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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU141556" target="_blank" >RIV/00216305:26210/21:PU141556 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0959652621028110" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0959652621028110</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jclepro.2021.128606" target="_blank" >10.1016/j.jclepro.2021.128606</a>

Result language

angličtina

Original language name

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

Original language description

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

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20704 - Energy and fuels

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Volume of the periodical

318

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

128606-128606

UT code for WoS article

000725264900007

EID of the result in the Scopus database

2-s2.0-85112829670