In-Situ Hydrogen Generation through Heavy Oil Pyrolysis Catalyzed by Clay Minerals
Bibliographic record
Abstract
Summary In-situ gasification (ISG) has been recognized as a highly promising technology for hydrogen generation. One of the key methods for generating hydrogen from crude oil is through the pyrolysis gasification of heavy oil. Clay minerals in the rock matrix function as natural catalysts due to their exceptional adsorption capacity, ion exchange capability, and abundance of acidic sites. These properties greatly enhance the process of ISG of heavy oil, leading to efficient hydrogen generation. This study examines the impact of different clay minerals on in-situ hydrogen generation through heavy oil pyrolysis. We investigate the catalytic effect of clay minerals on hydrogen generation from crude oil pyrolysis using thermogravimetric mass spectrometry (TG-MS) analysis. We determine the amount of hydrogen generated and hydrogen generation efficiency (HGE) through equivalent characteristic spectrum (ECS) analysis. Additionally, we calculate the kinetic parameters using the Friedman and Distributed Activation Energy Model (DAEM) methods to assess the influence of clay minerals on the activation energy of the hydrogen generation process in crude oil pyrolysis gasification. The oil samples containing clay minerals exhibit greater mass loss during the pyrolysis stage and higher thermal conversion compared with the oil samples without clay minerals. Through MS and ECS analysis, it is observed that the oil samples generate hydrogen during both the pyrolysis and coking stages, which can be attributed to the acidic catalytic and ion exchange effects of the clay minerals. The Lewis and Brønsted acid sites of the clay minerals play a role in advancing the temperature range of hydrogen generation from oil samples pyrolysis. The HGE analysis indicates that the strong adsorption of the clay minerals contributes to a more complete pyrolysis of heavy oil, resulting in the generation of more hydrogen. The kinetic analysis of hydrogen generation reveals that the activation energy tends to increase with the conversion rate. Notably, montmorillonite, a type of clay mineral, significantly reduces the activation energy for hydrogen generation during heavy oil pyrolysis and enhances the hydrogen conversion rate. Hydrogen generation is achieved by utilizing natural clay minerals, which act as catalysts in the catalytic ISG of crude oil. The ISG of heavy oil represents a significant stride in advancing methods for hydrogen generation from heavy oil, thereby facilitating the transition of fossil fuels to cleaner energy sources like hydrogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".