BSTRACTPredicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.
In this paper the synthesis of the predictive controller for control of the nonlinear object is considered. It is supposed that the object model is not known. The method is based on a digital recurrent network (DRN) model of the system to be controlled, which is used for predicting the future behavior of the output variables. The cost function which minimizes the difference between the future object outputs and the desired values of the outputs is formulated. The function ga of the Matlab's Genetic Algorithm Optimization Toolbox is used for obtaining the optimum values of the control signals. Controller synthesis is illustrated for plants often referred to in the literature. Results of simulations show effectiveness of the proposed control system.
In Serbia, for heating of domestic hot water (DHW) it is customary to use electricity. As around 70% of electricity is produced by using low quality coal with high greenhouse emission, it is beneficial to environment to use solar energy by flat-plate solar collectors for heating of DHW in a solar DHW system (SDHWS). The SDHWS with variable tilt flat-plate solar collectors placed in north-south direction at roofs of houses are investigated for their optimal operation in Belgrade, Serbia. The investigated variable-tilt collectors annually take 2 tilts, 4 tilts, and 12 tilts. The used weather data are from the meteorological station. These investigations use three computer codes: EnergyPlus, GenOpt, and Hooke-Jeeves search algorithm. For different solar collectors, the investigations revile their optimum tilts that maximize the solar fraction by the SDHWS. Then, the solar fraction and avoided fossil energy by the SDHWS are maximized. In addition, the deficit in the solar fraction is estimated when the solar collectors are not at their optimum tilt.
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