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Melt Index (melt + index)
Selected AbstractsEffect of different starches on rheological and microstructural properties of (II) commercial processed cheeseINTERNATIONAL JOURNAL OF FOOD SCIENCE & TECHNOLOGY, Issue 12 2008Darshan Trivedi Summary A range of commercial processed cheese samples containing starch were prepared on a Rapid Visco Analyser (RVA) and on a pilot plant scale. This work clearly demonstrated that it was possible to manufacture processed cheese with part of the protein replaced with potato starch, while maintaining similar rheological attributes (firmness) to those of the control and an acceptable melt index. Sensory evaluation showed that, although the reduced-protein cheese samples had a good, clean, fresh flavour that was comparable with that of the control, at high starch concentrations the starch-containing processed cheese had a pasty texture and tended to stick to the wrapper. [source] Miscibility and rheological properties of poly(vinyl chloride)/styrene,acrylonitrile blends prepared by melt extrusionJOURNAL OF APPLIED POLYMER SCIENCE, Issue 1 2007Hyun Sik Moon Abstract Styrene,acrylonitrile (SAN) with acrylonitrile (AN) concentrations of 11.6,26 wt % and ,-methylstyrene acrylonitrile (,MSAN) with a wide range of AN concentrations are miscible with poly(vinyl chloride) (PVC) through solution blending. Here we examine the rheological properties and miscibility of PVC/SAN and PVC/,MSAN blends prepared by melt extrusion for commercial applications. We have investigated the rheological properties of the blends with a rheometer and a melt indexer. The PVC/SAN and PVC/,MSAN blends have a low melting torque, a long degradation time, and a high melt index, and this means that they have better processability than pure PVC. The miscibility of the blends has been characterized with differential scanning calorimetry, dynamic mechanical thermal analysis, and advanced rheometrics expansion system analysis. The miscibility of the blends has also been characterized with scanning electron microscopy. The SAN series with AN concentrations of 24,31 wt % is immiscible with PVC by melt extrusion, whereas ,MSAN with 31 wt % AN is miscible with PVC, even when they are blended by melt extrusion, because of the strong interaction between PVC and ,MSAN. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007 [source] Effect of Reaction Conditions and Catalyst Design on the Rheological Properties of Polyolefins Produced in Gas-Phase Olefin Polymerization ReactorsMACROMOLECULAR THEORY AND SIMULATIONS, Issue 9 2008P. Pladis Abstract A model is developed to predict the viscoelastic behavior of polyolefins produced in catalytic polymerization reactors. The approach is based on the solution of different sub-models (e.g., a kinetic model, a single particle model, a macroscopic reactor model and a rheological model). From the calculated rheological curve, the polymer melt index is determined. The ability of the proposed model to predict the viscoelastic behavior of linear polymer melts quantitatively is examined for the operation of a catalytic olefin polymerization cascade-loop reactor process. In addition, the transient rheological properties of polyolefins produced in a Ziegler-Natta gas-phase olefin polymerization fluidized-bed reactor are calculated. [source] Reactor Modeling of Gas-Phase Polymerization of EthyleneCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 11 2004A. Kiashemshaki Abstract A model is developed for evaluating the performance of industrial-scale gas-phase polyethylene production reactors. This model is able to predict the properties of the produced polymer for both linear low-density and high-density polyethylene grades. A pseudo-homogeneous state was assumed in the fluidized bed reactor based on negligible heat and mass transfer resistances between the bubble and emulsion phases. The nonideal flow pattern in the fluidized bed reactor was described by the tanks-in-series model based on the information obtained in the literature. The kinetic model used in this work allows to predict the properties of the produced polymer. The presented model was compared with the actual data in terms of melt index and density and it was shown that there is a good agreement between the actual and calculated properties of the polymer. New correlations were developed to predict the melt index and density of polyethylene based on the operating conditions of the reactor and composition of the reactants in feed. [source] Application of support vector regression for developing soft sensors for nonlinear processes,THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 5 2010Saneej B. Chitralekha Abstract The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input,output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high-dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy-to-use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady-state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR-based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least-square-based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear-ARX model-based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input,output data. Le domaine du développement des capteurs logiciels a récemment gagné en importance avec la création d'outils de calcul efficaces et facilement utilisables à cette fin. L'idée de base est de convertir l'information obtenue dans les données d'entrée et de sortie recueillies à partir du processus dans un modèle mathématique. Un tel modèle mathématique peut servir de solution de rechange économique pour les capteurs matériels. L'outil de régression par machine à vecteur de support (RMVS) constitue un outil de calcul qui a récemment été l'objet de beaucoup d'attention dans la littérature des systèmes d'identification, surtout en raison de ses succès dans la création de modèles de boîte noire non linéaires. Dans ce travail, nous démontrons l'application de la RMVS comme outil efficace et facile à utiliser pour la création de capteurs logiciels pour les procédés non linéaires. Dans une étude de cas industrielle, nous illustrons le développement d'un capteur logiciel à indice de fluidité à état permanent pour un processus d'extrusion du polymère d'acétate de vinyle-éthylène à l'échelle industrielle en utilisant la RMVS. Le capteur logiciel fondé sur la RMVS, valide sur une vaste gamme d'indices de fluidité, a surclassé le capteur logiciel fondé sur les moindres carrés non linéaires existant en matière d'erreurs de prédiction plus faibles. Dans les deux autres études de cas, nous démontrons l'application de la RMVS pour la création de capteurs logiciels sous la forme de modèles dynamiques pour deux procédés non linéaires: un processus de neutralisation du pH simulé et un processus d'extrusion de polymère à deux vis à l'échelle laboratoire. Une procédure heuristique est proposée pour la création d'un capteur logiciel fondé sur un modèle ARX non linéaire dynamique en utilisant la RMVS, dans lequel on atteint automatiquement le délai optimal et les ordres en utilisant les données d'entrée et de sortie. [source] | |||||||||||||