Characterization of industrial waste from a natural gas distribution company and management strategies: a case study of the East Azerbaijan Gas Company (Iran)
Bibliographic record
Abstract
Although a fundamental prerequisite for the successful implementation of any waste management plan is the availability of sufficient and accurate data, there are few available studies regarding the characterization and management of gas distribution company waste (GDCW). This study aimed to characterize the industrial waste generated by the East Azerbaijan Gas Distribution Company (EAGDC) and to present environmental management strategies. The EAGDC serves 57 cities and 821 villages with a total population of more than 2.5 million as well as numerous industrial units. The methodology of this study was based on a checklist of data collected from each zone of the company, site visits (observation), and quantity and quality analysis according to the formal data available from different zones. The results indicate that more than 35 different kinds of industrial solid waste are generated in different industrial installations. The most important types of generated waste include empty barrels (including mercaptans, diesel fuel, deionized waters and oil), faulty gas meters and regulators, a variety of industrial oils, sleeves, filter elements and faulty pipes, valves and fittings. The results indicated that, currently, GDCW is generally handled and disposed of with domestic waste, deposited in companies' installation yards and stores or, sometimes, recycled through non-scientific approaches that can create health risks to the public and the environment, even though most of the GDCW was determined to be recyclable or reusable materials. This study concludes that gas distribution companies must pay more attention to source reduction, recycling and reusing of waste to preserve natural resources, landfill space and the environment.
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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 | 0.005 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".