RESUMO O trabalho teve os seguintes objetivos: 1) avaliar diferentes substratos para a produção semissólida dos fungos Metarhizium anisopliae e Beauveria bassiana; 2) verificar a tolerância dos conídios produzidos ao efeito da radiação ultravioleta e da temperatura; e 3) avaliar a patogenicidade dos conídios a Diatraea saccharalis. Para ambos os fungos, foram utilizadas 6 repetições para cada tratamento: amido de milho, arroz integral, arroz parboilizado tipo1, arroz tipo 1 e 2, aveia em flocos, canjiquinha, farelo de trigo, farinhas de mandioca crua, de milho amarela e de trigo especial, fubá, milho em grãos, polvilho azedo, soja em grãos, trigo moído e turfa. Os conídios foram quantificados em câmara de Neubauer e a determinação da viabilidade foi realizada através da observação em microscópio, dos conídios germinados e não germinados, após espalhamento da suspensão fúngica em placas de Petri contendo BDA. No ensaio com radiação os fungos foram expostos à radiação por 25 e 50 segundos e foi considerado um tratamento sem exposição; para temperatura os fungos foram expostos a 20, 25, 30 e 35o C. Utilizando a torre de Potter, 2 mL de cada suspensão fúngica dos tratamentos foram pulverizados em lagartas de D. saccharalis. As maiores concentrações de conídios de M. anisopliae e B. bassiana foram encontradas nos tratamentos com arroz parboilizado tipo 1, arroz tipo 1 e 2, farinha de milho amarela, fubá e trigo moído e a viabilidade dos conídios produzidos superou os 94%. Quanto maior o tempo de exposição à radiação menor foi o número de conídios viáveis; também ocorreu perda significativa da viabilidade dos conídios quando expostos à temperatura de 35o C. Os fungos foram patogênicos para D. saccharalis.

ABSTRACT The present study was aimed to evaluate different (semi-solid) media for the production of Metarhizium anisopliae and Beauveria bassiana propagules, and to evaluate the tolerance of these propagules to ultraviolet radiation and temperature. The experiments were performed at the Biological Control Laboratory of the Instituto Biológico at Campinas, São Paulo, Brazil. For both fungi, 6 repetitions were performed for each of the 17 treatments: corn starch, full rice, parboiled rice, type-1 rice, type-2 rice, oat flakes, canjiquinha [grits], wheat flour, raw cassava flour, yellow corn flour, special wheat flour, corn flour, corn in grains, cassava starch, soy in grains, crushed wheat, and turf. The viability analysis was done in plastic plates containing BDA. For the bioassays involving exposure to ultraviolet light and temperature, BDA was also used for viability analysis, and each treatment was exposed to the UV radiation for 0, 25 and 50 seconds, the temperature exposure being at 20, 25, 30 and 35º C. Using a Potter tower, 2 mL of fungus suspension from each treatment was inoculated to the Diatraea saccharalis caterpillars. Regarding the sporulation, the largest concentrations of M. anisopliae and B. bassiana were found for the treatments with parboiled rice, type-1 rice, type-2 rice, yellow corn flour, corn flour and crushed wheat. The viability of all treatments was superior to 94.00%. Also, the longer the duration of the exposition to the UV, the 2Universidade Estadual Paulista, Faculdade de Ciências Agronômicas, Botucatu, SP, Brasil.

This paper investigates the feasibility of using grey-box neural models (GNM) in Real Time Optimization (RTO). These models are based on a suitable combination of fundamental conservation laws and neural networks, being used in at least two different ways: to complement available phenomenological knowledge with empirical information, or to reduce dimensionality of complex rigorous physical models. We have observed that the benefits of using these simple adaptable models are counteracted by some difficulties associated with the solution of the optimization problem. Nonlinear Programming (NLP) algorithms failed in finding the global optimum due to the fact that neural networks can introduce multimodal objective functions. One alternative considered to solve this problem was the use of some kind of evolutionary algorithms, like Genetic Algorithms (GA). Although these algorithms produced better results in terms of finding the appropriate region, they took long periods of time to reach the global optimum. It was found that a combination of genetic and nonlinear programming algorithms can be use to fast obtain the optimum solution. The proposed approach was applied to the Williams-Otto reactor, considering three different GNM models of increasing complexity. Results demonstrated that the use of GNM models and mixed GA/NLP optimization algorithms is a promissory approach for solving dynamic RTO problems.

This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM) is based on fundamental conservation laws associated with a neural network (NN) used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN) used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.

Neural networks have been investigated for predicting mass transfer coefficients from supercritical Carbon Dioxide/Ethanol/Water system. To avoid the difficulties associated with reduce experimental data set available for supercritical extraction in question, it was chosen to use a technique to generate new semi-empirical data. It combines experimental mass transfer coefficient with those obtained from correlation available in literature, producing an extended data set enough for efficient neural network identification. With respect to available experimental data, the results obtained to benefit neural networks in comparing with empirical correlations for predicting mass transfer parameters.

or multivariable non linear predictive control implementations, a hybrid-neural model (lumped model) was successfully used for modeling a loop-tubular polymerization reactor (a lumped or distributed model, depending on recycle ratio). Bifurcation diagrams were computed in order to investigate the agreement between process and model, of paramount importance for model based controller implementation purposes. Performance was evaluated considering the nonlinear model predictive control of both a loop tubular reactor (lumped) SISO problem and a tubular reactor (distributed) MIMO problem.

The objective of this work is to develop a strategy of nonlinear model predictive control for industrial slurry reactors of propylene polymerizations. The controlled variables are the melt index (polymer quality) and the amount of unreacted monomer (productivity). The model used in the controller presents a linear dynamics and a nonlinear static gain given by a neuronal network MLP (multilayer perceptron). The simulated performance of the controller was evaluated for a typical propylene polymerization process. It is shown that the performance of the proposed control strategy is much better than the one obtained with the use of linear predictive controllers for setpoint tracking control problems.

The increasing degradation of water resources makes it necessary to monitor and control process variables that may disturb the environment, but which may be very difficult to measure directly, either because there are no physical sensors available, or because these are too expensive. In this work, two soft sensors are proposed for monitoring concentrations of nitrate (NO) and ammonium (NH) ions, and of carbonaceous matter (CM) during nitrification of wastewater. One of them is based on reintegration of a process model to estimate NO and NH and on a feedforward neural network to estimate CM. The other estimator is based on Stacked Neural Networks (SNN), an approach that provides the predictor with robustness. After simulation, both soft sensors were implemented in an experimental unit using FIX MMI (Intellution, Inc) automation software as an interface between the process and MATLAB 5.1 (The Mathworks Inc.) software.

In this work a hybrid neural modelling methodology, which combines mass balance equations with functional link networks (FLNs), used to represent kinetic rates, is developed for bioprocesses. The simple structure of the FLNs allows the easy and rapid estimation of network weights and, consequently, the use of the hybrid model in an adaptive form. As the proposed model is able to adjust to kinetic and environmental changes, it is suitable for use in the development of optimization strategies for fed-batch bioreactors. The proposed methodology is used to model the processes for penicillin and ethanol production, and the development of an adaptive optimal control scheme is discussed using ethanol fermentation as an example.

Abstract - The start-up of continuous bioreactors is solved as an optimal control problem. The choice of the dilution rate as the control variable reduces the dimension of the system by making the use of the global balance equation unnecessary for the solution of the optimization problem. Therefore, for systems described by four or less mass balance equations, it is always possible to obtain an analytical expression for the singular arc as a function of only the state variables. The steady state conditions are shown to satisfy the singular arc expression and, based on this knowledge, a feeding strategy is proposed which leads the reactor from an initial state to the steady state of maximum productivity