-2093
  • Soniya Behzadinasab * PhD in Business Administration - Marketing Management, Department of Business Administration, College of Management and Accounting, Roudehen Branch, Islamic Azad University, Roudehen, Iran https://orcid.org/0000-0002-3087-1208

https://doi.org/10.22105/raise.vi.85

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 assessment

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Published

2026-09-13

How to Cite

Zebarjad, K., & Behzadinasab, S. (2026). Circular Re-refining of Spent Lubricants via Supercritical CO₂ Extraction: Process Optimization and Life-Cycle Assessment. Research Annals of Industrial and Systems Engineering, 3(3), 192-211. https://doi.org/10.22105/raise.vi.85

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