| dc.creator | Rivera, EC | |
| dc.creator | da Costa, AC | |
| dc.creator | Regina, M | |
| dc.creator | Maciel, W | |
| dc.creator | Maciel, R | |
| dc.date | 2006 | |
| dc.date | MAR | |
| dc.date | 2014-11-10T17:03:51Z | |
| dc.date | 2015-11-26T18:07:02Z | |
| dc.date | 2014-11-10T17:03:51Z | |
| dc.date | 2015-11-26T18:07:02Z | |
| dc.date.accessioned | 2018-03-29T00:49:10Z | |
| dc.date.available | 2018-03-29T00:49:10Z | |
| dc.identifier | Applied Biochemistry And Biotechnology. Humana Press Inc, v. 132, n. 41699, n. 969, n. 984, 2006. | |
| dc.identifier | 0273-2289 | |
| dc.identifier | 1559-0291 | |
| dc.identifier | WOS:000203005100009 | |
| dc.identifier | http://www.repositorio.unicamp.br/jspui/handle/REPOSIP/65691 | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/65691 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/65691 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1293490 | |
| dc.description | In this present article, genetic algorithms and multilayer perceptron neural network (MLPNN) have been integrated in order to reduce the complexity of an optimization problem. A data-driven identification method based on MLPNN and optimal design of experiments is described in detail. The nonlinear model of an extractive ethanol process, represented by a MLPNN, is optimized using real-coded and binary-coded genetic algorithms to determine the optimal operational conditions. In order to check the validity of the computational modeling, the results were compared with the optimization of a deterministic model, whose kinetic parameters were experimentally determined as functions of the temperature. | |
| dc.description | 132 | |
| dc.description | 41699 | |
| dc.description | 969 | |
| dc.description | 984 | |
| dc.language | en | |
| dc.publisher | Humana Press Inc | |
| dc.publisher | Totowa | |
| dc.publisher | EUA | |
| dc.relation | Applied Biochemistry And Biotechnology | |
| dc.relation | Appl. Biochem. Biotechnol. | |
| dc.rights | fechado | |
| dc.source | Web of Science | |
| dc.subject | alcoholic fermentation process | |
| dc.subject | artificial intelligence | |
| dc.subject | design of experiments | |
| dc.subject | modeling | |
| dc.subject | penalty function | |
| dc.subject | Fermentation Processes | |
| dc.subject | Simulation | |
| dc.subject | Design | |
| dc.title | Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network | |
| dc.type | Artículos de revistas | |
