Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network
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 m...
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| Published in: | Applied biochemistry and biotechnology Vol. 129-132; no. 1-3; pp. 969 - 984 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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United States
Springer Nature B.V
2006
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| Abstract | 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. |
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| AbstractList | 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. 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.[PUBLICATION ABSTRACT] |
| Author | da Costa, Aline C Maciel, Maria Regina Wolf Rivera, Elmer Ccopa Maciel Filho, Rubens |
| Author_xml | – sequence: 1 givenname: Elmer Ccopa surname: Rivera fullname: Rivera, Elmer Ccopa email: elmer@feq.unicamp.br organization: DPQ/FEQ/UNICAMP, Campinas, SP, Brasil Cx. Postal 6066, 13081-970. elmer@feq.unicamp.br – sequence: 2 givenname: Aline C surname: da Costa fullname: da Costa, Aline C – sequence: 3 givenname: Maria Regina Wolf surname: Maciel fullname: Maciel, Maria Regina Wolf – sequence: 4 givenname: Rubens surname: Maciel Filho fullname: Maciel Filho, Rubens |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16915705$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s13197_012_0695_y crossref_primary_10_1080_02664763_2012_753041 crossref_primary_10_1002_jctb_2391 crossref_primary_10_1016_j_cep_2010_02_012 crossref_primary_10_1002_jctb_2383 crossref_primary_10_1007_s12010_007_8062_6 crossref_primary_10_1021_acs_iecr_2c04239 crossref_primary_10_1016_j_cherd_2022_03_022 crossref_primary_10_3390_app9183664 |
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| Copyright | Humana Press Inc. 2006 Humana Press Inc 2006. |
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| DOI | 10.1385/ABAB:132:1:969 |
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| SubjectTerms | Algorithms Bacteria - metabolism Bioreactors - microbiology Computer applications Computer Simulation Design of experiments Ethanol Ethanol - metabolism Genetic algorithms Identification methods Models, Biological Models, Genetic Multilayer perceptrons Neural networks Neural Networks (Computer) Optimization Pattern Recognition, Automated - methods Quality Control Studies |
| Title | Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network |
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