Early warning generation for process with unknown disturbance
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».