Innovations in plastic remediation: Catalytic degradation and machine learning for sustainable solutions

Abstract

Plastic pollution is an extreme environmental threat, necessitating novel restoration solutions. The present investigation investigates the integration of machine learning (ML) techniques with catalytic degradation processes to improve plastic waste management. Catalytic degradation is emphasized for its efficiency and selectivity, while several machine learning techniques are assessed for their capacity to enhance these processes. The review goes into ML applications for forecasting catalyst performance, determining appropriate reaction conditions, and refining catalyst design to improve overall process performance. Briefing about the reinforcement, supervised, and unsupervised learning algorithms that handle all complex data and parameters is explained. A techno-economic study is provided, evaluating these ML-driven system's performance, affordability, and environmental sustainability. The paper reviews how the novel method integrating ML with catalytic degradation for plastic cleanup might alter the process, providing new insights into scalable and sustainable solutions. This review emphasizes the usefulness of these modern strategies in tackling the urgent problem of plastic pollution by offering a comprehensive examination.