Modelling Fischer–Tropsch synthesis: A review of applications using genetic algorithms and hybrid GA–based models
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".