Optimization of energy system retrofitting in urban industrial symbiosis: A case study of heat recovery from a data center
Bibliographic record
Abstract
As the effects of climate change increase, building energy system retrofits become increasingly essential for reducing global emissions. This study introduces an optimization tool designed to assist energy system designers in selecting the most efficient heating options for retrofitting buildings, while also enabling decision-makers to understand the financial and environmental implications of various solutions. The methodology employs a series of mathematical models optimized sequentially to determine optimal design parameters, including system type, sizing, and the design of heat recovery networks. Validation is provided through a case study in Montreal, Canada, focusing on retrofitting the heating system of a university campus building located near a data center. Results indicate that campus greenhouse gas emissions (Scopes 1 and 2, along with embodied emissions) can be reduced by 95 %, with only a 11 % increase in annual costs compared to the current operation. This significant emission reduction is attributed to a shift from natural gas to excess heat recovered from the data center, which can meet 74 % of the heating demand. Additionally, sensitivity analyses are conducted to identify key parameters that can significantly impact financial outcomes. The study also addresses implementation considerations, helping stakeholders remain aware of potential limitations. • Introducing a hybrid optimization model for urban heating system retrofitting. • Decarbonizing building heat demand through industrial excess heat recovery. • Demonstrating the potential for 95 % annual emissions reduction in a real case study.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".