Adapted Time Slice Model of Pinch Analysis for Direct-Indirect Heat Recovery in Buildings
Bibliographic record
Abstract
Heat integration techniques such as pinch analysis can play a significant role in saving energy in the design of buildings. The application of pinch analysis in this sector encounters difficulties due to the highly time-dependent behavior of energy streams such as waste heat and solar thermal collectors, as well as the possible need for heat storage units (HSUs). The existing pinch models in the literature either bear little relation to reality because they ignore the time dependency of the streams, or they do not respect the pinch analysis minimum temperature difference of the system, which leads to temperature penalties. This study introduces a novel and straightforward adapted time slice model of pinch analysis, beneficial for energy targeting in buildings. First, an algorithm for the selection of the appropriate time slice duration is proposed. Then, additional steps are embedded in the conventional problem table algorithm to account for both direct heat transfer (co-existing streams) and indirect heat transfer (time mismatched streams requiring thermal energy storage). i.e., the modified table includes both external and internal streams, respectively. The detailed application of the proposed model is demonstrated through the analysis of a direct-indirect heat recovery system for a residential test building equipped with waste heat and solar energy, considering a summer’s day. This case study, or sample calculation, determines the HSU specifications, including their design temperatures and volumes. A heat exchanger evaluation quantifies both their number and their thermal conductances, which are an economic indicator of the system capital cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".