Dynamic Analysis of the Solid-Gas Thermochemical Heat Transformers for Industrial Heat Recovery
Bibliographic record
Abstract
A significant portion of the energy supplied to the industries is dissipated as low, medium, or high-grade waste heat.In the sake of improved energy efficiency and sustainability in the industrial sectors, significant recovery and heat upgrading measures for these waste heat streams are needed.Thermochemical heat transformers (THTs) have emerged as a promising sort of technological solutions for upgrading waste heat streams in industry.Among various types of THTs, solid-gas (SG) species reacting type provides significant advantages, including a higher temperature lift, greater heat storage capacity, and scalability.This study presents a dynamic model of an innovative SG-THT technology under development for waste heat upgrading for process heating applications in the range of 200-300, using SrBr2.H2O as the working pair due to its stable chemical properties and high energy density.The system is programmed in Modelica and dynamically simulated to track its chemical reactions within the hydration and dehydration reactors.The results indicate the maximum temperature lift of the system to be 84.59under the considered realistic operational conditions at an overall thermal energy efficiency of 66.65%.The results indicate that the proposed SG-THT system can show a satisfactory performance in transient conditions such as fluctuating off-design loads, start-up, and shutdown demonstrating its capability for making a potential role in the industrial sector decarbonization.This dynamic simulation provides important information for designing the system more effectively to reach better efficiency levels, reducing capital and operational costs, and cope better with use case dynamics.
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".