Green Manufacturing of Electricity for Stationary Industrial Applications
Bibliographic record
Abstract
On-site power generation in industrial plants are desirable, however, this may not contribute as a significant source of pollution to the environment. In order to lower the potential impact on the environment in terms of less toxic emissions, to save useless cost and the sustainability, manufacturer’s responsibilities become more than end-of-pipe control and includes end-of-life management. Green manufacturing is the method that minimizes waste and pollution achieved through product and process life cycle design. The cradle to grave analysis in green manufacturing system provides the complete analysis in every aspect of the sustainable manufacturing system for policy makers to take decisions. In this paper the renewable source of alternate energy manufacturing system has been analyzed for production of power. The renewable energy from hydrogen and (green gas synthetic natural gas) for generation of electricity focusing large stationary application set up has been studied. A prototype model has been developed in order to draw analogy for establishing future Industrial power parks of mega energy productions in order to meet the peak load requirements of the electricity consumption. The results has been analyzed and comparison have also been made for the purpose to apply green industrial manufacturing process in renewable energy sector as much as possible for reducing waste and with zero potential environmental burden on our Eco-system.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".