A novel approach for applied membrane filtration on processing flows
Bibliographic record
Abstract
Membrane filtration technique shows exclusively properties for optimized filtration for a given size, from micrometer to nanometer scales compared to other industrial filtration. However, there is a trade off in flux that has delayed the implementation of the membrane techniques for a wider range of applications. For a specific flow in a process stream, the limited flux through the membrane means large membrane area is demanded, that results in a return of investments not accepted by industry. On the other hand, as the energy prices increases and environmental issues become more and more important as design parameters, the membrane techniques will due to its excellent separation performance be of interest, and from a life cycle analysis (LCA) a feasible approach.In this project the process flows in pulp and paper sector, and in particular the waste water treatment, are studied based on membrane technology. The purpose is to obtain a procedure to evaluate the membrane technique so that the results are guiding for extended studies. Two different representative integrated mills have been in focus, one thermo-mechanical pulp mill and one kraft pulp mill. The waste water streams that result in challenges for treatment process and that due to membrane filtration give reject / accept of greater values are studied. The possible benefit is two-fold the waste can be used as a resource of added valued products and the energy demanding waste water treatment decreases.A membrane filtration equipment from Alfa Laval LabStak M20 was used in this study on different streams for a pulp and paper mill. In this unit several membranes can be tested at the same time in order to briefly evaluate a type of membrane which suitable for the prevailing conditions, ranging from RO, NF, UF and MF depending on molecular weight cut-off (MWCO) and pore size. The volume concentrations factor (VFC) ranged from 1-4 in this study and the reduced TOC indicates satisfactory results. As pointed out, a trade off in decrease flux during filtration was in the order of 20%, to be considered in process design. The result gives indication of feasible way to use membrane filtration techniques as a tool for creating new business opportunities and elucidate the potential in reducing the water and energy consumption. The technique is demonstrated to gained parameters of importance for the pulp and paper sector. However, also applicable for other industry sectors when dealing with large scale processing flows
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".