Banca de DEFESA: ARIVONALDO BEZERRA DA SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : ARIVONALDO BEZERRA DA SILVA
DATE: 21/12/2023
TIME: 10:00
LOCAL: Auditório do LABTAM
TITLE:

BIBLIOMETRIC ANALYSIS AND MACHINE LEARNING TO DETERMINE OPTIMIZED MANGANESE- BASED OXYGEN CARRIERS FOR APPLICATION IN CHEMICAL LOOPING PROCESSES

KEY WORDS:

Oxygen carrier; Manganese; Bibliometric Analysis; CL, Machine Learning

PAGES: 100
BIG AREA: Ciências Exatas e da Terra
AREA: Química
SUBÁREA: Química Inorgânica
SPECIALTY: Físico Química Inorgânica
SUMMARY:

The scope of this dissertation consists of determining optimized manganese-based oxygen carriers for application in Chemical Looping processes using Bibliometric Analysis and Machine Learning. The Bibliometric Analysis carried out in this study offers a comprehensive view of statistical data and trends related to manganese-based oxygen carriers for applications in Chemical Looping (CL) processes from 2006 to 2023. The search was carried out in the Web database of Science resulted in a total of 426 documents, of which 65 were carefully selected using the ProKnow-C method to compose the study portfolio. Then, to carry out the bibliometric analysis (construction of tables, graphs and bibliometric maps) the Web of Science platform, VOSviewer and Excel were used. Soon after, an Excel spreadsheet was created containing the input and output data referring to the articles in the bibliographic portfolio for application in Machine Learning, to determine the optimized oxygen carriers based on manganese. Then, the oxygen carriers were experimentally reproduced and XRF, DRX, SEM and oxygen transport capacity (Roc) analyzes were carried out on a thermobalance. Bibliometric analysis revealed the considerable potential of manganese-based synthetic oxygen carriers for application in Chemical Looping processes. According to analyzes of the most relevant articles, these materials have been shown to have reduced friction rates and a low tendency to agglomerate in continuous fluidized bed reactors. Furthermore, the analysis will contribute to the optimization of the physicochemical properties of oxygen carriers, as it considered the influence of the type of active phase and support on reactivity tests, oxygen transport capacity, friction rate and agglomeration in continuous fluidized bed reactors in CL processes. Regarding data processing in Machine Learning, it was found that the data relating to Oxygen Transport Capacity adjusted very well to the Random Forest and XGBoost regression models, with highly accurate predictions with a high coefficient of determination for the training set. (R2 = 0.9511 in Random Forest and R2 = 0.9999 in XGBoost) and test (R2 = 0.9352 in Random Forest and R2 = 0.9309 in XGBoost), low mean squared errors (MSE = 0.0728 in Random Forest and MSE = 0.0776 in XGBoost) and Root Mean Square Error for the test set (RMSE = 0.2698 in Random Forest and RMSE = 0.2786 in XGBoost). Furthermore, analyzing the graphs of the input variables versus Roc for the training sets of the two models, it is possible to determine two oxygen carriers that have good oxygen transport capacity and good mechanical resistance: TO_MnFe (76% Mn and 24 % Fe) and TO_MnMg (60% Mn and 40% Mg).

COMMITTEE MEMBERS:
Externo à Instituição - DENER DA SILVA ALBUQUERQUE - IFRN
Presidente - 349770 - DULCE MARIA DE ARAUJO MELO
Externa à Instituição - REBECCA ARAÚJO BARROS DO NASCIMENTO SANTIAGO - UFRN
Externo ao Programa - 3304576 - RODOLFO LUIZ BEZERRA DE ARAÚJO MEDEIROS - nullInterna - 1308577 - SIBELE BERENICE CASTELLA PERGHER