Experimental Fuzzy Modelling and Control of a Steam Power Plant Boiler
Bibliographic record
Abstract
Increasing the use of electricity and the need for more and safer power generation has motivated investigation into new control methods resulting in better performance. Better system performance means increase in power generation efficiency, also decrease in the maintenance costs. To design suitable controllers, adequate information about the system dynamics is required, which in turn has motivated the methods of system identification and simulation studies of power plants. In this paper, simple first-order models are developed for the subsystems of a subcritical once-through boiler, based on the principles of thermodynamics and energy—mass balance, together with parameter estimation routines. These routines are applied on the experimental data obtained from a complete set of field experiments. However, since most processes in a boiler are categorized as multi-input and multi-output systems, mathematical boiler models, which are derived from physical structure and parameters estimation routines, lead to a time-consuming procedure, and employing such models in control algorithms becomes very complex. Therefore, to improve the dynamics modelling, a concise multilayer neuro fuzzy model of the boiler is developed. Next, these two models are compared based on the performance of the real system. This comparison validates the accuracy of both original and neuro fuzzy models, while the latter can be successfully employed in simulation studies, and to design modern model-based control systems. Finally, a new Fuzzy P2 ID controller is developed to use for superheaters temperature control. Simulation results show very good performance of this controller in terms of more accurate and less fluctuation in the temperature of corresponding subsystems, compared to the existing classic controllers.
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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 | 0.001 |
| 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) | 0.001 | 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".