Treatment of aqueous phase from hydrothermal liquefaction of municipal sludge by adsorption: Comparison of biochar, hydrochar, and granular activated carbon
Bibliographic record
Abstract
Hydrothermal liquefaction (HTL) is promising for treating waste with high moisture, such as municipal sludge, and producing biocrude (a petroleum-like biofuel). However, a large amount of wastewater byproduct, HTL aqueous, is generated. The presence of hazardous compounds (e.g., phenolic compounds and nitrogenous organics) makes HTL aqueous the biggest bottleneck for full-scale implementation at treatment plants. This study investigated the adsorption of various pollutants, focusing on chemical oxygen demand (COD), in HTL aqueous to granular activated carbon (GAC), biochar, and hydrochar. It assessed the effect of pH, temperature, time, and adsorbent concentration on adsorption efficiency and identified proper adsorbent and process conditions for removing most of the pollutants from HTL aqueous. GAC showed the highest adsorption capacity (184 mg/g) for COD, surpassing biochar (44 mg/g) and hydrochar (42 mg/g). The adsorption of COD to all adsorbents followed pseudo-second-order kinetic and Freundlich isotherm, suggesting that the adsorption of HTL aqueous pollutants is a heterogeneous and multilayer process, limited by chemosorption. The adsorption was endothermic, favored by elevated temperatures and neutral pH. This means adsorption is more efficient and economical for treating HTL aqueous that is a hot stream at the large-scale and it saves chemical needs. Lastly, GAC was highly efficient and selective in removing harmful pollutants, such as COD (up to 66%), total phenolic compounds (up to 94%), pyrazines (up to 99%), pyridines (up to 100%), and cyclic ketones (up to 95%) while preserving valuable volatile fatty acids (VFAs) and ammonia for subsequent recovery. Removal of potentially inhibitory compounds and preserving VFAs are crucial for carbon recovery in anaerobic biological treatment of HTL aqueous. The results suggested the necessity of optimizing adsorbent dose for maximizing removal of specific group of inhibitory compounds in full-strength HTL aqueous for enhancing downstream biological treatment. Lastly, this study established the groundwork for HTL aqueous adsorption, elucidating its effectiveness and mechanism for pollutant removal.
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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".