Prospects and challenges of thermal hydrolysis pretreatment of microalgae for enhancing bioenergy and resource recovery in anaerobic bioprocesses
Bibliographic record
Abstract
Microalgae have emerged as a promising feedstock for bioenergy production through anaerobic digestion and fermentation, gaining significant attention due to their rapid growth rate, ability to adapt to diverse environments, and rich biochemical composition. However, the recalcitrant nature of the microalgal cell wall necessitates pretreatment to enhance the accessibility of intracellular components and improve overall bioenergy yields from anaerobic digestion/fermentation. Among the various pretreatment methods, the thermal hydrolysis process has proven to be a promising strategy for enhancing the efficiency of bioenergy recovery from microalgal biomass. The benefits of thermal hydrolysis pretreatment of microalgae include improved organic matter solubilization, enhanced digestibility, and increased product yields in subsequent anaerobic digestion/fermentation processes for biomethane, biohydrogen, and volatile fatty acids production. However, thermal pretreatment poses challenges, such as forming future research by-products like furfural and ammonia, which can adversely affect microbial activities and reduce process efficiency. Thus, addressing its associated challenges is critical for maximizing its effectiveness in bioenergy and resource recovery. This review provides a comprehensive analysis of these challenges and offers recommendations for future research, emphasizing the need for optimized pretreatment strategies for advancing the sustainable and efficient use of microalgae in bioenergy production. • Thermal hydrolysis process (THP) for microalgal solubilization was discussed. • Impact on anaerobic digestion and fermentation processes was critically reviewed. • Roles of THP process parameters on bioenergy and resource recovery were reviewed. • Prospects, challenges, and future research needs were discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".