Innovative Multigeneration System with Heat Exchangers for Harnessing Thermal Energy from Cement Kiln Exhaust Gases
Bibliographic record
Abstract
This article introduces a novel multiple-cycle generation system for efficient heat recovery at high and low temperatures. The system is modeled and optimized using the M2EP analysis method (mass, energy, exergy, and performance) and the particle swarm optimization algorithm. The multigeneration system produces electricity, cold, domestic hot water, and biogas by utilizing Kalina cycles, diffusion–absorption refrigeration machines, and high-performance heat exchangers by harnessing waste heat from cement kiln exhaust gases. The Kalina cycle is employed for electricity generation, wherein the H2O+NH3 mixture, heated by hot water, circulates through heat exchangers. Downstream of the Kalina cycle, the refrigeration machine generates cold by evaporating the strong solution of the H2O+NH3 mixture. Hydrogen circulates in the diffusion–absorption refrigerator (DAR) circuit, facilitating the exchange between the evaporator and the absorber. The domestic hot water and biogas production systems operate at lower temperatures (around 45 °C). The simulation results for the Kalina cycle indicate an electrical energy production of 2565.03 kW, with a release of usable energy (residual gases) estimated at 7368.20 kW and a thermal efficiency of 22.15%. Exergy destruction is highest at heat exchanger 1, accounting for 26% of the total. A coefficient of performance of 0.268 and an evaporator temperature of 10.57 °C were obtained for the DAR cycle. The absorber contributes the most to energy exchanges, comprising 37% of the entire circuit. Summarizing the potential for valorizing waste heat from cement kilns, this article lays the foundation for future research.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".