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

References

[1] 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

[2] 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

[3] Pabortsava K , Lampitt R S . High concentrations of plastic hidden beneath the surface of the Atlantic Ocean. Nature Communications, 2020, 11(1): 4073

[4] 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

[5] 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

[6] Moore C J . Synthetic polymers in the marine environment: a rapidly increasing, long-term threat. Environmental Research, 2008, 108(2): 131–139

[7] Martins J , Sobral P . Plastic marine debris on the Portuguese coastline: a matter of size?. Marine Pollution Bulletin, 2011, 62(12): 2649–2653

[8] 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 . . Polymer identification of plastic debris ingested by pelagic-phase sea turtles in the central Pacific. Environmental Science & Technology, 2018, 52(20): 11535–11544

[9] 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

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

[15] Iñiguez M E , Conesa J A , Fullana A . Hydrothermal carbonization of marine plastic debris. Fuel, 2019, 257: 116033

[16] Ge S , Shi Y , Xia C , Huang Z , Manzo M , Cai L , Ma H , Zhang S , Jiang J , Sonne C . . 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

[17] 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

[18] 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

[19] 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

[20] 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

[21] Olah G A . Beyond oil and gas: the methanol economy. Angewandte Chemie International Edition, 2005, 44(18): 2636–2639

[22] 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

[23] 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

[24] 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

[25] Lyman J , Fleming R H . Composition of sea water. Journal of Marine Research, 1940, 3(2): 134–146

[26] 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

[27] Iwaya T , Sasaki M , Goto M . Kinetic analysis for hydrothermal depolymerization of nylon 6. Polymer Degradation & Stability, 2006, 91(9): 1989–1995

[28] HastieJTibshiraniRFriedmanJ. The Elements of Statistical Learning. New York: Springer, 2009

[29] 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

[30] ClareAKingR D. Knowledge discovery in multi-label phenotype data. In: European conference on principles of data mining and knowledge discovery. Berlin: Springer, 2001, 42–53

[31] Boucheron S , Bousquet O , Lugosi G . Theory of classification: a survey of some recent advances. ESAIM: Probability and Statistics, 2005, 9: 323–375

[32] 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

[33] Elmaz F , Yücel Ö , Mutlu A Y . Predictive modeling of biomass gasification with machine learning-based regression methods. Energy, 2020, 191: 116541

[34] MighaniMShahiAAntonioniG. 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

[35] 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

[36] 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

[37] 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

[38] 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

[39] 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

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