Gonzalo Astray Dopazo-rekin lankidetzan egindako argitalpenak (31)

2017

  1. Approach of different properties of alkylammonium surfactants using artificial intelligence and response surface methodology

    Tenside, Surfactants, Detergents, Vol. 54, Núm. 2, pp. 132-140

  2. 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

  3. 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

  1. Airborne castanea pollen forecasting model for ecological and allergological implementation

    Science of the Total Environment, Vol. 548-549, pp. 110-121

  2. 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

  3. 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

  4. Electrical percolation of AOT-based microemulsions with n-alcohols

    Journal of Molecular Liquids, Vol. 215, pp. 18-23

  5. 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

  1. A model to forecast the risk periods of Plantago pollen allergy by using the ANN methodology

    Aerobiologia, Vol. 31, Núm. 2, pp. 201-211

  2. Cleavage of carbofuran and carbofuran-derivatives in micellar aggregates

    Progress in Reaction Kinetics and Mechanism, Vol. 40, Núm. 2, pp. 105-118

  3. 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

  4. Forecasting Olea airborne pollen concentration by means of artificial intelligence

    Fresenius Environmental Bulletin, Vol. 24, Núm. 12B, pp. 4574-4580

  5. 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

  6. 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

  1. 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

  2. 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

  1. Esters flash point prediction using artificial neural networks

    Journal of Computational Chemistry, Vol. 34, Núm. 5, pp. 355-359

  2. Modelos de predicción basados en inteligencia artificial

    Investigación: cultura, ciencia y tecnología, Núm. 10, pp. 34-41

  3. 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

  4. Predicting critical micelle concentration values of non-ionic surfactants by using artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 50, Núm. 2, pp. 118-124