Banca de QUALIFICAÇÃO: LAMARA MACIEL DOS SANTOS

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : LAMARA MACIEL DOS SANTOS
DATE: 06/02/2024
TIME: 10:00
LOCAL: videoconferência
TITLE:

Selection of Ni-based catalysts for CO2 methanation using data processing via regression models (machine learning)

KEY WORDS:

machine learning; catalysis; methanation; CO₂ hydrogenation; nickel based catalyst

PAGES: 97
BIG AREA: Ciências Exatas e da Terra
AREA: Química
SUBÁREA: Físico-Química
SPECIALTY: Cinética Química e Catálise
SUMMARY:

The CO₂ methanation process is a crucial step in the energy transition towards a carbon-free future. By utilizing carbon dioxide as a renewable feedstock in catalytic hydrogenation reactions to produce methane, not only does it enable the recycling of carbon resources, but it also meets the growing demand for natural gas. Thus, catalytic hydrogenation of CO₂ into methane stands out as an attractive defossilization technology in the fight against climate change, as it consumes CO₂ with H₂ derived from renewable energy sources to generate CH₄. However, predicting the performance of catalytic systems for this process remains a challenge. The application of Machine Learning techniques emerges as a promising approach to predict the performance of potential catalysts based on experimental descriptors. For this purpose, a database of Ni-based catalysts was created from literature data and subjected to data mining. Ensemble tree-based machine learning regression models were developed to predict CO₂ conversion and CH₄ selectivity based on experimental descriptors. In this context, the significance of variables such as Ni content, calcination temperature, and reaction temperature in predictive modeling was highlighted. The experimental validation of the prediction model and the interpretation of the insights generated not only confirmed the effectiveness of the method but also established this approach as a fundamental tool to guide and optimize future experiments.

COMMITTEE MEMBERS:
Interno - 1086214 - ANDERSON DOS REIS ALBUQUERQUE
Presidente - 349770 - DULCE MARIA DE ARAUJO MELO
Externo ao Programa - 3304576 - RODOLFO LUIZ BEZERRA DE ARAÚJO MEDEIROS - nullExterno à Instituição - ÂNGELO ANDERSON SILVA DE OLIVEIRA - UFRN