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

JOURNAL OF CLEANER PRODUCTION

卷:318

Article

Fozer, Daniel^1,2  Toth, Andras Jozsef¹  Varbanov, Petar Sabev³  Klemes, Jiri Jaromir³  Mizsey, Peter⁴ 

[1] Budapest Univ Technol & Econ, Dept Chem & Environm Proc Engn, Budafoki Ut 8, H-1111 Budapest, Hungary

[2] Tech Univ Denmark, Dept Technol Management & Econ, Div Sustainabil, Prod Torvet,Bldg 424, DK-2800 Lyngby, Denmark

[3] Brno Univ Technol VUT Brno, Sustainable Proc Integrat Lab SPIL, NETME Ctr, FME,Tech 2896 2, Brno 61669, Czech Republic

[4] Univ Miskolc, Dept Fine Chem & Environm Technol, Egyet Ut, H-3515 Miskolc, Hungary

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

DOI  :  10.1016/j.jclepro.2021.128606

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free