Experimental Study on the Treatment of Rural Domestic Wastewater Using the Multi-Soil-Layering System Filled with Sludge-Based Biochar
Bibliographic record
Abstract
This paper aims to improve the treatment effect of the multi-soil-layering system (MSL) on rural domestic wastewater. For this, sludge-based biochar materials were selected in the experiments to study its impacts on the treatment of rural domestic wastewater using the MSL. The comparative experiments were conducted for the three MSL systems filled with different materials: the sludge-based biochar (a), wood chips (b), and charcoal (c). The results showed that when the sludge-based biochar material was used as a filler, the removal effect of COD, NH4+-N, TN, TP is better than that of wood chips and charcoal, and the removal rates were 80%, 90%, 65% and 92%, respectively, meeting the Grade A standard specified in the Hebei Province Rural Domestic wastewater Discharge Standard (DB13/2171-2015); hydraulic loading rates (HLRs) have a great impact on the removal of pollutants, and the pollutant indicators in the system (a)re affected by the HLRs in different degrees, i.e., NH4+-N>TP>COD>TN from high to low; considering the treatment efficiency and pollutant removal effect, the HLRs should be selected between 800L/(m2·d) and 1200L/(m2·d). It’s concluded finally that the sludge-based biochar material can strengthen the removal effect of the MSL system. This study is of theoretical value for the further research on MSL system and resource utilization of the sludge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".