A review using data-driven approach in quantitative assessment of Fischer–Tropsch synthesis

This study presents a new, data-driven review of Fischer–Tropsch (FT) synthesis by systematically analyzing the interplay between reaction parameters and product selectivity using a comprehensive literature-derived dataset. Unlike conventional reviews that focus solely on descriptive trends, this work integrates a structured data matrix comprising 11 input variables and 21 output responses, enabling a quantitative evaluation of process–performance relationships. Moreover, a generalized kinetic model that departs from conventional assumptions of fixed reaction orders is developed and compared. By employing regression techniques on estimated kinetic data, both empirical and mechanistic models are assessed, with particular emphasis on the underexplored role of water in modulating catalytic behaviour. The findings reveal that water exerts a positive influence on olefin production by promoting surface-active carbon formation, though this effect diminishes with increasing hydrocarbon chain length. Molecular dynamics simulations further support this by showing enhanced water-metal interactions, particularly for Fe–Ni alloys. The study also employs analysis of variance (ANOVA) to quantify the binary effects of operating conditions on conversion and selectivity (olefin/paraffin with carbon number up to 10). Altogether, this work not only consolidates kinetic insights but also introduces a predictive framework for understanding and optimizing FT synthesis performance, offering fresh perspectives for catalyst and process design.