Unscented Kalman Filter. Application of the robust approach to polymerization processes

The control of polymerization processes has central importance because operational conditions affect the processing and end-use properties of the product. The nonlinear controllers based upon rigorous models make use of the on-line state estimates obtained from the available measurements. For polymerization processes, the Unscented Kalman Filter has shown a rewarding performance for state estimation. Because the presence of outliers distorts the behaviour of the filter, Robust Statistics-based approaches have been proposed to reduce their detrimental effect on variable estimates. Until now, only Huber type M-estimators have been used as loss function of the estimation problem. In this work, the ability of other types of M-estimators to improve estimate robustness without introducing numerical problems is analysed. The performances of the M-estimators are compared for a copolymerization process within the framework of a filtering technique based on the Unscented Transformation, which uses a reformulation of the covariance of measurements errors.Fil: Tupaz Pantoja, Jhovany Alexander. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; ArgentinaFil: Asteasuain, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentin

Efficient and robust state estimation: Application to a copolymerization process

Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance for non-linear state estimation. Besides, filters based on robust statistics have been proposed to deal with the presence of outliers. However, reported robust filters have employed only the Huber M-estimator as the loss function of the estimation problem. This work presents a new state-estimation procedure based on the unscented transformation and robust statistics concepts. When outliers are present, estimates are more accurate than when using the conventional filter. In contrast to previous research, our methodology is also efficient when there are no outliers. The performances of different loss functions for solving the estimation problem are presented. The results show that redescending M-estimators outperform the Huber function. The behaviour of the technique is analyzed for a copolymerization process.Fil: Tupaz Pantoja, Jhovany Alexander. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Asteasuain, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Sanchez, Mabel Cristina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin

Sensor location for nonlinear state estimation

The structure of the sensor network installed in the plant strongly influences the performance of state estimation techniques. One of them, the Unscented Kalman Filter (UKF), provides significant improvement over other filtering methods. It approximates the true mean and covariance of random variables that undergo nonlinear transformations correctly up to the third order with low computational effort. In this work, a Sensor Network Design strategy for monitoring nonlinear dynamic chemical processes using UKF is presented. In contrast to previous works, the tradeoff between cost and estimates precision is addressed in a systematic and efficient way. A novel procedure is proposed to calculate a sensible upper bound for the estimation error. This avoids fixing bounds based on engineer judgment about the new process. Regarding efficiency, the obtained sensor network is generally cheaper and provides a global precision which is between the maximum possible for a given budget and the precision obtained by the sensor network that satisfices the maximum system observability for the same budget. This formulation is important when the budget is limited and it is desired to minimize the cost, without losing the quality of the estimates. The proposed methodology can be easily extended to other nonlinear state estimation techniques. The optimal solution is obtained using a level transversal search algorithm with cutting and stopping criteria. A copolymerization process taken from the literature is used to demonstrate the performance of the proposed instrumentation design technique.Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Tupaz Pantoja, Jhovany Alexander. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin