Automated Metabolic Flux Analysis and Optimization for Circular Economy Implementation in Petrochemical Feedstock Recycling (A Deep Learning Approach)

**Abstract:** This paper introduces a novel, fully automated framework for Metabolic Flux Analysis (MFA) and optimization, leveraging deep learning techniques to accelerate and improve the accuracy of biofuel and chemical production from recycled petrochemical feedstock. Current MFA processes are labor-intensive and require significant manual adjustments, hindering the rapid deployment of circular economy initiatives. Our system, termed "MetaFlow AI," addresses this limitation by predicting metabolic fluxes within microbial consortia consuming mixed hydrocarbon waste streams with unprecedented speed and accuracy, dramatically reducing operational costs and maximizing product yield. This technology is commercially viable within 3 years, with the potential to significantly reduce petrochemical waste volume and enable more sustainable chemical production.