Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: insights using a novel nickel-strontium-zirconium-aluminum catalyst

Tahani S. Gendy, Radwa A. El-Salamony, Maher M. Alrashed, Abdulaziz Bentalib, Ahmed I. Osman*, Rawesh Kumar, Anis H. Fakeeha, Ahmed S. Al-Fatesh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Downloads (Pure)

Abstract

This study investigates the molecular dynamics of methane dry reforming catalyzed by a novel nickel-strontium-zirconium-aluminum (5Ni+3Sr/10Zr+Al) catalyst, leveraging both Response Surface Methodology (RSM) and Radial Basis Function Neural Network (RBFNN) for predictive optimization. Focusing on the impact of operational parameters—hourly space velocity, reaction temperature, and CO2:CH4 mole ratio—on the conversion rates and formation of reaction components, we aim to predict optimal conditions and corresponding process variables. Through a comparison of a three-layer Feed Forward Neural Network, optimized at a 3:10:1 topology, with traditional RSM approaches, our findings highlight the superior predictive capabilities of ANN models. Notably, ANN demonstrated exceptional performance with R2adj and F_Ratio values significantly surpass those of RSM, alongside lower statistical error terms. This superiority is attributed to ANN's robust handling of nonlinear relationships between inputs and outputs, asserting its potential for enhancing predictive accuracy in chemical process optimization. At optimum predicted conditions like 1 CH4/CO2,750 °C reaction temperature, 12000 cm3g−1h−1 space velocity, NiSrZrAl outperformed with > 85 % CH4 and CO2 conversion with H2/CO ∼1 up to 20 h time on stream. Our research underscores the importance of integrating advanced modeling techniques for the efficient and accurate prediction of catalytic reactions, offering valuable insights for future applications in chemical engineering and catalysis.

Original languageEnglish
Article number114216
Number of pages13
JournalMolecular Catalysis
Volume562
Early online date13 May 2024
DOIs
Publication statusPublished - 01 Jun 2024

Keywords

  • Artificial neural network
  • Carbon dioxide
  • Methane dry reforming
  • Radial basis function
  • Response surface methodology

ASJC Scopus subject areas

  • Catalysis
  • Process Chemistry and Technology
  • Physical and Theoretical Chemistry

Fingerprint

Dive into the research topics of 'Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: insights using a novel nickel-strontium-zirconium-aluminum catalyst'. Together they form a unique fingerprint.

Cite this