Herbicides in the upper Poxim River, Sergipe, and the risk of contamination of water resources

Increased agricultural activity in watershed areas has been causing concern over contamination by herbicides in agricultural areas. The problem becomes more important when contamination can affect water for human consumption, as happens with water from the Poxim river, which supplies the city of Aracaju, capital of the State of Sergipe. The aim of this study was to evaluate the risk of contamination by herbicides to both surface and groundwater in the upper sub-basin of the Poxim River, and to detect the presence of the active ingredients Diuron and Ametrine up-river from the sugar-cane plantations. Risk analysis was carried out using criteria from the Environmental Protection Agency (EPA), the GUS index, and the GOSS method. It was observed that several active ingredients are at risk of leaching, demonstrating the importance of monitoring the river to control both the quality of water and the frequency and volume of herbicides used in the region. Based on the results, monitoring was carried out bi-monthly from July 2009 to July 2010 at two sampling points. Water samples were analyzed in the laboratory, where the presence of Diuron and Ametrine was noted. Water quality in the Sub-basin of the Rio Poxim is being influenced by the use of herbicides in the region. There was an increase in herbicide concentration in the surface water during the rainy season, possibly caused by soil runoff.

Optimization of Large-Scale Hydrothermal System Operation

This paper presents the development of a mathematical model to optimize the management and operation of the Brazilian hydrothermal system. The system consists of a large set of individual hydropower plants and a set of aggregated thermal plants. The energy generated in the system is interconnected by a transmission network so it can be transmitted to centers of consumption throughout the country. The optimization model offered is capable of handling different types of constraints, such as interbasin water transfers, water supply for various purposes, and environmental requirements. Its overall objective is to produce energy to meet the country's demand at a minimum cost. Called HIDROTERM, the model integrates a database with basic hydrological and technical information to run the optimization model, and provides an interface to manage the input and output data. The optimization model uses the General Algebraic Modeling System (GAMS) package and can invoke different linear as well as nonlinear programming solvers. The optimization model was applied to the Brazilian hydrothermal system, one of the largest in the world. The system is divided into four subsystems with 127 active hydropower plants. Preliminary results under different scenarios of inflow, demand, and installed capacity demonstrate the efficiency and utility of the model. From this and other case studies in Brazil, the results indicate that the methodology developed is suitable to different applications, such as planning operation, capacity expansion, and operational rule studies, and trade-off analysis among multiple water users. DOI: 10.1061/(ASCE)WR.1943-5452.0000149. (C) 2012 American Society of Civil Engineers.

Tiller population density and tillering dynamics in marandu palisade grass subjected to strategies of rotational stocking management and nitrogen fertilization

The objective of this experiment was to evaluate tiller population density and the dynamics of the tillering process in marandu palisade grass subjected to strategies of rotational stocking management and nitrogen fertilization. Treatments corresponded to combinations between two targets of pre-grazing conditions (sward surface height of 25 and 35 cm) and two rates of nitrogen application (50 and 200 kg ha-1 year-1), and were allocated to experimental units according to a 2 x 2 factorial arrangement in a randomised complete block design, with four replications. The following response variables were studied: initial (TPDi), intermediate (TPDm) and final (TPDf) tiller population density as well as the rates of tiller appearance (TAR) and death (TDR) and the tiller population stability index (SI). TPDi was similar to all treatments, with differences in tiller population density becoming more pronounced as the experiment progressed, resulting in larger TPDf on swards managed at 25 cm pre-grazing height. Tiller death was larger on swards managed at 35 cm, with differences in tiller appearance being recorded only from February 2010 onwards. Stability of tiller population was higher on swards managed at 25 cm pre-grazing height. Overall, there was no effect of nitrogen on the studied variables, and the most adequate grazing strategy corresponded to the pre-grazing height of 25 cm, regardless of the nitrogen application rate used.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

A new approach for modeling and control of nonlinear systems via norm-bounded linear differential inclusions

A systematic approach to model nonlinear systems using norm-bounded linear differential inclusions (NLDIs) is proposed in this paper. The resulting NLDI model is suitable for the application of linear control design techniques and, therefore, it is possible to fulfill certain specifications for the underlying nonlinear system, within an operating region of interest in the state-space, using a linear controller designed for this NLDI model. Hence, a procedure to design a dynamic output feedback controller for the NLDI model is also proposed in this paper. One of the main contributions of the proposed modeling and control approach is the use of the mean-value theorem to represent the nonlinear system by a linear parameter-varying model, which is then mapped into a polytopic linear differential inclusion (PLDI) within the region of interest. To avoid the combinatorial problem that is inherent of polytopic models for medium- and large-sized systems, the PLDI is transformed into an NLDI, and the whole process is carried out ensuring that all trajectories of the underlying nonlinear system are also trajectories of the resulting NLDI within the operating region of interest. Furthermore, it is also possible to choose a particular structure for the NLDI parameters to reduce the conservatism in the representation of the nonlinear system by the NLDI model, and this feature is also one important contribution of this paper. Once the NLDI representation of the nonlinear system is obtained, the paper proposes the application of a linear control design method to this representation. The design is based on quadratic Lyapunov functions and formulated as search problem over a set of bilinear matrix inequalities (BMIs), which is solved using a two-step separation procedure that maps the BMIs into a set of corresponding linear matrix inequalities. Two numerical examples are given to demonstrate the effectiveness of the proposed approach.

Surface-enhanced Raman spectroscopy studies of organophosphorous model molecules and pesticides

This work reports the analytical application of surface-enhanced Raman spectroscopy (SERS) in the trace analysis of organophosphorous pesticides (trichlorfon and glyphosate) and model organophosphorous compounds (dimethyl methylphosphonate and o-ethyl methylphosphonothioate) bearing different functional groups. SERS measurements were carried out using Ag nanocubes with an edge square dimension of ca. 100 nm as substrates. Density functional theory (DFT) with the B3LYP functional was used for the optimization of ground state geometries and simulation of Raman spectra of the organophosphorous compounds and their silver complexes. Adsorption geometries and marker bands were identified for each of the investigated compound. Results indicate the usefulness of SERS methodology for the sensitive analyses of organophosphorous compounds through the use of vibrational spectroscopy.

OPTIMIZATION AND CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

Optimization and control of a continuous polymerization reactor

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.