Thermodynamic Performance of a Cogeneration Plant Driven by Waste Heat from Cement Kilns Exhaust Gases
Bibliographic record
Abstract
The dwindling and scarcity of fossil energy sources is the basis of the energy transition, where renewable resources are increasingly valued. The purpose of the cogeneration system studied in this article is to recover the residual heat from the gases coming out of the chimneys of the cement kilns, to produce at the same time the electricity and the heat required for offices and residential houses of cement workers. Cement kilns are reputed to be energy-intensive, generating excessive heat losses. These heat losses are found mainly in the conduction–convective and radiative modes, representing about 26% of the overall heat input to the system. Nevertheless, the gases at the chimney outlet can still have temperatures between 250 and 350 °C, which presents a non-negligible potential for a cogeneration system. This study compares the thermal performance of different cogeneration plant configurations (KCA, KCB, and KCC systems) using the Kalina cycle to determine the best one. Several assumptions were made to reduce the complexity of the model. MATLAB and Excel software were used to solve the system of equations. After extensive analysis of the results, the KCA system showed the best performance, compared to the KCB and KCC systems, with a thermal efficiency of 22.15%, an exergy efficiency of 45.12%, and a net electrical capacity of 2565.03 kWe. Model sensitivity to concentration, temperature, and pressure variations also gave the KCA system the best-performing system. Evaluation of the excess heat flux removed from the process yields values of 7368.20 kW, 7421.86 kW, and 8094.15 kW for the KCA, KCB, and KCC systems. The results of this article serve as a decision support tool for installing the cogeneration system via the Kalina cycle in cement installations.
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".