Julio Cesar Sampaio Dutra

A Hybrid Estimation Scheme Based on the Sequential Importance Resampling Particle Filter and the Particle Swarm Optimization (PSO-SIR)

Particle filters are recursive Bayesian estimators, which are being applied to many areas of engineering in recent years to estimate states and parameters, regarding fire spread, tumors, oil pipelines, heat transfer, chemical reactors, etc. The key idea behind particle filters is that they use an initial distribution (sample), based on the previous state estimate, to calculate the best estimate for the current state, relying only on the current available measurements and the knowledge about the system. The greatest advantage of these methods is the easy computational implementation. However, setting the standard deviation for the initial distribution is very important for the success of the method. For this reason, standard formulation of these methods may not provide good results in problems with large discontinuities (or irregular/abrupt changes). For example, this would be the case of estimating step changes in the heat flux on a plate. Although several solutions have been proposed to improve the estimation performance, they still suffer from the curse of discontinuity. This occurs because particle filters proposed in the literature are not adaptive methods. In the example mentioned above, particle filters can have both a priori information and sample satisfactory before the change. However, after the change begins, the available information could be not enough to draw a suitable sample for the estimation. At this point, it is necessary to modify the standard deviation to broaden the particle search field or to move the a priori information to a new region where a new sample should be drawn. In this regard, the aim of this chapter is to propose a hybrid estimation scheme based on Particle Swarm Optimization (PSO) built into the particle filter Sampling Importance Resampling (SIR) to project the a priori information to a new search region, according to the current observation. To demonstrate the proposal, the problem of estimating step changes on the heat flux on a plate is taken into account, considering experimental measurements. The results allow to state that the scheme combining PSO and SIR provides good performance for this type of problem.

Particle filter-based monitoring scheme for simulated bio-ethylene production process

ABSTRACT The use of biomass has been promoted to meet the need for sustainable production of ethylene, which is the most used petroleum-derived in the polymer industry. Ethanol is an alternative feedstock to yield the so-called bio-ethylene through catalytic dehydration in fixed-bed reactors. As the reaction system is strongly endothermic, it is very important to know accurately the reactor temperature to assure the process performance. However, in the industrial context, the process measurements are often uncertain and not all variables can be directly measured online. In this regard, this paper analyses the mathematical modelling and numerical simulation of the ethanol catalytic dehydration and contributes with a monitoring scheme using the Bayesian method known as particle filter. Numerical simulations helped understanding the process behaviour and locating the best position for the temperature sensor in the reactor. From temperature measurements, the proposed inferential tool estimates hidden state variables and unmeasured disturbances, using Sequential Importance Resampling algorithm for the particle filter. The proposal is investigated according to the number of particles and the criterion total error reduction. The results show that the monitoring scheme is able to estimate satisfactorily the process variable profiles, as the temperature and chemical conversion, along the reactor length.