Publication detail

Sustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural network

Fózer, D., Tóth, A.J., Varbanov, P.S., Klemeš, J.J., Mizsey, P.

Original Title

Sustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural network

Type

journal article in Web of Science

Language

English

Original Abstract

Global warming and climate change urge the deployment of close carbon-neutral technologies via the synthesis of low-carbon emission fuels and materials. An efficient intermediate product of such technologies is the biomethanol produced from biomass. Microalgae based technologies offer scalable solutions for the biofixation of CO2, where the produced biomass can be transformed into value-added fuel gas mixtures by applying thermochemical processes. In this study, the environmental and economic performances of biomethanol production are examined using artificial neural networks (ANNs) for the modelling of catalytic and noncatalytic hydrothermal gasification (HTG). Levenberg-Marquardt and Bayesian Regularisation algorithms are applied to describe the thermocatalytic transformation involving various types of feedstocks (biomass and wastes) in the training process. The relationship between the elemental composition of the feedstock, HTG reaction conditions (380 ?C & ndash;717 ?C, 22.5 MPa & ndash;34.4 MPa, 1 & ndash;30 wt% biomass-to-water ratio, 0.3 min & ndash;60.0 min residence time, up to 5.5 wt% NaOH catalyst load) and fuel gas yield & composition are determined for Chlorella vulgaris strain. The ideal ANN topology is characterised by high training performance (MSE = 5.680E-01) and accuracies (R-2 >= 0.965) using 2 hidden layers with 17-17 neurons. The process flowsheeting of biomass-to-methanol valorisation is performed using ASPEN Plus software involving the ANN-based HTG fuel gas profiles. Cradle-to-gate life cycle assessment (LCA) is carried out to evaluate the climate change potential of biomethanol production alternatives. It is obtained that high greenhouse gas (GHG) emission reduction (-725 kg CO2,eq (t CH3OH)-1) can be achieved by enriching the HTG syngas composition with H2 using variable renewable electricity sources. The utilisation of hydrothermal gasification for the synthesis of biomethanol is found to be a favourable process alternative due to the (i) variable synthesis gas composition, (ii) heat integration, and (iii) GHG emission mitigation possibilities.

Keywords

Artificial neural networks; Biomethanol; Cost analysis; Hydrothermal gasification; Life cycle assessment; Power-to-Liquid

Authors

Fózer, D., Tóth, A.J., Varbanov, P.S., Klemeš, J.J., Mizsey, P.

Released

10. 10. 2021

Publisher

Elsevier

Location

ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND

ISBN

0959-6526

Periodical

Journal of Cleaner Production

Year of study

318

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

128606

Pages to

128606

Pages count

19

URL

https://www.sciencedirect.com/science/article/pii/S0959652621028110

Full text in the Digital Library

http://hdl.handle.net/11012/203075

BibTex

@article{BUT172449,
  author="Dániel {Fózer} and András József {Tóth} and Petar Sabev {Varbanov} and Jiří {Klemeš} and Peter {Mizsey}",
  title="Sustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural network",
  journal="Journal of Cleaner Production",
  year="2021",
  volume="318",
  number="1",
  pages="128606--128606",
  doi="10.1016/j.jclepro.2021.128606",
  issn="0959-6526",
  url="https://www.sciencedirect.com/science/article/pii/S0959652621028110"
}