Article: Evaluation of Fischer-Tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network
Journal of cleaner production. 2021 Oct. 25, v. 321
2021
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, ... ...
| 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. |
|---|---|
| Keywords | Fischer-Tropsch reaction ; biomass ; carbon ; carbon dioxide ; carbon monoxide ; catalysts ; chemical industry ; energy ; natural gas ; neural networks ; prediction ; temperature |
| Language | English |
| Dates of publication | 2021-1025 |
| Publishing place | Elsevier Ltd |
| Document type | Article |
| ISSN | 0959-6526 |
| DOI | 10.1016/j.jclepro.2021.129003 |
| Database | NAL-Catalogue (AGRICOLA) |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Inter-library loan at ZB MED
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.