The Effect of Using Bioflocculants as Conditioners on Dewatering of Biosludge
Bibliographic record
Abstract
Chemical conditioning by biopolymers, as an alternative for petroleum-based synthetic polymers, for improving the dewatering of biosludge from pulp and paper mills is a growing interest. There are limited studies on the application of biopolymers for enhancing the dewaterability of biosludge and the underlying mechanisms in this process. This thesis explored the possibility of employing biopolymers such as hemoglobin (Hb), lignin-based flocculants (LBFs), and protamine to enhance the dewaterability.Cationic polymers improve the dewaterability of sludge by neutralizing the negative charge of the biosludge particles. Biopolymers such as hemoglobin and lignin do not possess high charge density naturally. As a result, they only exhibit a positive effect on dewaterability after modification through different processes. Hemoglobin from animal blood was methylated to replace the hydrogen carboxyl groups in its structure to become more positively charged. Lignin from pulp manufacturing processes was also modified via free radical polymerization with acrylate-based monomers. Dewaterability was assessed based on the dry solids content after treatment and pressing by Crown press. Capillary suction time (CST) was also used as water separation rate indicator. Only after modification, by using 10% of methylated hemoglobin, the dry solids content of biosludge increased by 5%. Similarly, LBFs considerably decreased the CST by nearly 50 seconds from 73s to 23s and increased the dry solids content by 7% with a relatively high dose of 7.5%. The potential of using dual conditioning method by combining cationic biopolymers such as protamine and LBFs with a small amount of a synthetic anionic polyacrylamide (APAM) was also investigated to lower the dosage required for biopolymers. Dual conditioning provided significant synergistic effect resulting in increasing the solids content of biosludge by 9% and lowering the amount of protamine addition substantially to 2%. In a similar way for LBFs, the biopolymer demand decreased from 7.5% to around 3% by using a dual conditioning system. The cationic biopolymer reduced the negative charge on the particles, allowing smaller particles to agglomerate and provided a positively charged framework for the addition of the APAM. Significant floc-bridging occurred after the addition of APAM, allowing smaller flocs to aggregate into larger flocs.
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) | 0.000 | 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) | 0.001 | 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".