Methods of pretreatment and their impacts on anaerobic codigestion of multifeedstocks: A review
Bibliographic record
Abstract
Anaerobic codigestion (AnCoD) has attracted attention owing to its advantages over conventional anaerobic digestion, and attempts are still going on to develop methods for improving the efficiency of this technology. Mostly, addition of cosubstrates without applying a proper pretreatment cannot adequately enhance the performance of the digestion. However, there is a lack of a comprehensive study on different pretreatment methods specific to the wide range of cosubstrates. This review aimed to (i) categorize pretreatment techniques that have been developed for improving AnCoD, (ii) present the results of the studies on the effect of pretreatment on improving AnCoD, and (iii) provide a comparison between pretreatment methods and their application for different types of cosubstrates. The findings primarily validated the influence of pretreatment to enhance the process by increasing biodegradability, improved hydrolysis, reduced hydraulic retention time (HRT), and improved methane production. The five main categories of pretreatment employed in codigestion included the following: mechanical, thermal, chemical, biological, and hybrid pretreatment. Among them, mechanical and biological pretreatment have the most and least application in codigestion, respectively. Greater efforts are required on the application of biological pretreatment and cost-benefit analysis of different pretreatment options on the variety of the cosubstrates. PRACTITIONER POINTS: Pretreatment can significantly enhance biomethane production in anaerobic digestion Anaerobic codigestion along with pretreatment can further enhance the conventional anaerobic digestion of single feedstock Mechanical and biological methods have been the most and least practiced pretreatment options Selection of applicable pretreatment option to enhance methane production is subject to the type of cosubstrates in the system There is a research gap in evaluating the application of biological pretreatment for various types of cosubstrates.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".