Polymeric materials are present in various industrial sectors and in daily life, presenting advantages such as low cost and durability. Several processes for manufacturing have been developed. To achieve safety and operational goals measurement methods for proper process monitoring and effective control are needed. However, in real polymer plants, measuring devices are subject to uncertainties and are not always available. Hence, this paper proposes a virtual sensor scheme based on a particle filter and artificial neural network (ANN) that is applied to a simulated polymerization reactor. This scheme reduces uncertainties and enables the observation of latent variables. The ANN is also used for predicting the final properties of the polymer. The goal is to provide controllers with more complete and improved information. The results show that the virtual sensor scheme improves the process control, providing accurate estimates and action times that are consistent with industrial sampling intervals, which highlights its potential for practical applications.

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In this paper, we combine algorithm of Liu & West for the Particle Filter (PF) with SIRU-type epidemic model to monitor and forecast cases of Covid-19 in Brazil from February up to September. We filter the number of cumulative reported cases and estimate model parameters and more importantly unreported infectious cases (asymptomatic and symptomatic infectious individuals). The parameters under study are related to the attenuation factor of the transmission rate and the fraction of asymptomatic infectious becoming reported as symptomatic infectious. Initially, the problem is analysed through Particle Swarm Optimization (PSO) based simulations to provide initial guesses, which are then refined by means of PF simulations. Subsequently, two additional steps are performed to verify the capability of the adjusted model to predict and forecast new cases. According to the results, the pandemic peak is expected to take place in mid-June 2020 with about 25,000 news cases per day. As medical and hospital resources are limited, this result shows that public health interventions are essential and should not be relaxed prematurely, so that the coronavirus pandemic is controlled and conditions are available for the treatment of the most severe cases.

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Este artigo realiza o estudo dos métodos de otimização determinístico, Steepest Descent, e heurístico, Differential Evolution e Particle Swarm, para um problema black box genérico com duas variáveis em sua função objetivo. O método determinístico apresentou forte dependência dos valores iniciais adotados, apresentando diversos mínimos locais, sendo necessário a adoção de múltiplos pontos iniciais. Os métodos Particle Swarm e Differential Evolution apresentam resultados razoáveis, porém o funcionamento dos algoritmos heurísticos impossibilita que o ponto encontrado seja certamente definido como mínimo global.