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

Yi Cheng; Qiong Pan; Jie Li; Nan Zhang; Jiawei Wang; Ningbo Gao

doi:10.1007/s11705-024-2468-3

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

Yi Cheng
, Qiong Pan
, Jie Li
, Nan Zhang
, Jiawei Wang^*
, Ningbo Gao

^*Corresponding author for this work

Xi'an Jiaotong University

Research output: Contribution to journal › Article › peer-review

3 Citations (SciVal)

5 Downloads (Pure)

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.

Original language	English
Article number	117
Number of pages	13
Journal	Frontiers of Chemical Science and Engineering
Volume	18
Issue number	10
Early online date	22 Jul 2024
DOIs	https://doi.org/10.1007/s11705-024-2468-3
Publication status	Published - Oct 2024

Bibliographical note

Copyright © The Author(s) 2024. This article is published with open access at link.springer.com and journal.hep.com.cn. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

Funding

The authors are grateful for financial support from the Marie Sklodowska Curie Actions Fellowships by The European Research Executive Agency, Belguim (Grant Nos. H2020-MSCA-IF-2020 and 101025906). More importantly, Dr. Yi Cheng acknowledge Dr. Fanhua Kong from the Petrochemical Research Institute of PetroChina Co., Ltd., China, who proposed the initial assumption of the system and years of guidance in the industrial syngas research area.

Keywords

marine plastics
hydrothermal
methanol
machine learning
techno-economic assessment

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10.1007/s11705-024-2468-3Licence: CC BY 4.0

Y Cheng et al_Machine learning facilitated modeling of plastics hydrothermal pretreatment_on-ship marine litter-to-methanol plant
Copyright © The Author(s) 2024. This article is published with open access at link.springer.com and journal.hep.com.cn. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
Final published version, 2.02 MBLicence: CC BY 4.0

Cite this

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title = "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. ",

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ER -

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

Abstract

Bibliographical note

Funding

Keywords

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