An Empirical Model for Industrial Generator’s Capacity Requirement Determination
Bibliographic record
Abstract
In our community today, the existence of Power Holding Company of Nigeria can only help for a short period when it is available. In some areas, it is not available at all. Therefore, there is always need for generator as back up or continuous use in our industries. Determination of capacity of generator to procure is always a problem. Some company by error purchased generators that cannot carry the load of their industries. This always led to load shed either on machines or the entire facilities they have. This is due to the fact that the capacity of the generator required was not predetermined and also the expansion of the companies in the nearest future was not considered. This had contributed to the low productivity of many companies because of their inability to meet their monthly as well as yearly production targets. Hence the development of a model for the appropriate generator capacity selection for industrial installation which is empirically oriented. Developing an empirical model for this selection involves adequate understanding of electrical load distributions, variations and utilities connected to the electrical load of the generator. Parameters for industrial generator capacity were identified, mathematical model for each parameter were determined and integrated to form a unique model for decision making. The identified parameters are: capacity utilization, diversity factors, deration factor and usage type. The scenarios for computation were three based on the type of load required. This load were identified to be existing load, new and future loads. The developed models were applied using Honeywell foods (FMCG) company as case study under the first scenario. The load analysis for both the non-factory and factory load gave Summation of 531.47kW with power factor of 0.8 gave a converted value of 664.34kVA. The total variation factor gotten is 0.765 with 0.85 capacity utilization factor and diversity factor was 0.9. Application of total variation factor gave the converted load of 664.kVA and new load value of 508 kVA. Using power factor of 0.8 resulted into 406kW the generator considerations were derating factor of 0.75 and usage type factor (which is continuous) is 1 or 100%. The final determined generator capacity for this case study using derating factor of 0.75 made the required capacity to be 677kVA, and 542kW.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".