Optimising pulp and paper mill sludge through alternate end of life pathways: enabling the transition towards circularity within the pulp and paper industry.
Bibliographic record
Abstract
Globally the pulp and paper industry (PPI) is regarded as one of the most polluting industries in terms of land, air and water pollution, requiring large quantities of process waters. PPI’s process fibres of different origins i.e. virgin fibres, recycled fibres and non-wood fibres. The source of fibre and manufacturing processes employed determines the quantities and composition of wastes generated. Currently most PPIs, globally follow a linear economic model in which wastes generated are landfilled and is not sustainable as we strive towards a shared goal of carbon neutrality. Limited renewable natural resources and landfill airspace are facing increased demands with the latter being non-renewable. Common waste management methods of pulp and paper mill sludge (PPMS) include incineration prior to landfilling, and by enabling a transition towards a circular economy will facilitate the addition of economic value to ‘wastes’ generated. Adopting circularity facilitates the utilisation of alternate pathways by maximising the benefits posed by such resources, simultaneously reducing quantities of waste requiring landfilling. Increased levels of sustainability can be achieved through mitigating the everincreasing demands placed on limited natural resources through reuse and recycling efforts. This research which was a desktop study focused on alternate waste management approaches on PPMS, exploring of alternate end-of-life pathways facilitating landfill diversion, and increase circularity within the PPI. The pathways explored are relevant globally and within a South African context such as the use of PPMS as a soil conditioner and compost, conversion into an energy pellet, anaerobic digestibility, reuse within the pulp and paper industry and mineral based products and as a landfill cover material. They could be used proactively in anticipation of extreme climatic conditions posed by climate change in efforts of reducing our vulnerability to such risks. Circularity within the PPI will allow for the conservation resources like water, soils and wood. Results from this study highlighted the viability of sustainable integrated waste management of the PPI. Pathways explored allow for the PPI shifting away from the cradle-to-grave and towards a cradle-to-cradle approach of the PPI’s wastes. The results also displayed great potential in integrated waste management systems with benefits posed to economic, social and environmental spheres.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".