Global plastic waste generation exceeds 460 million tons per year, while recycling rates remain below 10%. Innovative solutions are needed for recycling plastic waste into valuable products. This study employs experimental data and two methods to evaluate economic analysis of fluid catalytic cracking (FCC) of plastic-derived pyrolysis oil: physics-based and machine learning (ML) -based methods to model the effects of reactions in the FCC unit. FCC offers many advantages compared to hydrocracking: e. g. , it operates under lower pressure (implying lower equipment costs), eliminates the use of hydrogen, and runs with reduced operating costs. The physics-based model was used to determine reaction stoichiometries, ensuring chemical plausibility, while a Decision Tree regressor was employed to predict product yields based on the cycle number and cycle time, a proxy for catalyst deactivation that captures time-dependent experimental observations. Both models were integrated surrogate models within a full process model in BioSTEAM to conduct a technoeconomic assessment (TEA) and a life-cycle assessment (LCA) with uncertainty analysis. The research also highlights the production of valuable chemicals, specifically light olefins such as ethylene and propylene and aromatic compounds such as benzene, toluene, and xylenes (BTX). The ML-based framework estimated a minimum selling price (MSP) for naphtha of approximately 1. 38/kg under input uncertainties, while the physics-based approach estimated an MSP of 1. 76/kg. These results are within 22% of each other, which is within the expected range of ± 30% for the preliminary TEA estimates. These findings suggest that ML-based approaches can be an effective substitute for physics-based models when there is limited understanding of the underlying chemical mechanisms. Sensitivity analysis captured the impacts of varying the catalyst cycle number and times. Cycle numbers between 1 and 4 and cycle times of up to 20 min resulted in MSPs ranging between 1 and 2 /kg. This variation captures the impacts of catalyst deactivation in FCC systems, supporting the need for optimizing the catalyst performance.
Dubey et al. (Thu,) studied this question.