ISSN print edition: 0366-6352
ISSN electronic edition: 1336-9075
Registr. No.: MK SR 9/7

Published monthly
 

Zeolite-Y-based catalyst synthesis from Nigerian Elefun Metakaolin: computer-aided batch simulation, comparative predictive response surface and neuro-fuzzy modelling with optimization

Kazeem Kolapo Salam, Emmanuel Olusola Oke, Chiamaka Joan Ude, and Umar Yahaya

Department of Chemical Engineering, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria

 

E-mail: kksalam@lautech.edu.ng

Received: 2 January 2021  Accepted: 10 October 2021

Abstract:

Nigerian Elefun Kaolinite Clay is a potential precursor for Zeolite-Y-based catalyst production, a raw material that is very useful during oil refining operation. However, fundamental process engineering studies that are necessary for the zeolite production scale-up and process design have not been investigated. Therefore, this study is based on ASPEN batch simulation and comparative study between response surface methodology and neural-fuzzy modelling with optimization of Zeolite-Y synthesis from Nigerian Elefun Metakaolin (NEM). Base case simulation model of Zeolite-Y production from NEM was performed in ASPEN Batch Process Developer (ABPD) V10 environment. Box Behnken Design (BBD) in Design Expert Software V10 was used to develop predictive model for optimization study and its prediction was compared with adaptive neuro-fuzzy inference system (ANFIS) model in MATLAB environment. Optimal conditions that maximized batch throughput of Zeolite-Y production were achieved using numerical optimization algorithm in BBD. ASPEN base case batch simulation gave batch size 0.00821 kg, cycle time 36.1 h and production rate 0.000352 kg/h. The correlation coefficients (R2) of predictive BBD and ANFIS models gave 0.9976 and 1, respectively. Optimum conditions of factors used for Zeolite-Y production via numerical optimization are 0.00854 kg zeolite per batch, sulphuric acid concentration 0.201539, sodium hydroxide concentration 2.00057 and partition coefficient of 0.010001 with desirability of 0.991. Thus, this study shows that ABPD, ANFIS and BBD are capable of simulating, predicting and optimizing Zeolite-Y production from NEM. The data obtained from this study serve as precursors to scale-up study, techno-economic feasibility and uncertainty analysis of the zeolite production.

Keywords: Zeolite; Optimization; Modelling; Adaptive neuro-fuzzy inference system

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-021-01931-1

 

Chemical Papers 76 (2) 1213–1224 (2022)

Wednesday, May 29, 2024

IMPACT FACTOR 2021
2.146
SCImago Journal Rank 2021
0.365
SEARCH
Advanced
VOLUMES
European Symposium on Analytical Spectrometry ESAS 2022
© 2024 Chemical Papers