Evaluation of Fischer-Tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network. (25th October 2021)

• Record Type: Journal Article
• Authors: Garona, Higor A.; Cavalcanti, Fabio M.; de Abreu, Thiago F.; Schmal, Martin; Alves, Rita M.B.
• Is Part Of: Journal of cleaner production. Volume 321(2021)
• Publication Date: 2021-10-25
• Subjects: Environmental catalysis -- Fischer-Tropsch synthesis -- Artificial neural networks -- Light olefins -- Carbon dioxide; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.129003
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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 °C and 20 wt% of active phase), despite showing the highest emission of carbon dioxide. Highlights: Artificial neural networks were used to describe Fischer-Tropsch Synthesis. The performance of iron and cobalt-based catalysts was investigated. The temperature has a more pronounced effect than the metal composition. Iron-based catalysts present higher light olefins selectivity and conversion. Cobalt-based catalysts emit less carbon dioxide.