Hydrological modelling of Maranhão and Pracana Basins, a first approach

Modelling the hydrology of hydrographic basins has shown itself as a useful tool in environment management. The hydrological models can be used for multiple purposes: estimate runoff from sequences of rainfall, access stream water quality, quantify the diffuse pollution that reaches water masses such as estuaries, rivers and lakes, etc. This study has as final objective to simulate and analyse the flow, sediment transport and water quality as a function of landuse and soil type in the basins of Maranhão and Pracana. The modelling system used is SWAT, Soil Water Assessment Tool. In this first phase of the study the hydrodynamic calibration of the model was performed using measurements of average daily flows in five stations. The model compares well with the measurements; the annual average flows are similar and the majority of the measured flow peaks coincide with the model peaks.

A new combined joules supercritical rankine cycle exergetic analysis and optimization

The application of a supercritical Rankine cycle in combined cycles does not happen in today’s thermoelectric power stations. Nevertheless, the most recent development in gas turbines, that allows a high efficiency and high exhaust gases temperatures, and the improvement of high pressure and temperature alloys, makes this cycle possible. This study’s intent is to prove the viability of this combined cycle, since it can break the 60% efficiency barrier, which is the plafond in actual power stations. To attain this target, several configurations for this cycle have been simulated, optimized and analyzed [1]. The simulations were done with the computational program IPSEpro [2] and the optimizations were effectuated with software developed for the effect, using the DFP method [3]. In parallel with the optimization that claims the cycle’s efficiency maximization, an exergetic analysis was also made [4] to all the cycle components. In opposite to what happens in subcritical combined cycles, it was demonstrated that in supercritical combined cycles the higher efficiency takes place with a single steam pressure in the heat recovery steam generator (HRSG).

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.