Biochar-based Downflow Fixed-Bed Adsorption Systems for Water Treatment: Process Optimization, Reusability, and Techno-Economic Evaluation
Bibliographic record
Abstract
Adsorption processes have emerged as promising solutions for water treatment, particularly when utilizing bioderived materials, due to their environmental sustainability. Nevertheless, a key challenge of this approach lies in the regeneration of spent materials. This study investigates the possibility of using biochar in a fixed-bed adsorption system for water treatment, focusing on its potential reuse following an environmentally friendly regeneration process and evaluating its feasibility for large-scale applications. Rapid small-scale column tests were conducted to optimize process parameters for removing sulfamethoxazole while using a high concentration to prove the concept and evaluate process efficiency. Under optimal conditions, the column maintained its operational capability after treating 33 L, achieving a saturation time of up to 130 h. The adsorption behavior was explored using kinetic models, analyzing breakthrough curves to reveal dynamic performance. The Clark model demonstrated the highest degree of fit to the data, making it a reliable tool for predicting adsorption efficiencies. The reusability of the adsorbent was evaluated through a sustainable regeneration approach, enabling effective reusability for up to 5 cycles. The applicability of the proposed treatment method was further validated on real water samples, demonstrating a significant reduction in turbidity and the concentration of detected substances in the samples. Finally, a techno-economic assessment estimated a treatment cost of €0.89/m 3 , supporting the economic feasibility of the approach. This research highlights the efficiency and scalability of the proposed process as a viable, cost-effective water treatment solution for large-scale applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".