SYNTHESIS OF A NOVEL ROBUST INTELLIGENT CASCADED REACTIVITY CONTROLLER FOR CANDU REACTOR
Bibliographic record
Abstract
In this paper, a Novel Intelligent Cascaded Reactivity Controller (NICRC) is synthesized for a CANadian Deuterium Uranium (CANDU) -type Nuclear Power Plant (NPP) operating in Pakistan. The designed NICRC is a cascaded configuration of reactor power and moderator level controllers composed of five sub-controllers. The proposed NICRC is designed using intelligent soft computing technique. The original reactivity controller of CANDU nuclear power plant is a networked controller implemented on a Programmable Logic Controller (PLC). The new NICRC is designed based on two Intelligent Distributed Cascaded Power Controller (IDCPC) and Intelligent Cascaded Moderator Level Controller (ICMLC) for moderator level control in CANDU reactor core. The IDCPC is composed of three neural sub-controllers while ICMLC is composed of two neural sub-controllers. The proposed NICRC is designed using Adaptive Back Propagation Feedforward Neural Network (ABPFNN). The proposed controller is synthesized in a distributed parallel computing environment using MATLAB. The proposed NICRC is formulated in a highly complex multi-objective neural form and evaluated against full power operation of CANDU nuclear power plant from cold start-up to high power. The performance of highly robust NICRC is tested and evaluated for a typical transient providing complete coverage of moderator level, low log and steam pressure modes of CANDU reactor and found excellent within the desired control bands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".