Enhancing anaerobic digestion Efficiency: A comprehensive review on innovative intensification technologies
Notice bibliographique
Résumé
• Multiple innovative intensification technologies for anaerobic digestion were reviewed. • The mechanisms and efficiency gains of each technology were discussed. • A comparison between the advantages and challenges of each technology was presented. • Possible integration of the technologies with existing infrastructure was highlighted. • The technology readiness level (TRL) of each technology was quantified. Anaerobic digestion (AD) is an established technology that plays a crucial role in breaking down the organic compounds and biomass during the sludge treatment processes. However, there are multiple challenges associated with the application of AD on different feedstocks and under various operational conditions. The AD process is highly sensitive to operational conditions (e.g., temperature and pH) with relatively slow reactions rates especially during the hydrolysis and methanogenesis stages. These limitations can significantly affect the performance of anaerobic digesters and the biogas production rate. Therefore, various intensification technologies were proposed and investigated in the literature to upgrade the biogas production and yield as well as enhancing the removal of organics and biomass during the sludge treatment processes. Although different review studies have examined some of these intensification technologies such as physical and chemical pretreatment techniques, limited studies have focused on reviewing the innovative intensification technologies, such as microbial electrolysis cells (MEC) and micro-aeration, in AD applications. Moreover, there are no systematic investigations that compared the performance, mechanisms, advantages, and challenges of these innovative technologies to draw strong conclusions about the applicability of each technology with different wastes, feedstocks, and operation conditions. In addition, the quantification of possible integration of these technologies with the current infrastructure and the technology readiness level were not well-investigated in literature. Therefore, in the current study, seven different innovative intensification technologies were reviewed including MEC-assisted AD, conductive functional materials, micro-aeration, anaerobic membrane bioreactors, hydrogen injection, IntensiCarb, and microbial hydrolysis process using Caldicellulosiruptor bescii . A detailed description of these technologies for increasing biogas yields was presented, with a special focus on the performance, reliability, efficiency gains, and applicability of each technology. The major insights of this review can serve as a reference for the potential intensification technologies that can be integrated with existing AD systems for enhanced biogas production and removal of organics and biomass.
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».