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

作　　者：Yi Cheng Qiong Pan Jie Li Nan Zhang Yang Yang Jiawei Wang Ningbo Gao 

机构地区：[1]Department of Chemical Engineering and Applied Chemistry,Aston University,Birmingham B47ET,UK [2]Energy and Bioproducts Research Institute,Aston University,Birmingham B47ET,UK [3]Centre for Process Integration,Department of Chemical Engineering,School of Engineering,The University of Manchester,Manchester M139PL,UK [4]State Key Laboratory of Coal Combustion,Huazhong University of Science and Technology,Wuhan 430074,China [5]School of Energy and Power Engineering,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Frontiers of Chemical Science and Engineering》2024年第10期105-117,共13页化学科学与工程前沿（英文版）

基　　金：financial support from the Marie Skłodowska Curie Actions Fellowships by The European Research Executive Agency,Belguim(Grant Nos.H2020-MSCA-IF-2020 and 101025906)。

摘　　要：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.

关 键 词：marine plastics HYDROTHERMAL methanol machine learning technoeconomic assessment 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]