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| Menezes,Paulo L. de; Azevedo,Carlos A. V. de; Eyng,Eduardo; Dantas Neto,José; Lima,Vera L. A. de. |
| ABSTRACTDetermining uniformity coefficients of sprinkle irrigation systems, in general, depends on field trials, which require time and financial resources. One alternative to reduce time and expense is the use of simulations. The objective of this study was to develop an artificial neural network (ANN) to simulate sprinkler precipitation, using the values of operating pressure, wind speed, wind direction and sprinkler nozzle diameter as the input parameters. Field trials were performed with one sprinkler operating in a grid of 16 x 16, collectors with spacing of 1.5 m and different combinations of nozzles, pressures, and wind conditions. The ANN model showed good results in the simulation of precipitation, with Spearman's correlation coefficient (rs)... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Sprinkler; Water distribution uniformity; Artificial intelligence; Computational model. |
| Ano: 2015 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662015000900817 |
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| Eyng,Eduardo; Silva,Flávio Vasconcelos da; Palú,Fernando; Fileti,Ana Maria Frattini. |
| Gaseous ethanol may be recovered from the effluent gas mixture of the sugar cane fermentation process using a staged absorption column. In the present work, the development of a nonlinear controller, based on a neural network inverse model (ANN controller), was proposed and tested to manipulate the absorbent flow rate in order to control the residual ethanol concentration in the effluent gas phase. Simulation studies were carried out, in which a noise was applied to the ethanol concentration signals from the rigorous model. The ANN controller outperformed the dynamic matrix control (DMC) when step disturbances were imposed to the gas mixture composition. A security device, based on a conventional feedback algorithm, and a digital filter were added to the... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Absorption column; Artificial neural network; Feedforward control. |
| Ano: 2009 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132009000400020 |
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