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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Evaluation of Fischer-Tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network

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Garona, Higor A. [1] ; Cavalcanti, Fabio M. [1] ; de Abreu, Thiago F. [1] ; Schmal, Martin [1] ; Alves, Rita M. B. [1]
Total Authors: 5
[1] Univ Sao Paulo, Escola Politecn, Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Web of Science Citations: 0

Currently, energy transition due to fossil fuel negative side effects is taking place. This transition impacts the chemical industry based on light olefins reactions. Plastics, detergents, polymers, and others are mostly produced from such hydrocarbons, which are mainly originated from oil-based and highly energy-consuming processes. Fischer-Tropsch Synthesis (FTS) is a strategic technology capable to transform a given carbon source, including natural gas and biomass, into high added-value hydrocarbons. It is affected by several conditions, such as catalyst design and operating conditions and its feasibility requires a good selection of relevant process variables to optimize light olefins yield. In this work, Machine Learning models were used to predict adequate reaction conditions from the catalytic literature data. Three-layer feedforward neural networks were adjusted using a careful selection of operating conditions and catalyst composition as inputs and carbon monoxide conversion, light olefins selectivity, and carbon dioxide yield as outputs. The results indicate neural network prediction efficacy for FTS most relevant variables, such as temperature and catalyst composition. This work presents the novelty of including more variables in the model compared to recent similar studies, such as catalyst support, active phase, and promoters as inputs; and light olefins selectivity and CO2 yield as outputs. Overall, Fe-based catalyst (standard Fe (1.6 wt%) K/TiO2) presented the highest light olefins selectivity and yield at optimal conditions (T = 500 degrees C and 20 wt% of active phase), despite showing the highest emission of carbon dioxide. (AU)

FAPESP's process: 14/50279-4 - Brasil Research Centre for Gas Innovation
Grantee:Julio Romano Meneghini
Support Opportunities: Research Grants - Research Centers in Engineering Program
FAPESP's process: 17/11940-5 - Evaluation of catalysts supported on carbon nanotubes and kinetic modeling of the process for the Water-Gas-Shift reaction (WGS)
Grantee:Fábio Machado Cavalcanti
Support Opportunities: Scholarships in Brazil - Doctorate