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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Garona, Higor A. [1] ; Cavalcanti, Fabio M. [1] ; de Abreu, Thiago F. [1] ; Schmal, Martin [1] ; Alves, Rita M. B. [1]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Escola Politecn, Sao Paulo - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF CLEANER PRODUCTION; v. 321, OCT 25 2021.
Citações Web of Science: 0
Resumo

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)

Processo FAPESP: 14/50279-4 - Brasil Research Centre for Gas Innovation
Beneficiário:Julio Romano Meneghini
Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia
Processo FAPESP: 17/11940-5 - Avaliação de catalisadores suportados em nanotubos de carbono e modelagem cinética do processo para a reação de Water-Gas-Shift (WGS)
Beneficiário:Fábio Machado Cavalcanti
Modalidade de apoio: Bolsas no Brasil - Doutorado