Process development and techno-economic analysis of microwave-assisted demetallization and desulfurization of crude petroleum oil
Bibliographic record
Abstract
Metals and sulfur, if not efficiently eliminated from crude petroleum oil during refining, may have severe detrimental impacts in refinery processes such as fluid catalytic cracking and hydrotreating units. Recently, the lab-scale microwave-assisted demetallization and desulfurization (MW-DMDS) of crude oil using Bis(2-ethylhexyle) phosphoric acid (D2EHPA) have shown several advantages such as high removal efficiency, being environmentally green, and lower energy requirements. This paper presents a comprehensive industrial process scheme for MW-DMDS by designing the required processing units. In addition, an effective methodology to regenerate D2EHPA using sulfuric acid and sodium hydroxide (NaOH) aqueous solutions was developed and experimentally validated. A Techno-economic investigation was carried out by adopting the ASPEN Plus process simulator to estimate the upscaling feasibility of the process to treat 50,000 barrels per stream day (BPSD) of crude oil. Total capital costs (CAPEX) and total annual operating costs (OPEX) were estimated at 6.77 MUSD and 4.23 MUSD (0.24 $/bbl), respectively. The results indicated the economic superiority of the proposed process compared to the existing technologies, like hydrodemetallization (HDM) and hydrodesulfurization (HDS) due to the remarkably lower CAPEX and OPEX costs. Sensitivity analysis by changing the primary design parameters demonstrated that the required microwave power and the corresponding purchase costs of the microwave generators have the highest share of the estimated CAPEX costs. Moreover, the annual operating costs seem to strongly depend on the reagent consumption and regeneration process effectiveness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".