An integrated approach to explore UASB reactors for energy recycling in pulp and paper industry: a case study in Brazil
Bibliographic record
Abstract
Brazil is currently focused on its energy matrix transition in favor of increasing of the share of renewable energy carriers for both enhanced energy security and mitigation of greenhouse gas emissions. In this context, the country`s pulp and paper industry whose different wastes teams are not generally exploited, could play a critical role. Accordingly, the main objective of this work is to develop a conceptual ‘systemic’ biorefinery framework integrating the treatment of pulp and paper mill wastewater using upflow anaerobic sludge blanket (UASB) reactor with energy recovery through biogas production and its conversion into heat and power in stationary engines and boilers, respectively. Based on the results obtained through the present case study, it was revealed that the adoption of UASB reactors by the paper mill industry could properly addresses the environmental concerns faced while could contribute to the national agenda favoring an increasing share of renewable energies in the country`s energy matrix. The financial analysis showed that the investment required for the implementation of UASB reactors within a biorefinery platform would be minor vs. the investment in the whole mill and would be returned in 6.4 yr with a high return on investment even when operated at half of operational capacity. Moreover, through the developed UASB reactor-based biorefinery, the Brazilian pulp and paper industry as a whole could avoid 1.06 ×105 CO2eq tons, effectively contributing to the decarbonization of the country`s economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".