Ciencias
Área
Gonzalo
Astray Dopazo
Forscher in der Zeit 2013-2016
Publikationen, an denen er mitarbeitet Gonzalo Astray Dopazo (31)
2017
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Approach of different properties of alkylammonium surfactants using artificial intelligence and response surface methodology
Tenside, Surfactants, Detergents, Vol. 54, Núm. 2, pp. 132-140
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Improved 1,3-propanediol production with maintained physical conditions and optimized media composition: Validation with statistical and neural approach
Biochemical Engineering Journal, Vol. 126, pp. 109-117
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Sistema de Gestión integrada de la Prevención de la Calidad, Medioambiental y de la Seguridad alimentaria: la visión de la Prevención de Riesgos
Actas Congreso Prevencionar 2017
2016
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Airborne castanea pollen forecasting model for ecological and allergological implementation
Science of the Total Environment, Vol. 548-549, pp. 110-121
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Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain
Water Science and Technology, Vol. 73, Núm. 7, pp. 1756-1767
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Comparison between developed models using response surface methodology (RSM) and artificial neural networks (ANNs) with the purpose to optimize oligosaccharide mixtures production from sugar beet pulp
Industrial Crops and Products, Vol. 92, pp. 290-299
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Electrical percolation of AOT-based microemulsions with n-alcohols
Journal of Molecular Liquids, Vol. 215, pp. 18-23
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Geochemistry of hydrothermal systems: thermal springs of Ourense
Libro de actas del I Congreso Internacional del Agua "Termalismo y Calidad de Vida": Ourense (España), 23-24 de septiembre de 2015 (Universidade de Vigo), pp. 23-26
2015
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A model to forecast the risk periods of Plantago pollen allergy by using the ANN methodology
Aerobiologia, Vol. 31, Núm. 2, pp. 201-211
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Cleavage of carbofuran and carbofuran-derivatives in micellar aggregates
Progress in Reaction Kinetics and Mechanism, Vol. 40, Núm. 2, pp. 105-118
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Density prediction of ternary mixtures of ethanol + water + ionic liquid using backpropagation artificial neural networks
Ionic liquid-based surfactant science: formulation, characterization and applications (John Wiley & Sons (USA)), pp. 447-458
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Forecasting Olea airborne pollen concentration by means of artificial intelligence
Fresenius Environmental Bulletin, Vol. 24, Núm. 12B, pp. 4574-4580
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Identification of relevant phytochemical constituents for characterization and authentication of tomatoes by General Linear Model linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs)
PLoS ONE, Vol. 10, Núm. 6
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Influence prediction of alkylamines upon electrical percolation of AOT-based microemulsions using artificial neural networks
Tenside, Surfactants, Detergents, Vol. 52, Núm. 6, pp. 473-476
2014
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Implementación de la técnica de gases difusos en la exploración del campo geotérmico San Vicente, El Salvador
Investigación: cultura, ciencia y tecnología, Núm. 12, pp. 41-45
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Percolative behavior models based on artificial neural networks for electrical percolation of AOT microemulsions in the presence of crown ethers as additives
Tenside, Surfactants, Detergents, Vol. 51, Núm. 6, pp. 533-540
2013
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Esters flash point prediction using artificial neural networks
Journal of Computational Chemistry, Vol. 34, Núm. 5, pp. 355-359
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Modelos de predicción basados en inteligencia artificial
Investigación: cultura, ciencia y tecnología, Núm. 10, pp. 34-41
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Percolation threshold of AOT microemulsions with n-alkyl acids as additives prediction by means of artificial neural networks
Tenside, Surfactants, Detergents, Vol. 50, Núm. 5, pp. 360-368
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Predicting critical micelle concentration values of non-ionic surfactants by using artificial neural networks
Tenside, Surfactants, Detergents, Vol. 50, Núm. 2, pp. 118-124