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

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dc.contributor.authorFózer, Dánielcs
dc.contributor.authorTóth, András Józsefcs
dc.contributor.authorVarbanov, Petar Sabevcs
dc.contributor.authorKlemeš, Jiřícs
dc.contributor.authorMizsey, Petercs
dc.coverage.issue1cs
dc.coverage.volume318cs
dc.date.accessioned2021-12-06T15:52:02Z
dc.date.available2021-12-06T15:52:02Z
dc.date.issued2021-10-10cs
dc.description.abstractGlobal 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.en
dc.formattextcs
dc.format.extent128606-128606cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Cleaner Production. 2021, vol. 318, issue 1, p. 128606-128606.en
dc.identifier.doi10.1016/j.jclepro.2021.128606cs
dc.identifier.issn0959-6526cs
dc.identifier.other172449cs
dc.identifier.urihttp://hdl.handle.net/11012/203075
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofJournal of Cleaner Productioncs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0959652621028110cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0959-6526/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectArtificial neural networksen
dc.subjectBiomethanolen
dc.subjectCost analysisen
dc.subjectHydrothermal gasificationen
dc.subjectLife cycle assessmenten
dc.subjectPower-to-Liquiden
dc.titleSustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural networken
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-172449en
sync.item.dbtypeVAVen
sync.item.insts2022.05.19 16:55:02en
sync.item.modts2022.05.19 16:14:09en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Laboratoř integrace procesůcs
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