Development of novel iron catalyst supported on carbon material for Fischer-Tropsch synthesis
Bibliographic record
Abstract
Energy demand is rising, and concerns about depletion of the current energy sources and climate change issues lead to the search of renewable alternatives. FTS introduces a sustainable route for catalytic conversion of syngas derived from biomass gasification to sulfur-free transportation fuels. FTS is a catalytic polymerization reaction for production of wide range of hydrocarbons. Effective catalyst design is a crucial step in improving the performance of FTS and selectivity of the desired range of products. Fe as the active metal is preferred to Co, due to the lower price, activity in severe operating conditions, and application when syngas with lower H2/CO ratios (derived from biomass gasification process). In this study, catalytic performance of Fe catalyst was evaluated using Cu and Mo as promoters and biomass-derived activated carbon (AC) as support. The study plan for this research was divided into four sub-objectives. In the first phase, machine learning models were investigated to prepare AC with optimized textural properties using canola hull. Using random forest regression (RFR) method, 90% mesoporosity was achieved for the AC synthesized at 600 °C, impregnation ratio (weight of chemical activation agent/weight of biochar) of 2, and reaction time of 60 min. The FTS performance of the 20Fe/AC catalyst was compared with the Fe catalyst supported on commercial Al2O3, and AC, leading to higher catalytic activity of the Fe catalyst supported on synthesized AC derived from biomass (46.7% CO conversion and C5+ selectivity of 72.5%). The improved performance of the synthesized 20Fe/AC catalyst was assigned to the engineered textural properties and surface chemistry of AC. Promoting the Fe catalyst with structural and electronical promoters is reported to enhance the CO conversion and selectivity of the desired products in FTS. Therefore, in the second phase, the Cu-Mo promoted Fe catalyst supported on optimized and alkaline treated AC was scrutinized to enhance the FTS performance. Optimization of the metal loadings (Fe, Cu, and Mo) using a two-level full factorial design enhanced the CO conversion to 78.8% for 20Fe10Mo2Cu/AC catalyst. Simultaneous contributions from Fe and Mo carbides (as confirmed with XPS and EXAFS results) improved the selectivity of light olefins (20.4%) and C5+ yield (48.3%) in the structure of the trimetallic catalyst. In the third phase, the kinetics study of FTS was conducted over the optimum trimetallic catalyst (20Fe10Mo2Cu/AC) in a fixed bed reactor by collecting experimental data over a wide range of FTS operating conditions (P=2.06-4.13 MPa, T=280-310 °C, H2/CO=1, GHSV=1500-3000 h-1). The reaction kinetics was evaluated based on the enol mechanism (molecular adsorption of CO), using Langmuir-Hinshelwood-Hougen-Watson (LHHW) adsorption theory considering an integral system. Based on the developed one-dimensional model, the partial pressure profiles of the reactants and products were studied along the reaction bed. The activation energy for CO consumption rate model was obtained as 51.9 kJ. mol-1. In the last phase, CFD simulation FTS using the synthesized Cu-Mo promoted Fe/AC catalyst in a fixed bed reactor was conducted using a commercial software package (COMSOL Multiphysics). A 2D reactor model considering the catalytic bed as a porous media was implemented and the governing equations of momentum, mass, and transport species were solved for the computational domain. The effects of operational conditions were critically investigated on the partial pressure profiles of the species (based on the CFD contours), and the simulation results were validated using experimental data. While increasing total pressure improved the partial pressure of C5+ hydrocarbons to 0.12 bar, increasing temperature to 310 °C limited the chain growth. Moreover, the simulated results were in line with the experimental data, providing an insightful overview on the black box of the hydrodynamics coupled with reaction kinetics in FTS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".