Nonlinear State Estimation and Control of an Organic Rankine Cycle
Bibliographic record
Abstract
Waste heat recovery systems are designed to capture thermal energy from mechanical systems that would normally be transferred to their surroundings.Due to the stochastic nature of waste heat sources, control systems implemented to maintain process setpoints often have issues working with the apparent nonlinear, time-varying system.This work proposes using a Trans-critical Organic Rankine Cycle (TORC), where an organic working fluid is evaporated above its critical point, as a waste heat recovery system.The TORC system in this work is modelled as a 13-dimensional dynamic model with additive gaussian noise.An Extended Kalman Filter (EKF) is implemented to construct a full state estimate given a subset of noisy measurements which can be obtained with conventional sensors.Two different control systems are then implemented on this system.The first, a Cerebellar Model Articulation Control (CMAC), involves a proportional control output and a Neural Network learned output which satisfy the Lyapunov stability criterion.The second, an Iterative Linear Quadratic Regulator (ILQR), uses linearized points along a trajectory with a quadratic cost-function minimizing algorithm to choose control outputs.It was found that both the CMAC and ILQR can reliably track process setpoints and exhibit significantly less drift than linear control methods such as Proportional-Integral-Derivative Control.
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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 | 0.001 | 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".