Environmental life cycle assessment of different types of the municipal wastewater pipeline network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With the onset of social life, humans have considered waste disposal as essential, and they have been able to repel it through brick and clay channels. Checking sewage pipes for energy consumption and a longer lifetime than other sewage system components is important. Climate change and exploitation of industrial resources have made environmental impacts, which are important factors in decision making. The purpose of this study was to introduce the most suitable type of sewage pipe considering environmental protection. Therefore, we applied the environmental life cycle assessment (LCA) method, using Sima Pro 8.2.3 software for the one‐kilometer length of concrete pipes (300 mm in diameter), Polyvinyl chloride (PVC), and polyethylene (PE) (315 mm in diameter). Also, the BEES method and sensitivity analysis were used to validate the results. The comparison between three types of municipal wastewater pipes indicated that PE pipes are a more environmentally friendly option than PVC, and concrete pipes in pipe recycling, reducing extraction from untapped resources, and inefficient extraction of resources. Electricity, diesel fuel, and sulfate resistance cement consumption for concrete production are the most pollution elements in the LCA of concrete pipes. Usage of PVC granular, sanitary landfill of PVC pipes, and using hydraulic drill in LCA of PVC pipes are the most elements of generating pollution. The usage of PE granules, PE pipes landfilling, hydraulic excavator, and electricity consumption in the LCA of the PE pipes are the greatest polluting parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it