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.

In this work, ZnAl2O4 spinel was used as photocatalyst for dye degradation in aqueous solutions under UV irradiation. This material was prepared by a route with chitosan as template. The effects of process variables, such as, initial dye concentration and photocatalyst concentration were investigated. Kinetic parameters were obtained and the reaction order was determined. The results revealed that both studied variables presented strong influence on the dye degradation efficiency, which attained 100% in some cases. It was found that the dye photocatalytic degradation under UV irradiation followed a first order kinetic behavior. The results indicated that ZnAl2O4 spinel can be used to treat dye containing effluents

The present study describes the adsorption of textile dye on rice hull. The adsorption parameters were obtained from Langmuir and Freundlich isotherms utilizing linear regression plots to fit the data. The results showed that Langmuir equation was more suitable than the Freundlich one to predict the experimental adsorption isotherms, indicating that the adsorption of dye molecules is localized in a monolayer on the surface of the adsorbent. Thermodynamic parameters such as ΔHa, ΔSa and ΔGa were determined in this work. The heat evolved during adsorption is endothermic. The maximum sorption capacity of rice husk was satisfactory when compared to capacities of other low-cost adsorbent materials.

This work is focused on the production of struvite as an alternative to reduce the concentration of phosphorus and nitrogen from swine wastewater. The effect of Mg source (MgO and MgCl2. 6H2O) and pH on nitrogen and phosphorus removal was investigated. From the results was seen that N and P were efficiently removed from swine wastewater. Data of X-ray diffraction (XRD) revealed that the production of struvite in the crystalline form is dependent of pH, which was obtained only in the runs carried out at pH 9.5, independent of Mg source employed. The production of struvite showed to be effective for adding value to swine wastewater.

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.