Unit simulation that combines heat production and power applied to an industrial spray dryer
Bibliographic record
Abstract
The need to improve energy efficiency in industry plays an important role in minimizing climate change. Most thermoelectric plants are used to generate electricity due to their production flexibility, being used to balance energy supply and demand. The main problems with thermoelectric plants are low efficiency and high heat rejection. We worked with the proposal to generate electrical energy on-site using a thermal machine (gas turbine), and the rejected heat was used to heat the drying air of a Spray Dryer (SD). The performance of a conventional SD, which uses a burner with liquefied petroleum gas (LPG), was compared with an SD that uses a gas turbine to generate electricity and the rejected heat to heat the drying air, replacing the burner. The calculations were carried out using the iiSE Process Simulation process simulator, and the operational cost of the SD was calculated considering the prices of electrical energy and thermal energy (gas) for the ten largest economies in the world. The simulations observed that LPG consumption was 22.1% higher than the proposed alternative (SD with gas turbine). Conversely, 2.0 kW of electrical energy was produced per kg of LPG consumed, while a conventional SD does not produce electrical energy. Comparing the operational course, the system becomes viable in countries where the cost of electrical energy is higher than thermal energy, such as the United Kingdom, Italy, and Canada, and less viable in countries where electrical energy has similar costs to thermal energy, such as Brazil, China, and India.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".