Technical challenges and corrosion research progress in bio-crude co-processing
Bibliographic record
Abstract
Bio-oil (BO), produced from fast pyrolysis (FPBO) and hydrothermal liquefaction (HTL-BO) methods, is a promising renewable energy source derived from biomass. However, its undesirable properties such as high oxygen and moisture content resulting in high corrosivity and poor thermal stability, have hindered its widespread adoption as a drop-in fuel and necessitate its upgrading. Co-processing BO with petroleum intermediates in existing fluid catalytic cracking (FCC) units presents a promising approach for converting low-quality bio-crudes into valuable fuels. Nonetheless, three primary challenges - BO’s low thermal stability, high corrosivity, and immiscibility with petroleum fractions - complicate its co-processing. This review summarizes these critical challenges related to BO storage and co-processing, with a particular emphasis on corrosion issues. Recent progress in corrosion research related to BO handling, including both FPBO and HTL-BO, is thoroughly examined. This includes studies on corrosion in pure BO, BO with additives, mixtures of BO and petroleum fractions, and model BO. The effect of different process parameters—such as alloying elements, testing temperature, exposure time, BO sources, catalysts, and inorganic corrodents—on the corrosion susceptibility of candidate steels was investigated. Chromium-enriched alloys demonstrated superior corrosion resistance compared to low chromium alloys, particularly at elevated temperatures. Blending BO with petroleum fractions and additives was found to improve the resistance to corrosivity and thermal stability. Beyond weight loss immersion experiments, electrochemical techniques, such as electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP), are effective in obtaining in-depth corrosion mechanisms in BO environments, though challenges remain. Finally, research challenges and knowledge gaps are discussed to direct future efforts, including understanding BO phase behavior, corrosion mechanisms in BO environments, improvements in experimental methods and standards, and potential research paths.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".