A Review of Hydrothermal Liquefaction of Biomass for Biofuels Production with a Special Focus on the Effect of Process Parameters, Co-Solvents, and Extraction Solvents
Bibliographic record
Abstract
Hydrothermal liquefaction is one of the common thermochemical conversion methods adapted to convert high-water content biomass feedstocks to biofuels and many other valuable industrial chemicals. The hydrothermal process is broadly classified into carbonization, liquefaction, and gasification with hydrothermal liquefaction conducted in the intermediate temperature range of 250–374 °C and pressure of 4–25 MPa. Due to the ease of adaptability, there has been considerable research into the process on using various types of biomass feedstocks. Over the years, various solvents and co-solvents have been used as mediums of conversion, to promote easy decomposition of the lignocellulosic components in biomass. The product separation process, to obtain the final products, typically involves multiple extraction and evaporation steps, which greatly depend on the type of extractive solvents and process parameters. In general, the main aim of the hydrothermal process is to produce a primary product, such as bio-oil, biochar, gases, or industrial chemicals, such as adhesives, benzene, toluene, and xylene. All of the secondary products become part of the side streams. The optimum process parameters are obtained to improve the yield and quality of the primary products. A great deal of the process depends on understanding the underlined reaction chemistry during the process. Therefore, this article reviews the major works conducted in the field of hydrothermal liquefaction in order to understand the mechanism of lignocellulosic conversion, describing the concept of a batch and a continuous process with the most recent state-of-art technologies in the field. Further, the article provides detailed insight into the effects of various process parameters, co-solvents, and extraction solvents, and their effects on the products’ yield and quality. It also provides information about possible applications of products obtained through liquefaction. Lastly, it addresses gaps in research and provides suggestions for future studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".