Resumo Stiretrus decastigmus (Herrich-Schaeffer) (Hemiptera: Pentatomidae) é um importante predador do inseto-praga Microtheca ochroloma Stal (Coleoptera: Chrysomelidae). O objetivo deste trabalho foi estudar o desenvolvimento pré-imaginal de S. decastigmus em diferentes temperaturas. Foram utilizadas as temperaturas de 20, 25, e 30 °C, umidade relativa do ar de 70 ± 10% e fotofase de 12 h, sendo as ninfas alimentadas com larvas de M. ochroloma. Foram avaliados a duração e viabilidade dos estágios de ovo e ninfa, a duração de cada instar e o potencial de predação. O período de incubação diminuiu com o aumento da temperatura e apresentou maior viabilidade a 25 °C. A duração do estágio ninfal foi inversamente proporcional a temperatura com 18 dias a 30 °C e 40,6 dias a 20 °C. A maior taxa de predação de S. decastigmus foi encontrada a 20 °C (90,4 larvas) e 30 °C (72,5 larvas). S. decastigmus teve maior viabilidade e menor consumo de larvas de M. ochroloma a 25 °C.

Abstract Stiretrus decastigmus (Herrich-Schaeffer) (Hemiptera: Pentatomidae) is an important predator of the insect pest Microtheca ochroloma Stal (Coleoptera: Chrysomelidae). The present study investigated the pre-imaginal development of S. decastigmus at different temperatures. The temperatures were: 20, 25, and 30 °C, with a relative humidity of 70 ± 10% and a photofase of 12 h, and the nymphs were fed larvae of M. ochroloma. We evaluated the duration and viability of the egg and nymphal stages, the duration of each instar, and the predation potential. The incubation time decreased with increasing temperature, and the viability was highest at 25 °C. The duration of the nymphal stage was inversely proportional to the temperature, ranging from 18 days at 30 °C to 40.6 days at 20 °C. The highest S. decastigmus predation rates were found at 20 °C (90.4 larvae) and 30 °C (72.5 larvae). S. decastigmus showed the highest viability and lowest consumption of larvae of M. ochroloma at 25 °C.

Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

Microtheca spp. (Coleoptera: Chrysomelidae) são insetos-praga relacionados principalmente às culturas da família Brassicaceae. No Estado do Rio Grande do Sul (RS), no sul do Brasil, são encontrados no nabo forrageiro, Raphanus sativus L. var. oleiferus Metzg, comumente cultivado no outono/inverno. Este trabalho relata a predação de larvas de Microtheca spp. por larvas de Toxomerus duplicatus Wiedemann, 1830 (Diptera: Syrphidae), em nabo forrageiro, em Santa Maria, RS. Este registro oferece nova informação sobre os inimigos naturais de Microtheca spp. no Brasil os quais podem ser uma nova opção para o manejo integrado dessas espécies.

Microtheca spp. (Coleoptera: Chrysomelidae) are insect pests primarily related to Brassicaceae crops. In the State of Rio Grande do Sul (RS), southern Brazil, they are found on forage turnip, Raphanus sativus L. var. oleiferus Metzg., which is commonly grown during fall/winter seasons. This work reports the predation of Microtheca spp. larvae by Toxomerus duplicatus Wiedemann, 1830 (Diptera: Syrphidae) larvae, on forage turnip crop, in Santa Maria, RS. This register provides new information about Microtheca spp. natural enemies in Brazil, which might be a new option for integrate pest management of these species.

In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system.

Although it is still used very little by industry, the process of essential oil extraction from vegetable matrices with supercritical CO2 is regarded as a potentially viable technique. The operation of separating the extract from the solvent is carried out by reducing the pressure in the system. Separation by membranes is an alternative that offers lower energy consumption and easier operation than traditional methods of separation. Combining the processes essential oil extraction with supercritical CO2 and separation by membranes permits the separation of solvent and oil without the need for large variations in extraction conditions. This results in a large energy savings in the case of solvent repressurisation and reuse. In this study, the effectiveness of reverse osmosis membranes in separating lemongrass essential oil from mixtures with supercritical CO2 was tested. The effects of feed oil concentration and transmembrane pressure on CO2 permeate flux and oil retention were studied for three membrane models.

The development of control systems based on fuzzy rules facilitates the solving of problems when insufficient phenomenological information is available. The most common way of grouping fuzzy rules to form a controller is known as Mamdani controller. This controller consists of a set of rules with two premises, the error and the error variation, and one conclusion, the control action variation. One of the most delicate phases of the project of fuzzy systems is the definition of the supports (range) of each fuzzy qualifiers. This work apply genetic algorithms, together with some model of the system, to the adjustment of the supports of the fuzzy sets used in a Mamdani controller. The results show that the automatic adjustment is faster and more efficient that the manual one. Finally, the results are compared with a PID that was also adjusted with genetic algorithms.

Particle size distribution (PSD) of polystyrene particles produced by suspension polymerization is of fundamental importance in determining suspension stability and product quality attributes. Within a population balance framework, a model is proposed for suspension polymerization reactors to describe the evolution of the PSD. The model includes description of breakage and coalescence rates in terms of reaction kinetics and rheology of the dispersed phase. The model is validated with experimental data of styrene suspension polymerization.