Development of a Simulation Program to Optimise Process Parameters of Steam Power Cycles
Bibliographic record
Abstract
Conventional coal-based thermal power plants have an average overall efficiency in the range of 35-38 %. Any increase in the percent efficiency of these power plants, is subjected to constraints posed by maximum and minimum temperatures, which are restricted by the creep property of materials and ambient temperature, respectively. Hence, an increase of efficiency beyond certain limits is not possible without optimising the process parameters associated with reheat and regenerative cycles. In this work, an attempt is made to optimise reheat and regenerative cycle process parameters such as, reheat pressure, tapping pressure of bled steam, and mass fraction of bled steam, in order to achieve maximum cycle efficiency. The optimisation of the process parameters was achieved by developing a simulation program using Microsoft Visual Studio. This program takes into account isentropic efficiencies of turbines and pumps and pressure drop in the boiler, and it can be used to simulate the optimum operating conditions of multi-stage reheat & regenerative cycle based thermal power plants. A comparison between the efficiencies of eight kinds of steam power cycles, at optimised conditions, has been made for different boiler pressures and steam temperatures at the turbine inlet. This comparison can aid power plant designers in choosing appropriate steam power cycles for a given set of operating conditions. It is observed that the results obtained from the program, such as, the optimum reheat pressures for two stage reheat cycles and optimum bled steam tapping pressures for two stage regenerative cycles are in good agreement with the published literature.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".