Machine learning for real-time green carbon dioxide tracking in refinery processes

Liang Cao, Jianping Su, Jack Saddler, Yankai Cao, Yixiu Wang, Gary Lee, Lim C. Siang, Yi Luo, Robert Pinchuk, Jin Li and R. Bhushan Gopaluni

Renewable and Sustainable Energy Reviews, 2025, vol. 213, issue C

Abstract: The global increase in greenhouse gas emissions presents an urgent environmental challenge, demanding innovative strategies for emission reduction and a fundamental shift in energy consumption practices. Co-processing biogenic feedstocks, such as used cooking oils and biocrudes derived from forest and agricultural residues, within existing oil refineries has been demonstrated as a cost-effective, scalable approach to producing low-carbon fuels, quickly helping the oil refiners to mitigate carbon dioxide emissions, leveraging the existing infrastructures. Despite its potential, monitoring the ”green” CO2 emissions originating from biogenic feedstocks during co-processing remains challenging. The molecular structure of biogenic components becomes indistinguishable from fossil-based molecules, necessitating costly, labor-intensive, and time-consuming sample collection and testing procedures, often involving isotope carbon analysis. This work proposes a new approach by applying artificial intelligence to model green CO2 emissions in real-time. By analyzing over 102,000 samples of industrial data from a commercial FCC unit, a robust machine learning framework is developed to provide continuous, cost-effective, and accurate green CO2 monitoring. The methodology encompasses a comparative analysis of ten input analysis techniques and five regression models to model emissions, achieving an average error margin of just 2.66% compared to traditional laboratory measurements. This AI-driven approach offers refiners and policymakers a practical tool for assessing the environmental performance of biogenic feedstock co-processing, facilitating informed decision-making in renewable fuel production.

Keywords: Biogenic feedstocks; Co-processing; Machine learning; Real-time monitoring; Green Carbon Dioxide tracking (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.115417

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