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Response Surface Models (response + surface_models)
Selected AbstractsACIDIC ELECTROLYZED WATER PROPERTIES AS AFFECTED BY PROCESSING PARAMETERS AND THEIR RESPONSE SURFACE MODELSJOURNAL OF FOOD PROCESSING AND PRESERVATION, Issue 1 2004GABRIEL O. I. EZEIKE Several studies of acidic electrolyzed (EO) water demonstrated the efficacy of EO water for inactivation of different foodborne pathogens and reported on the chemical species present in EO water. This study was conducted to investigate the effect of production parameters (voltage, NaCl concentration, flow rate, and temperature) on the properties of EO water and to model the complex reactions occurring during the generation of EO water. At 0.1% salt concentration, EO water was produced at 2, 10, and 28 V. However, due to high conductivity of the electrolyte at 0.5% salt concentration, the voltage applied across the cell was limited to 7 V. The electrolyte flow rate was set at 0.5, 2.5, and 4.5 L/mn. For pH and oxidation-reduction potential (ORP), NaCl concentration was the most significant factor followed by voltage, electrolyte flow rate and temperature, respectively. However, in the case of residual chlorine, flow rate was relatively more important than voltage. Response surface methodology yielded models to predict EO water properties as functions of the process parameters studied, with very high coefficients of determination (R2= 0.872 to 0.938). In general, the higher the NaCl concentration and voltage, the higher the ORP and residual chlorine of EO water. Increased electrolyte flow rate will produce EO water with lower ORP and residual chlorine due to the shorter residence time in the electrolytic cell. [source] Multi-objective turbomachinery optimization using a gradient-enhanced multi-layer perceptronINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 6 2009M. C. Duta Abstract Response surface models (RSMs) have found widespread use to reduce the overall computational cost of turbomachinery blading design optimization. Recent developments have seen the successful use of gradient information alongside sampled response values in building accurate response surfaces. This paper describes the use of gradients to enhance the performance of the RSM provided by a multi-layer perceptron. Gradient information is included in the perceptron by modifying the error function such that the perceptron is trained to fit the gradients as well as the response values. As a consequence, the back-propagation scheme that assists the training is also changed. The paper formulates the gradient-enhanced multi-layer perceptron using algebraic notation, with an emphasis on the ease of use and efficiency of computer code implementation. To illustrate the benefit of using gradient information, the enhanced neural network model is used in a multi-objective transonic fan blade optimization exercise of engineering relevance. Copyright © 2008 John Wiley & Sons, Ltd. [source] Analysis and prediction of protein folding rates using quadratic response surface modelsJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 10 2008Liang-Tsung Huang Abstract Understanding the relationship between amino acid sequences and folding rates of proteins is an important task in computational and molecular biology. In this work, we have systematically analyzed the composition of amino acid residues for proteins with different ranges of folding rates. We observed that the polar residues, Asn, Gln, Ser, and Lys, are dominant in fast folding proteins whereas the hydrophobic residues, Ala, Cys, Gly, and Leu, prefer to be in slow folding proteins. Further, we have developed a method based on quadratic response surface models for predicting the folding rates of 77 two- and three-state proteins. Our method showed a correlation of 0.90 between experimental and predicted protein folding rates using leave-one-out cross-validation method. The classification of proteins based on structural class improved the correlation to 0.98 and it is 0.99, 0.98, and 0.96, respectively, for all-,, all-,, and mixed class proteins. In addition, we have utilized Baysean classification theory for discriminating two- and three-state proteins, which showed an accuracy of 90%. We have developed a web server for predicting protein folding rates and it is available at http://bioinformatics.myweb.hinet.net/foldrate.htm. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [source] Influence of water activity and temperature on conidial germination and mycelial growth of ochratoxigenic isolates of Aspergillus ochraceus on grape juice synthetic medium.JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 10 2005Predictive models Abstract The first stages in the development of Aspergillus ochraceus, an ochratoxin A-producing fungus that infects grapes and may grow on them, have been studied on a synthetic nutrient medium similar to grape in composition. Spore germination and mycelial growth have been tested over a water activity (aw) and temperature range which could approximate to the real conditions of fungal development on grapes. Optimal germination and growth were observed at 30 °C for all three isolates tested. Maximal germination rates were detected at 0.96,0.99 aw at 20 °C, while at 10 and 30 °C the germination rates were significantly higher at 0.99 aw. Although this abiotic factor (aw) had no significant influence on mycelial growth, growth rates obtained at 0.98 aw were slight higher than those at other aw levels. Predictive models for the lag phase before spore germination as a function of water activity and temperature have been obtained by polynomial multiple linear regression, and the resulting response surface models have been plotted. Copyright © 2005 Society of Chemical Industry [source] Response Surface Designs for Experiments in BioprocessingBIOMETRICS, Issue 2 2006Steven G. Gilmour Summary Many processes in the biological industries are studied using response surface methodology. The use of biological materials, however, means that run-to-run variation is typically much greater than that in many experiments in mechanical or chemical engineering and so the designs used require greater replication. The data analysis which is performed may involve some variable selection, as well as fitting polynomial response surface models. This implies that designs should allow the parameters of the model to be estimated nearly orthogonally. A class of three-level response surface designs is introduced which allows all except the quadratic parameters to be estimated orthogonally, as well as having a number of other useful properties. These subset designs are obtained by using two-level factorial designs in subsets of the factors, with the other factors being held at their middle level. This allows their properties to be easily explored. Replacing some of the two-level designs with fractional replicates broadens the class of useful designs, especially with five or more factors, and sometimes incomplete subsets can be used. It is very simple to include a few two- and four-level factors in these designs by excluding subsets with these factors at the middle level. Subset designs can be easily modified to include factors with five or more levels by allowing a different pair of levels to be used in different subsets. [source] | ||||||||||