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

Integration of MOF-derived coating and noble metal promotion on granular supports: high-performance ZIF-67(Co)/Sn-Pt-Al₂O₃ catalysts for Fischer-Tropsch synthesis

Meysam Mirshekari, Halime Kord-Tamandani, Ali Akbar Mirzaei, and Ebrahim Mollashahi

Department of Chemistry, Faculty of Sciences, University of Sistan and Baluchestan, Zahedan, Iran

E-mail: kord.chem96@gmail.com

Received: 26 February 2026  Accepted: 2 June 2026

Abstract:

The development of Fischer–Tropsch synthesis (FTS) catalysts with high activity and long-term stability remains a major challenge. In this study, an innovative strategy was proposed to design granular catalysts by promoting industrial alumina granules with Sn and Pt, followed using in-situ growth of a ZIF-67 coating. To assess Fischer–Tropsch performance, three catalyst systems were investigated: (i) ZIF-67(Co)/Al₂O₃ granular catalyst (Sample 1), (ii) ZIF-67(Co)/Al₂O₃ powder catalyst (Sample 2), and (iii) conventional Co/Al₂O₃ granules (Sample 3). The results demonstrated that the in-situ synthesized ZIF-67(Co)/Sn–Pt–Al₂O₃ granular catalyst exhibited higher CO conversion, enhanced selectivity toward heavy hydrocarbons (C₅⁺) and significantly improved stability during 100 h of reaction. To analyze catalytic behavior, the experimental data were fitted using two semi-empirical kinetic models (GPLE1 and GPLE2) and a computational model based on an artificial neural network (ANN) developed in Python. For the ZIF-67/Al₂O₃ granular systems, the ANN model achieved the highest accuracy with RMSE = 0.1 × 10^− 2 and R² ≈ 0.95, whereas the predictive accuracy decreased for the promoted catalysts without MOF. These findings indicate that integrating innovative experimental approaches with advanced predictive modeling can provide a powerful strategy for the development of industrial FTS catalysts with high performance and long-term durability.

Graphical abstract

Keywords: Fischer–Tropsch synthesis; Granular catalysts; ZIF-67; Catalyst deactivation; Artificial neural network; Kinetic modeling

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-026-05158-w

Chemical Papers 80 (11) 12941–12953 (2026)