23 research outputs found
Model predictive control of a fluid catalytic cracking unit with estimation of key parameters
No full textLe craquage catalytique à lit fluidisé (FCC) est l'un des procédés les plus importants au sein d'une raffinerie moderne et joue un rôle économique primordial. Le fonctionnement du FCC pose des problèmes d'opération liés à sa complexité. L'étude a porté sur la simulation du FCC, sa commande prédictive multivariable et l'estimation de paramètres-clés. Après une revue de la littérature sur les FCC et les différentes approches de modélisation ainsi que des cinétiques de craquage, un modèle du FCC qui intègre les dynamiques importantes a été choisi pour les besoins de la commande prédictive. La simulation du riser a été effectuée pour différents modèles de craquage et a montré de grandes disparités entre modèles, créant une difficulté à définir un modèle général de riser pour les FCC. Outre le nombre de groupes considérés, les différences concernent la chaleur de réaction globale, les lois de formation de coke sur le catalyseur et la désactivation de ce dernier. Des algorithmes de commande prédictive linéaire et non linéaire basée sur le modèle ont été utilisés pour commander le FCC en tenant compte de sa nature multivariable et des contraintes imposées aux variables manipulées. Les sorties commandées, température en haut du riser et température du régénérateur ont été maintenues proches des consignes, tant en régulation qu'en poursuite, tout en respectant les contraintes portant sur les deux variables manipulées, le débit de catalyseur régénéré et le débit d'air entrant dans le régénérateur. Une commande à trois entrées manipulées, incluant le débit d'alimentation, a également été testée avec succès. La commande prédictive linéaire avec observateur a fourni des résultats encore meilleurs que la commande linéaire quadratique. La commande prédictive non linéaire a été testée mais présente des problèmes pour une implantation en temps réel. L'estimation du coke sur le catalyseur a été réalisée par le filtre de Kalman étendu, mais les erreurs d'estimation sont importantes, probablement à cause du choix insuffisant des mesures effectuées. L'ensemble de l'étude a montré que la commande avancée prédictive du FCC est performante et doit être recommandée, mais peut encore être améliorée en particulier par son réglage et l'estimation des étatsFluid catalytic cracking (FCC) is one of the most important processes in a modern refinery and is of essential economic importance. The FCC operation presents difficulties related to its complexity. The study was related to its simulation, multivariable control and estimation of key parameters. After a litterature review of the FCC, the different approaches of modelling and cracking kinetics, a FCC model that takes into account the important dynamics was chosen for model predictive control purposes. The riser simulation was carried out for different cracking models and shows great differences between these models, which makes it difficult to define a general riser model for the FCC. Besides the number of lumps, differences deal with the global heat of reaction, the coke formation laws and its deactivation functions. Linear and nonlinear model predictive algorithms were used for FCC control taking into account its multivariable nature and the constraints imposed on the manipulated variables. The controlled outputs, temperature at the riser top and temperature in the regenerator were maintained close to their respective set points in regulation and tracking modes while respecting the constraints on the two manipulated variable, the flow rate of regenerated catalyst and the flow rate of air entering the regenerator. A control with three manipulated variables including the feed flow rate was also successfully tested. Linear predictive control with an observer gave better results than linear quadratic control. Nonlinear predictive control was tested but presents problems for real time implementation. The estimation of coke on the catalyst was carried out using extended Kalman filter, but the estimation errors are important, probably due to an insufficient choice of measurements. The overall study showed that advanced predictive control of the FCC is efficient and must be recommended, but it can still be improved upon particularly by its tuning and state estimatio
Commande prédictive d'un craqueur catalytique à lit fluidisé avec estimation des paramètres clés
No full textFluid catalytic cracking (FCC) is one of the most important processes in a modern refinery and is of essential economic importance. The FCC operation presents difficulties related to its complexity. The study was related to its simulation, multivariable control and estimation of key parameters. After a litterature review of the FCC, the different approaches of modelling and cracking kinetics, a FCC model that takes into account the important dynamics was chosen for model predictive control purposes. The riser simulation was carried out for different cracking models and shows great differences between these models, which makes it difficult to define a general riser model for the FCC. Besides the number of lumps, differences deal with the global heat of reaction, the coke formation laws and its deactivation functions. Linear and nonlinear model predictive algorithms were used for FCC control taking into account its multivariable nature and the constraints imposed on the manipulated variables. The controlled outputs, temperature at the riser top and temperature in the regenerator were maintained close to their respective set points in regulation and tracking modes while respecting the constraints on the two manipulated variable, the flow rate of regenerated catalyst and the flow rate of air entering the regenerator. A control with three manipulated variables including the feed flow rate was also successfully tested. Linear predictive control with an observer gave better results than linear quadratic control. Nonlinear predictive control was tested but presents problems for real time implementation. The estimation of coke on the catalyst was carried out using extended Kalman filter, but the estimation errors are important, probably due to an insufficient choice of measurements. The overall study showed that advanced predictive control of the FCC is efficient and must be recommended, but it can still be improved upon particularly by its tuning and state estimationLe craquage catalytique à lit fluidisé (FCC) est l'un des procédés les plus importants au sein d'une raffinerie moderne et joue un rôle économique primordial. Le fonctionnement du FCC pose des problèmes d'opération liés à sa complexité. L'étude a porté sur la simulation du FCC, sa commande prédictive multivariable et l'estimation de paramètres-clés. Après une revue de la littérature sur les FCC et les différentes approches de modélisation ainsi que des cinétiques de craquage, un modèle du FCC qui intègre les dynamiques importantes a été choisi pour les besoins de la commande prédictive. La simulation du riser a été effectuée pour différents modèles de craquage et a montré de grandes disparités entre modèles, créant une difficulté à définir un modèle général de riser pour les FCC. Outre le nombre de groupes considérés, les différences concernent la chaleur de réaction globale, les lois de formation de coke sur le catalyseur et la désactivation de ce dernier. Des algorithmes de commande prédictive linéaire et non linéaire basée sur le modèle ont été utilisés pour commander le FCC en tenant compte de sa nature multivariable et des contraintes imposées aux variables manipulées. Les sorties commandées, température en haut du riser et température du régénérateur ont été maintenues proches des consignes, tant en régulation qu'en poursuite, tout en respectant les contraintes portant sur les deux variables manipulées, le débit de catalyseur régénéré et le débit d'air entrant dans le régénérateur. Une commande à trois entrées manipulées, incluant le débit d'alimentation, a également été testée avec succès. La commande prédictive linéaire avec observateur a fourni des résultats encore meilleurs que la commande linéaire quadratique. La commande prédictive non linéaire a été testée mais présente des problèmes pour une implantation en temps réel. L'estimation du coke sur le catalyseur a été réalisée par le filtre de Kalman étendu, mais les erreurs d'estimation sont importantes, probablement à cause du choix insuffisant des mesures effectuées. L'ensemble de l'étude a montré que la commande avancée prédictive du FCC est performante et doit être recommandée, mais peut encore être améliorée en particulier par son réglage et l'estimation des état
Multivariable control and online state estimation of an FCC unit
No full textInternational audienceThe purpose of this paper is to realize multivariable control , tuning and online state estimation of some parameters of the FCC unit . We implemented two control structures with the manipulated variables being the air inlet flow rate in the regenerator, the regenerated catalyst flow rate and the feed flow rate and, the controlled variable being the temperatures in the riser and in the densed bed of the regenerator. A novel four transfer function is built and used for controllability studies. Hard constraints are imposed with respect to the manipulated variables. Simulation results show that the configuration made of two inputs and two outputs is more easy to tune for control purposes. Althought there are important dynamic interactions between the components of the FCC and important nonlinearities, linear model predictive control is able to maintain a smooth multivariable control of the plant, while taking into account the different constraints. Tuning strategy is implemented to improve the tracking of the set point. Online state estimation is carried out with the use of the extended Kalman filter. The estimation gives results that can be used for monitoring purposes even in the presence of model mismatc
Optimal Reconfiguration of Power Distribution Systems Based on Symbiotic Organism Search Algorithm
No full textStates and Parameters Estimation for Induction Motors Based on a New Adaptive Moving Horizon Estimation
No full textThis paper investigates the joint states and parameters estimation problem for induction machine. In order to develop new states and parameters estimation methods that greatly improve the estimation bandwidth, this paper proposes an adaptive moving horizon estimation of the crucial states and parameters of the induction machine. The model of the machine under study is the one taking into consideration the magnetic saturation and the iron losses simultaneously. The estimator used is based on a least squares algorithm but includes a dead zone that ensures robustness and a variable forgetting factor that is based on the constant information principle. The simulation results show that the adaptive estimator can efficiently estimate the states and parameters of the induction machine with a fast convergence rate despite the initial parametric errors
Modeling and optimization of turmerone concentrations from fermented turmeric waste essential oil via ANN and statistical techniques
No full textTurmeric (Curcuma longa L.) pulp waste is an underutilized byproduct rich in essential oils components, particularly turmerone, which has high therapeutic and commercial value. Despite its potential, its recovery from such waste has not been extensively explored. With fermentation – solid-state fermentation (SSF) as a preliminary process to increase yield with lower cost, the present study focused on predicting and optimizing the turmerone concentration in essential oils extracted from turmeric waste through spontaneous fermentation via two modeling approaches: artificial neural networks (ANNs) and response surface methodology (RSM). A central composite rotatable design (CCRD) was employed to assess the influence of two key variables, namely fermentation time and hydrodistillation duration, on the response turmerone concentration. An evaluation of the models through the coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), mean percentage error (MPE), and Pearson's χ2 revealed that the ANN was superior (R2= 0.9952, MSE= 0.14, RMSE= 0.40, MPE= 0.20 and χ2 = 0.02) to the RSM model (R2= 0.9894, MSE= 0.19, RMSE= 0.44, MPE= −0.09 and χ2 = 0.12) in terms of prediction accuracy and estimation reliability. The performance advantages of the RSM model (MAE = 0.24 and MAPE = 2.32) over the ANN (MAE = 0.39 and MAPE = 3.07) are not significant enough to outweigh the ANN's overall superiority. On the basis of ANN predictions, the optimal process conditions were determined to be 10 days of fermentation and 2 h of hydrodistillation, leading to a maximum predicted turmerone concentration of 16 millimolar (mM). Furthermore, the statistical analysis highlighted the distillation time as the most influential factor on the turmerone concentration. This study shows ANN modeling enhances turmeric waste valorization and highlights fermentation’s significant impact on turmerone recovery efficiency in extraction cycles
Multivariable control and online state estimation of an FCC unit
No full textInternational audienceThe purpose of this paper is to realize multivariable control , tuning and online state estimation of some parameters of the FCC unit . We implemented two control structures with the manipulated variables being the air inlet flow rate in the regenerator, the regenerated catalyst flow rate and the feed flow rate and, the controlled variable being the temperatures in the riser and in the densed bed of the regenerator. A novel four transfer function is built and used for controllability studies. Hard constraints are imposed with respect to the manipulated variables. Simulation results show that the configuration made of two inputs and two outputs is more easy to tune for control purposes. Althought there are important dynamic interactions between the components of the FCC and important nonlinearities, linear model predictive control is able to maintain a smooth multivariable control of the plant, while taking into account the different constraints. Tuning strategy is implemented to improve the tracking of the set point. Online state estimation is carried out with the use of the extended Kalman filter. The estimation gives results that can be used for monitoring purposes even in the presence of model mismatc
Multivariable control and online state estimation of an FCC unit
No full textInternational audienceThe purpose of this paper is to realize multivariable control , tuning and online state estimation of some parameters of the FCC unit . We implemented two control structures with the manipulated variables being the air inlet flow rate in the regenerator, the regenerated catalyst flow rate and the feed flow rate and, the controlled variable being the temperatures in the riser and in the densed bed of the regenerator. A novel four transfer function is built and used for controllability studies. Hard constraints are imposed with respect to the manipulated variables. Simulation results show that the configuration made of two inputs and two outputs is more easy to tune for control purposes. Althought there are important dynamic interactions between the components of the FCC and important nonlinearities, linear model predictive control is able to maintain a smooth multivariable control of the plant, while taking into account the different constraints. Tuning strategy is implemented to improve the tracking of the set point. Online state estimation is carried out with the use of the extended Kalman filter. The estimation gives results that can be used for monitoring purposes even in the presence of model mismatch
