Early warning generation for process with unknown disturbance
Bibliographic record
Abstract
Process safety has paramount importance in a chemical process. A well designed control system is the first layer in a process system. The warning system works as the upper protection layer above the control system. It alerts the operators when the control system fails to prevent an undesired situation. A typical warning system issues warnings when a monitored variable exceeds the threshold. Often these do not allow operators sufficient lead-time to take corrective actions. With the motivation of improving the operator’s working environment by providing lead-time, the current research develops a predictive warning scheme using a moving horizon technique. The main hypothesis proposed in this thesis is given the current state of process system, the future states of the system can be predicted using a suitable model of process system. If an external input disturbs the system state, the controller will try to bring the system within the desired control/safety limits of the system. A warning is issued if it is determined that the control system will not be able to keep the system withing the safety limits. Based on the hypothesis, warning systems were developed for both linear and nonlinear systems. For linear systems, using the gain of the models, a linear constrained optimization problem was formulated. Linear programming (LP) was used to determine if the system will remain within the safety limits or not. In case the LP determines that there is no feasible solution within the constrained limits, warnings are issued. The predictive warning scheme was also extended for nonlinear systems. A non-linear receding horizon predictor was used to predict the future states of the nonlinear system. However, for nonlinear system formulation leads to nonlinear constrained optimization problem, where the constraints are the safety limits. Controller’s ability to keep the predicted states inside the safety limit was checked using a feasibility test algorithm. The algorithm uses a constraint separation method with weighting functions to determine the existence of a feasible solution. The algorithm calculates the global minimum of the objective function. If the global minimum of the objective function is positive, it signifies no feasible solution within the input and output constraints of the system and a warning is issued. Prediction of the effect of the disturbances requires the knowledge of the disturbances. In process industries, disturbances are often unmeasured. This thesis also investigates the estimation of unknown disturbances. An iterative Expectation Minimization (EM) algorithm was proposed for the estimation of the unknown states and disturbances of nonlinear systems. Efficacy of the proposed methods was shown through a number of case studies. The warning system for the linear system was simulated on a virtual plant of a continuous stirred tank heater (CSTH). The nonlinear warning system was implemented on a continuous stirred tank reactor (CSTR). Both case studies showed that, the proposed method was capable of providing a warning earlier than the traditional methods that issues warning based on the measured signals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".