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Model Able (model + able)
Selected AbstractsHigh-Pressure Polymerization of Ethylene in Tubular Reactors: A Rigorous Dynamic Model Able to Predict the Full Molecular Weight DistributionMACROMOLECULAR REACTION ENGINEERING, Issue 7 2009Mariano Asteasuain Abstract A rigorous dynamic model of the high-pressure polymerization of ethylene in tubular reactors is presented. The model is capable of predicting the full molecular weight distribution (MWD), average branching indexes, monomer conversion and average molecular weights as function of time and reactor length. The probability generating function method is applied to model the MWD. This technique allows easy and efficient calculation of the MWD, in spite of the complex mathematical description of the process. The reactor model is used to analyze the dynamic responses of MWD and other process variables under different transition policies, as well as to predict the effects of process perturbations. The influence of the material recycle on the process dynamics is also shown. [source] A three-dimensional model describing stress-temperature induced solid phase transformations: solution algorithm and boundary value problemsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 6 2004Ferdinando Auricchio Abstract An always increasing knowledge on material properties as well as a progressively more sophisticated production technology make shape memory alloys (SMA) extremely interesting for the industrial world. At the same time, SMA devices are typically characterized by complex multi-axial stress states as well as non-homogeneous and non-isothermal conditions both in space and time. This aspect suggests the finite element method as a useful tool to help and improve application design and realization. With this aim, we focus on a three-dimensional macroscopic thermo-mechanical model able to reproduce the most significant SMA features (Int. J. Numer. Methods Eng. 2002; 55: 1255,1264), proposing a simple modification of such a model. However, the suggested modification allows the development of a time-discrete solution algorithm, which is more effective and robust than the one previously discussed in the literature. We verify the computational tool ability to simulate realistic mechanical boundary value problems with prescribed temperature dependence, studying three SMA applications: a spring actuator, a self-expanding stent, a coupling device for vacuum tightness. The effectiveness of the model to solve thermo-mechanical coupled problems will be discussed in a forthcoming work. Copyright © 2004 John Wiley & Sons, Ltd. [source] A Three-Dimensional Quanititative Structure-Activity Relationship (3D-QSAR) Model for Predicting the Enantioselectivity of Candida antarctica Lipase BADVANCED SYNTHESIS & CATALYSIS (PREVIOUSLY: JOURNAL FUER PRAKTISCHE CHEMIE), Issue 9 2009Paolo Braiuca Abstract Computational techniques involving molecular modeling coupled with multivariate statistical analysis were used to evaluate and predict quantitatively the enantioselectivity of lipase B from Candida antarctica (CALB). In order to allow the mathematical and statistical processing of the experimental data largely available in the literature (namely enantiomeric ratio E), a novel class of GRID-based molecular descriptors was developed (differential molecular interaction fields or DMIFs). These descriptors proved to be efficient in providing the structural information needed for computing the regression model. Multivariate statistical methods based on PLS (partial least square , projection to latent structures), were used for the analysis of data available from the literature and for the construction of the first three-dimensional quanititative structure-activity relationship (3D-QSAR) model able to predict the enantioselectivity of CALB. Our results indicate that the model is statistically robust and predictive. [source] A practical method for predicting the short-time trend of bivoltine populations of Ips typographus (L.) (Col., Scolytidae)JOURNAL OF APPLIED ENTOMOLOGY, Issue 1 2006M. Faccoli Abstract:,Ips typographus is the main spruce pest of European forests. In most areas of the Italian Alps there are two generations per year; overwintering adults fly in May looking for trees suitable for breeding, their offspring emerge in summer, 7,8 weeks after tree colonization, and the adults of the second generation emerge in spring of the following year after overwintering under the bark or in the litter. A long-term population monitoring was carried out in north-east Italy with the aim at developing a prediction model able to estimate the population density of the following year. Between 1996 and 2004, pheromone traps monitored populations of I. typographus annually. Monitoring lasted 4 months (May,August), with replacement of pheromone dispensers after 8 weeks. Insects trapped before dispenser change were called ,spring captures' (May,June), and included both overwintering and re-emerging adults. Beetles caught after dispenser change were called ,summer captures' (July,August), and included the adults of the first generation. The results show a high positive correlation between the ratio of summer and spring captures of one year (Summerx/Springx), and the ratio of total captures of the following year (Yx+1) and those of the current year (Yx) (Yx+1/Yx). Summerx/Springx lower than 0.62 indicate decreasing populations in the following year (Yx+1/Yx <1), whereas Summerx/Springx higher than 0.62 indicate increasing populations (Yx+1/Yx >1). The applicability of the model in the study of I. typographus risk of outbreak and in the forest management is discussed. The prediction of the short-time trend of the population allows assessing its density in the following year, and therefore the risk of outbreak. [source] Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatographyJOURNAL OF CHEMOMETRICS, Issue 2 2008Tomislav Bolan Abstract The reliability of predicted separations in ion chromatography depends mainly on the accuracy of retention predictions. Any model able to improve this accuracy will yield predicted optimal separations closer to the reality. In this work artificial neural networks were used for retention modeling of void peak, fluoride, chlorite, chloride, chlorate, nitrate and sulfate. In order to increase performance characteristics of the developed model, different training methodologies were applied and discussed. Furthermore, the number of neurons in hidden layer, activation function and number of experimental data used for building the model were optimized in terms of decreasing the experimental effort without disruption of performance characteristics. This resulted in the superior predictive ability of developed retention model (average of relative error is 0.4533%). Copyright © 2008 John Wiley & Sons, Ltd. [source] Neuropathy-induced apoptosis: Protective effect of physostigmineJOURNAL OF NEUROSCIENCE RESEARCH, Issue 8 2009L. Di Cesare Mannelli Abstract Traumatic, infectious, metabolic, and chemical noxa to the nervous system are the etiology of a crippling disease generally termed neuropathy. Motor disorders, altered sensibility, and pain are the pathognomonic traits. Cellular alterations induced by this chronic pathology include mitochondrial dysfunctions that lead to the activation of the apoptotic cascade. Energy imbalance can compromise the maintenance of mitochondrial membrane potential, furthering the release of cytochrome C and the subsequent cleavage and activation of caspases. Chronic constriction injury (CCI) of the rat sciatic nerve is a neuropathy model able to induce a strong mitochondrial impairment with a consequent apoptotic induction. In this model, the acetylcholinesterase inhibitor physostigmine is administered at 0.125 mg/kg i.p. (twice per day) starting from the operation and for 15 days after. The cholinergic activation reduces cytosolic levels of cytochrome C, suggesting an improved stability of the mitochondrial membrane, and the expression level of the active caspase 3 fragments (19, 16 kDa) is reduced significantly with respect to saline treatment. Accordingly, physostigmine impairs caspase 3 protease activity. In fact, the target of the activated caspase 3, the 89-kDa PARP fragment, is significantly less expressed in the ligated nerve of physostigmine-treated rats, reaching levels that are comparable to those in the contralateral unligated nerve. Finally, this natural acetylcholinesterase inhibitor reduces DNA fragmentation both in the proximal and in the distal parts of the nerve. This protection correlates with the induction of XIAP. Therefore, apoptosis, central to tissue degeneration, is prevented by repeated physostigmine treatment of CCI animals. © 2009 Wiley-Liss, Inc. [source] Vacuum drying of wood with radiative heating: II.AICHE JOURNAL, Issue 1 2004Comparison between theory, experiment Abstract In part I of this work extensive experimental data sets for the vacuum drying of wood with radiative heating were presented for sapwood and heartwood of different species (Picea abies, Abies alba, and Fagus silvatica). These data sets are used here to validate two previously developed drying models. The first drying model, which is known as TransPore, is a comprehensive model able to capture the intricately coupled heat- and mass-transfer mechanisms that evolve throughout the drying process. The second model, which is known as Front_2D, uses a number of simplifying assumptions to reduce the complexity of the comprehensive model to a system that enables a semianalytical approach to be exploited for its solution. Although the first model provides a more accurate description of the entire process, the second model is able to produce representative solutions very efficiently in terms of overall computational times, making it a viable option for on-line control purposes. The comparison with experimental data highlights that both models are able to capture all of the observed trends, allowing them to be used with confidence for investigating the vacuum drying process at a fundamental level. The new contribution of this work lies in the fact that both models are used here for the first time to simulate drying at a reduced external pressure. © 2004 American Institute of Chemical Engineers AIChE J, 50: 108,118, 2004 [source] Numerical modeling of nonisothermal polymer crystallization kinetics: Flow and thermal effectsPOLYMER ENGINEERING & SCIENCE, Issue 10 2010Matthieu Zinet A numerical model able to simulate polymer crystallization under nonisothermal flows is developed. It is based on the assumption that the trace of the extra-stress tensor, calculated according to a viscoelastic multimode Upper Convected Maxwell (UCM) model, is the driving force of the flow-induced extra nucleation. Two distinct sets of Schneider equations are used to describe the growth of thermally and flow induced nuclei. The model is then coupled with the momentum equations and the energy equation. As an application, a shear flow configuration between two plates (Couette flow) is simulated. The relative influence of the mechanical and thermal phenomena on the crystallization development as well as the final morphology distribution is then analyzed as a function of the shearing intensity and the cooling kinetics, in terms of nucleation density and crystallite mean sizes. POLYM. ENG. SCI., 50:2044,2059, 2010. © 2010 Society of Plastics Engineers [source] Isotactic polypropylene solidification under pressure and high cooling rates.POLYMER ENGINEERING & SCIENCE, Issue 11 2000A master curve approach Solidification in industrial processes very often involves flow fields, high thermal gradients and high pressures: the development of a model able to describe the polymer behavior becomes complex. Recently a new equipment has been developed and improved to study the crystallization of polymers when quenched under pressure. An experimental apparatus based on a modified, special injection moulding machine has been employed. Polymer samples can be cooled at a known cooling rate up to 100°C/s and under a constant pressure up to 40 MPa. Density, Micro Hardness (MH), Wide angle X-ray diffraction (WAXD), and annealing measurements were then used to characterize the obtained sample morphology. Results on one iPP sample display a lower density and a lower density dependence on cooling rate for increasing pressure. Micro hardness confirms the same trend. A deconvolution technique of WAXD patterns is used to evaluate the final phase content of samples and to assess a crystallization kinetics behavior. A master curve approach to explain iPP behavior under pressure and high cooling rates was successfully applied on density results. On the basis of this simple model it is possible to predict the final polymer density by superposition of the effect of cooling rate and the effect of pressure in a wide range of experimental conditions. [source] Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid CompoundsCHEMICAL BIOLOGY & DRUG DESIGN, Issue 6 2010Káthia M. Honório Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher's weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: ELUMO (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz,Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons. [source] | ||||||||||||||||