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

doi:10.1007/s11705-024-2468-3

2024, Vol. 18

Issue (10) : 117 https://doi.org/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^1,², Qiong Pan³, Jie Li³, Nan Zhang³, Yang Yang⁴, Jiawei Wang^1,²(

), Ningbo Gao⁵

¹. Department of Chemical Engineering and Applied Chemistry, Aston University, Birmingham B4 7ET, UK
². Energy and Bioproducts Research Institute, Aston University, Birmingham B4 7ET, UK
³. Centre for Process Integration, Department of Chemical Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, 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

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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.

Keywords marine plastics hydrothermal methanol machine learning techno-economic assessment

Corresponding Author(s): Jiawei Wang

Just Accepted Date: 27 June 2024 Issue Date: 06 August 2024

Cite this article:

Yi Cheng,Qiong Pan,Jie Li, et al. Machine learning facilitated the modeling of plastics hydrothermal pretreatment toward constructing an on-ship marine litter-to-methanol plant[J]. Front. Chem. Sci. Eng., 2024, 18(10): 117.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-024-2468-3
https://academic.hep.com.cn/fcse/EN/Y2024/V18/I10/117

Fig.1 (a) An overview of the marine debris conversion assumptions, (b) a summary of statistical data for input and response data sets, and (c) a heatmap illustrating data correlations.

Fig.2 Regression plots for training and testing using DT, NN, and SVM algorithms for (a) hydrochar recovery modeling, (b) importance estimates for recovery yield predictors, and (c) cross-validation fitness analysis.

Fig.3 (a) Regression plots for training and testing using the DT algorithm for hydrochar composition modeling, (b) importance estimates for predictors of each hydrochar composition derived from the DT algorithm.

Fig.4 Regression plots illustrating training and testing results for hydrochar composition modeling using (a) the NN algorithm and (b) SVM algorithm.

Fig.5 Regression plots illustrating (a) training and test data with the NN algorithm for hydrochar composition modeling using featured factors sets, (b) cross-validation results for hydrochar compositions with full factors set, and (c) a summary of R² values.

Tab.1 The economic estimations of waste to methanol factories

Fig.6 The methanol price tendency in the past decade and estimated payback years.

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