Machine learning facilitated the modeling of plastics hydrothermal pretreatment toward constructing an on-ship marine litter-to-methanol plant

Abstract

An onboard facility shows promise in efficiently converting floating plastics into valuable products, such as methanol, negating the need for regional transport and land-based treatment. Gasification presents an effective means of processing plastics, requiring their transformation into gasification-compatible feedstock, such as hydrochar. This study explores hydrochar composition modeling, utilizing advanced algorithms and rigorous analyses to unravel the intricacies of elemental composition ratios, identify influential factors, and optimize hydrochar production processes. The investigation begins with decision tree modeling, which successfully captures relationships but encounters overfitting challenges. Nevertheless, the decision tree vote analysis, particularly for the H/C ratio, yielding an impressive R² of 0.9376. Moreover, the research delves into the economic feasibility of the marine plastics-to-methanol process. Varying payback periods, driven by fluctuating methanol prices observed over a decade (ranging from 3.3 to 7 yr for hydrochar production plants), are revealed. Onboard factories emerge as resilient solutions, capitalizing on marine natural gas resources while striving for near-net-zero emissions. This comprehensive study advances our understanding of hydrochar composition and offers insights into the economic potential of environmentally sustainable marine plastics-to-methanol processes.

Graphical abstract

Keywords

marine plastics / hydrothermal / methanol / machine learning / techno-economic assessment

RIGHTS & PERMISSIONS

The Author(s) 2024. This article is published with open access at link.springer.com and journal.hep.com.cn