Modelling Fischer–Tropsch synthesis: A review of applications using genetic algorithms and hybrid GA–based models

The global transition toward sustainable energy production has intensified interest in clean fuel technologies derived from renewable organic materials, including biomass. Fischer–Tropsch synthesis (FTS) plays a crucial role in this shift as a key part of converting coal, natural gas, and biomass into transportation fuels and long-chain hydrocarbons, such as diesel and waxes. However, optimising the FTS process is highly challenging due to its sensitivity to multiple operational parameters, including temperature, pressure, gas flow rate, reactant ratio, catalyst type, and formulation method. Traditional deterministic models struggle to effectively capture the complex reaction mechanisms underlying FTS, as multiple pathways act simultaneously with varying influence depending on catalyst properties and process conditions. To address these limitations, this review explores how genetic algorithms (GA), inspired by the process of natural selection, have been used to optimise the FTS process. It also discusses hybrid GA–based models, which combine GA with other techniques to enhance optimisation performance and modelling accuracy. It provides a detailed overview of the mathematical foundations of GA, illustrating their ability to navigate high-dimensional, multi-variable optimisation landscapes and capture non-linear interactions in process modelling. A comprehensive analysis of research published between 2000 and 2025 sourced from databases including Scopus, Google Scholar, IEEE Xplore, and Springer focuses on identifying the key variables affecting FTS optimisation and modelling problems and demonstrating how GA and its hybrid extensions have been leveraged to optimise process conditions, particularly in terms of CO conversion, product selectivity, and hydrocarbon yield. By synthesising recent advancements and critically evaluating the strengths and limitations of GA-driven methodologies, this review highlights the potential of GA-based optimisation frameworks in advancing FTS modelling. Moreover, in alignment with the sustainability objectives of the Paris Agreement 2050, these advanced computational strategies offer promising pathways for developing more efficient, environmentally friendly, and scalable catalytic systems in energy production.