Pharmaceutical Waste Water Treatment & Related Economic Aspects for Reuse in the Context of Bangladesh
Bibliographic record
Abstract
The purpose of the work is to investigate the scenario of waste water treatment in pharmaceutical industries in Bangladesh and find some advanced steps to add in the existing ETP with economic considerations. For this, a renowned pharmaceutical industry in Bangladesh has been chosen that represents the feature of ETP in Bangladesh. Generally industries, including pharmaceuticals, discharge their waste water to the environment after treatment; the objective is to show some beneficial ways to use/reuse the treated water, applicable for the pharmaceutical industries in Bangladesh. Sample has been provided by ACI Pharmaceuticals Ltd. and different parameters of their supplied samples (both from inlet and outlet) have been measured to check whether they suit with the present standards of drinking water and/or other standards of industrial discharged water in context of Bangladesh. From the experiment it is found that the values of the different parameters of the treated water of the chosen industry can be considered to be favorable in comparison with the present standards of waste water regulations in Bangladesh. The present investigation also shows that if an ion exchange unit or membrane separation technology is incorporated after filtration, the waste water can be made potable after treatment by implementing some advanced technology DOI: http://dx.doi.org/10.3329/jce.v27i2.17801 Journal of Chemical Engineering, IEB Vol. ChE. 27, No. 2, December 2012: 46-49
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".