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Synthetic bacterial consortium for degradation of plastic pyrolysis oil waste: experimental optimization and neural network modeling
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Scientists engineered a team of bacteria that can break down the toxic, hard-to-recycle oily leftovers from melting down mixed plastics, eliminating nearly all the harmful chemicals within just a week. This matters because plastic waste and its byproducts are piling up faster than we can safely process them, and microplastics are already showing up throughout the human body—so finding ways to actually break down plastic waste (rather than just burning or burying it) could reduce our overall chemical exposure over time. The researchers also used AI to make this cleanup process faster and cheaper to fine-tune, which could help this approach scale up for real-
Introduction The plastic crisis is omnipresent, ranging from plastic littering to microplastics found in every niche of the planet, including the human body. To achieve higher recycling quotas, especially for mixed plastic waste, pyrolysis is a viable option. However, plastic pyrolysis oil waste (PPOW) poses significant challenges for valorization due to its extreme molecular heterogeneity. Methods To reduce this molecular heterogeneity, we artificially compounded, monitored, and optimized a bacterial consortium capable of tolerating organic pollutants and utilizing them as carbon and energy sources. Additionally, a back-propagation (BP) neural network was applied to evaluate O 2 consumption as an indicator of microbial activity and to build a predictive model for process optimization. Results The primary constituents of the PPOW were alkanes and ε-caprolactam. Within 7 days, the bacterial community demonstrated remarkable efficacy, degrading alkanes (C10–C28) at rates of 71–100% and achieving complete removal of ε-caprolactam (8,032 mg/mL) and naphthalene. The AI model predicted O 2 consumption with high accuracy (R 2 > 0.99). Discussion This AI-driven approach significantly reduces experimental iterations and operational costs for future process optimization. These results are discussed in the context of a developing open circular plastic economy.
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