Optimizing drying of municipal dewatered sludge using heat-assisted microorganisms and pig manure addition: A process and economic analysis
Bibliographic record
Abstract
• Multi-source heat and PM synergistically achieved rapid bio-drying of MDS. • The dried products met all the specifications for subsequent thermal treatment • The drying system was characterized by high energy efficiency and spontaneous drying. • Drying technology saved US $11.46–16.84/ton (¥83-122.10/ton) of MDS treated. Sludge drying is an important pretreatment step for municipal dewatered sludge (MDS) treatment and disposal, but the time-consuming and high cost of existing processes have hindered the development of MDS treatment and disposal. In this study, a novel sludge drying technology was proposed on the basis of the characteristics and treatment needs of MDS in China. Pig manure (PM) addition and multisource heat assistance together assisted hyperthermophilic bacteria in achieving rapid drying of MDS. Mechanical factors were optimized via orthogonal experiments, and the optimum PM addition ratio was determined. The relationship between energy input (generation) and output in the system was explored to reveal the reasons why the novel drying technology exhibited superiority. Compared with the traditional biological drying technique and the thermal drying technique, the novel technique has the advantages of high efficiency, time savings and low cost. After 24 h of drying, the moisture content, organic matter content and net calorific value on an air-dried basis ( Q net, V,Mad ) of the dried products were 31.43 ± 0.91 %, 72.47 ± 1.89 % and 16.94 ± 0.35 MJ/kg, respectively, which met the requirements of heat recovery and utilization for subsequent thermal treatment. The energy input (generation) to the system exceeded the energy output, indicating that the drying process was positively spontaneous. Multisource heat assistance accounted for 81.6 % of the total generated (input) energy, and 86.43 % of the energy was used for moisture evaporation, indicating high energy utilization of the drying system. In addition, cost savings of US $11.46–16.84/ton (¥83-122.10/ton) were achieved when MDS was treated via the novel drying technology. Overall, the novel drying technology proposed in this study provides feasible, efficient and cost-saving pretreatment technology and ideas for MDS treatment and disposal engineering.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".