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.
Article PDF
Avoid common mistakes on your manuscript.
References
Sheridan H, Johnson K, Capper A. Analysis of international, European and Scot’s law governing marine litter and integration of policy within regional marine plans. Ocean and Coastal Management, 2020, 187: 105119
Jambeck J R, Geyer R, Wilcox C, Siegler T R, Perryman M, Andrady A, Narayan R, Law K L. Plastic waste inputs from land into the ocean. Science, 2015, 347(6223): 768–771
Pabortsava K, Lampitt R S. High concentrations of plastic hidden beneath the surface of the Atlantic Ocean. Nature Communications, 2020, 11(1): 4073
van Giezen A, Wiegmans B. Spoilt-Ocean Cleanup: alternative logistics chains to accommodate plastic waste recycling: an economic evaluation. Transportation Research Interdisciplinary Perspectives, 2020, 5: 100115
Yao Z, Ma X. A new approach to transforming PVC waste into energy via combined hydrothermal carbonization and fast pyrolysis. Energy, 2017, 141: 1156–1165
Moore C J. Synthetic polymers in the marine environment: a rapidly increasing, long-term threat. Environmental Research, 2008, 108(2): 131–139
Martins J, Sobral P. Plastic marine debris on the Portuguese coastline: a matter of size? Marine Pollution Bulletin, 2011, 62(12): 2649–2653
Jung M R, Balazs G H, Work T M, Jones T T, Orski S V, Rodriguez C V, Beers K L, Brignac K C, Hyrenbach K D, Jensen B A, et al. Polymer identification of plastic debris ingested by pelagic-phase sea turtles in the central Pacific. Environmental Science & Technology, 2018, 52(20): 11535–11544
Hou Q, Zhen M, Qian H, Nie Y, Bai X, Xia T, Laiq Ur Rehman M, Li Q, Ju M. Upcycling and catalytic degradation of plastic wastes. Cell Reports. Physical Science, 2021, 2(8): 100514
Lopez G, Artetxe M, Amutio M, Alvarez J, Bilbao J, Olazar M. Recent advances in the gasification of waste plastics. A critical overview. Renewable & Sustainable Energy Reviews, 2018, 82: 576–596
Al-Salem S M, Antelava A, Constantinou A, Manos G, Dutta A. A review on thermal and catalytic pyrolysis of plastic solid waste. Journal of Environmental Management, 2017, 197: 177–198
Li J, Suvarna M, Pan L, Zhao Y, Wang X. A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification. Applied Energy, 2021, 304: 117674
Li J, Pan L, Suvarna M, Wang X. Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening. Chemical Engineering Journal, 2021, 426: 131285
Raikova S, Knowles T D J, Allen M J, Chuck C J. Co-liquefaction of macroalgae with common marine plastic pollutants. ACS Sustainable Chemistry & Engineering, 2019, 7(7): 6769–6781
Iñiguez M E, Conesa J A, Fullana A. Hydrothermal carbonization of marine plastic debris. Fuel, 2019, 257: 116033
Ge S, Shi Y, Xia C, Huang Z, Manzo M, Cai L, Ma H, Zhang S, Jiang J, Sonne C, et al. Progress in pyrolysis conversion of waste into value-added liquid pyro-oil, with focus on heating source and machine learning analysis. Energy Conversion and Management, 2021, 245: 114638
Cheng Y, Ekici E, Yildiz G, Yang Y, Coward B, Wang J. Applied machine learning for prediction of waste plastic pyrolysis towards valuable fuel and chemicals production. Journal of Analytical and Applied Pyrolysis, 2023, 169: 105857
Zhao S, Li J, Chen C, Yan B, Tao J, Chen G. Interpretable machine learning for predicting and evaluating hydrogen production via supercritical water gasification of biomass. Journal of Cleaner Production, 2021, 316: 128244
Katongtung T, Onsree T, Tippayawong N. Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes. Bioresource Technology, 2022, 344: 126278
Prifti K, Galeazzi A, Barbieri M, Manenti F. A Capex Opex Simultaneous Robust Optimizer: Process Simulation-based Generalized Framework for Reliable Economic Estimations. Montastruc L, Negny SBTCACE, eds. Computer Aided Process Engineering, 2022, 51: 1321–1326
Olah G A. Beyond oil and gas: the methanol economy. Angewandte Chemie International Edition, 2005, 44(18): 2636–2639
Al-Qadri A A, Ahmed U, Abdul Jameel A G, Zahid U, Usman M, Ahmad N. Simulation and modelling of hydrogen production from waste plastics: technoeconomic analysis. Polymers, 2022, 14(10): 2056
Besson P, Degboe J, Berge B, Chavagnac V, Fabre S, Berger G. Calcium, Na, K and Mg concentrations in seawater by inductively coupled plasma-atomic emission spectrometry: applications to IAPSO seawater reference material, hydrothermal fluids and synthetic seawater solutions. Geostandards and Geoanalytical Research, 2014, 38(3): 355–362
Millero F J, Feistel R, Wright D G, McDougall T J. The composition of standard seawater and the definition of the reference-composition salinity scale. Deep-sea Research. Part I, Oceanographic Research Papers, 2008, 55(1): 50–72
Lyman J, Fleming R H. Composition of sea water. Journal of Marine Research, 1940, 3(2): 134–146
Wensing M, Uhde E, Salthammer T. Plastics additives in the indoor environment—flame retardants and plasticizers. Science of the Total Environment, 2005, 339(1–3): 19–40
Iwaya T, Sasaki M, Goto M. Kinetic analysis for hydrothermal depolymerization of nylon 6. Polymer Degradation & Stability, 2006, 91(9): 1989–1995
Hastie J, Tibshirani R, Friedman J. The Elements of Statistical Learning. New York: Springer, 2009
Karabadji N E I, Seridi H, Bousetouane F, Dhifli W, Aridhi S. An evolutionary scheme for decision tree construction. Knowledge-Based Systems, 2017, 119: 166–177
Clare A, King R D. Knowledge discovery in multi-label phenotype data. In: European conference on principles of data mining and knowledge discovery. Berlin: Springer, 2001, 42–53
Boucheron S, Bousquet O, Lugosi G. Theory of classification: a survey of some recent advances. ESAIM: Probability and Statistics, 2005, 9: 323–375
Ascher S, Watson I, You S. Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renewable & Sustainable Energy Reviews, 2022, 155: 111902
Elmaz F, Yücel Ö, Mutlu A Y. Predictive modeling of biomass gasification with machine learning-based regression methods. Energy, 2020, 191: 116541
Mighani M, Shahi A, Antonioni G. Catalytic pyrolysis of plastic waste products: time series modeling using least square support vector machine and artificial neural network. In: the 16th International Conference on Sustainable Energy Technologies, 2017, available at WSSET
Ozbas E E, Aksu D, Ongen A, Aydin M A, Ozcan H K. Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms. International Journal of Hydrogen Energy, 2019, 44(32): 17260–17268
Fu C, Guo C Y, Lin X R, Liu C C, Lu C J. Tree decomposition for large-scale SVM problems. In: Proceedings of the International Conference on Technologies and Applications of Artificial Intelligence. IEEE, 2010, 11: 233–240
Cervantes J, García Lamont F, López-Chau A, Rodríguez Mazahua L, Sergio Ruíz J. Data selection based on decision tree for SVM classification on large data sets. Applied Soft Computing, 2015, 37: 787–798
Al-Qadri A A, Ahmed U, Jameel A G, Ahmad N, Zahid U, Zein S H, Naqvi S R. Process design and techno-economic analysis of dual hydrogen and methanol production from plastics using energy integrated system. International Journal of Hydrogen Energy, 2023, 48(29): 10797–10811
Prifti K, Galeazzi A, Manenti F. Design and simulation of a plastic waste to methanol process: yields and economics. Industrial & Engineering Chemistry Research, 2023, 62(12): 5083–5096
Acknowledgements
The authors are grateful for financial support from the Marie Skłodowska 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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests The authors declare that they have no competing interests.
Electronic supplementary material
11705_2024_2468_MOESM1_ESM.pdf
Machine learning facilitated the modeling of plastics hydrothermal pretreatment towards constructing an on-ship marine litter-to-methanol plant
Rights and permissions
Open Access 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/.
About this article
Cite this article
Cheng, Y., Pan, Q., Li, J. et al. Machine learning facilitated the modeling of plastics hydrothermal pretreatment toward constructing an on-ship marine litter-to-methanol plant. Front. Chem. Sci. Eng. 18, 117 (2024). https://doi.org/10.1007/s11705-024-2468-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11705-024-2468-3