Parameter identification and computational simulation of polyurethane foaming process by finite pointset method

Numerical simulation of the polyurethane foaming process is a valuable method to analyze the molding process at an early stage of product development to shorten time-to-market cycles and cut costs by using fewer prototypes. However, this process involves highly coupled thermo-chemo-rheological modeling and needs adequate model parameters’ identification. A theoretical model including chemical reactions and thermo-rheological coupling of conservation equations was developed. Based on the theoretical model, three-dimensional numerical simulation for mold filling of the polyurethane foam was carried out by using Finite Pointset Method (FPM) to predict flow field, flow front advancement, temperature and density distributions during mold filling. A FOAMAT system was used to monitor foam height rise and reaction temperature on a cylindrical test tube and foam viscosity was measured by using a dynamic rotational rheometer with parallel-plate system. The parameters of the model were identified by an inverse analysis method which consists in determining the parameters by comparing the computed quantities to those measured experimentally. The overall modeling was validated by using short shot foams obtained with a panel mold cavity. Mold filling of an automotive underlay carpet cavity was investigated numerically. Flow front results were successfully compared to short shot foams obtained with the industrial mold cavity.     

Prediction parameters of dimensioning of insulators under non-uniform contaminated conditions by multiple regression analysis

Surface tracking on solid insulators is one of the most severe breakdown mechanisms. Accurate assessment of real insulation conditions can be achieved by using the statistical techniques that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.To address the uncertainties that surround the exact physical mechanisms underlying the mathematical models, it has been shown that it is possible to include in the assessment of insulator performance contamination accumulation that is predicted by using a short historical of chemical components and atmospheric conditions. The results from this work are useful to predict the contamination severity, flashover voltage as a function of contamination severity, leakage current as a function of applied voltage, arc length and contamination severity, and especially to predict the flashover current.     

Experimental study of the performance of a cooling tower used in a solar distiller

In this work, we investigated experimentally the thermal performance of a forced cooling tower used in a solar desalination system based on humidification-dehumidification of air. The cooling tower is a counter flow wet one filled with film packing materials.The measured variables were obtained for wide ranges of mass flow rates of air and water as well as for several inlet water temperatures; the tower characteristic and efficiency were then evaluated and expressed in terms of water to air mass flow rate ratio.     

Spatial and seasonal variations and ecotoxicological significance of sediment trace metal concentrations in Kebir-Rhumel basin (Northeast of Algeria)

This study sought to assess sediment contamination by trace metals (cadmium, chromium, cobalt, copper, manganese, nickel, lead and zinc), to localize contaminated sites and to identify environmental risk for aquatic organisms in Wadis of Kebir Rhumel basin in the Northeast of Algeria. Water and surficial sediments (0-5 cm) were sampled in winter, spring, summer and autumn from 37 sites along permanent Wadis of the Kebir Rhumel basin. Sediment trace metal contents were measured by Flame Atomic Absorption Spectroscopy. Trace metals median concentrations in sediments followed a decreasing order: Mn > Zn > Pb > Cr > Cu > Ni > Co > Cd. Extreme values (dry weights) of the trace metals are as follows: 0.6-3.4 microg/g for Cd, 10-216 microg/g for Cr, 9-446 microg/g for Cu, 3-20 microg/g for Co, 105-576 microg/g for Mn, 10-46 microg/g for Ni, 11-167 microg/g for Pb, and 38-641 microg/g for Zn. According to world natural concentrations, all sediments collected were considered as contaminated by one or more elements. Comparing measured concentrations with American guidelines (Threshold Effect Level: TEL and Probable Effect Level: PEL) showed that biological effects could be occasionally observed for cadmium, chromium, lead and nickel levels but frequently observed for copper and zinc levels. Sediment quality was shown to be excellent for cobalt and manganese but medium to bad for cadmium, chromium, copper, lead, nickel and zinc regardless of sites.     

Quality assessment of polymer replication by hot embossing and micro-injection moulding processes using scanning mechanical microscopy

This paper describes observations and metrological analyses made to compare the replication quality of polymeric replicas obtained by filling micro-cavities using both hot embossing and micro-injection moulding processes. The experiments are performed with polypropylene (PP) at a constant melt temperature and a constant mould temperature, whereas hot embossing tests are carried out with the same polymer at temperatures close to the softening one.The results concerning the micro-cavities filling provide information on the reliability about the possibilities of replication topographical surface geometries. The data obtained by scanning mechanical microscopy (SMM) are used to determine the comparative filling ratio values.     

Physicochemical properties of fermented Arabian mares' milk

Due to its numerous benefits, including health-promoting characteristics, there is increasing interest in finding product applications for mares' milk. In the present study, the physicochemical properties, turbidity and conductivity of Arabian mares' milk were studied during acidification by fermentation at 30 °C and compared with those of bovine milk. The data for the acidification rate were modelled using a logistic equation. Mares' milk showed a longer latency phase (284 min) and lower acidification rate (μ_max = 0.0052 dpH min^?1) compared with bovine milk (194 min and 0.0098 dpH min^?1 respectively). The general shape of the curves of the turbidity versus time and pH were similar in nature for the acidification of both milks. However, the characteristic points were different, due to the differences in casein micelle composition. Measurement of electrical conductivity during acidification showed that maximum demineralization of casein micelles started around pH 6.09 and 5.31, for mares' and bovine milk, respectively. In conclusion, the technological characteristics of mares' milk were very different from those of bovine milk, due to intrinsic physicochemical properties of both milks.     

Bayesian Unsupervised Learning for Source Separation with Mixture of Gaussians Prior

This paper considers the problem of source separation in the case of noisy instantaneous mixtures. In a previous work [1], sources have been modeled by a mixture of Gaussians leading to an hierarchical Bayesian model by considering the labels of the mixture as i.i.d hidden variables. We extend this modelization to incorporate a Markovian structure for the labels. This extension is important for practical applications which are abundant: unsupervised classification and segmentation, pattern recognition and speech signal processing. In order to estimate the mixing matrix and the a priori model parameters, we consider observations as incomplete data. The missing data are sources and labels: sources are missing data for observations and labels are missing data for incomplete missing sources. This hierarchical modelization leads to specific restoration maximization type algorithms. Restoration step can be held in three different manners: (i) Complete likelihood is estimated by its conditional expectation. This leads to the EM (expectation-maximization) algorithm [2], (ii) Missing data are estimated by their maximum a posteriori. This leads to JMAP (Joint maximum a posteriori) algorithm [3], (iii) Missing data are sampled from their a posteriori distributions. This leads to the SEM (stochastic EM) algorithm [4]. A Gibbs sampling scheme is implemented to generate missing data. We have also introduced a relaxation strategy into these algorithms to reduce the computational cost which is due to the exponential influence of the number of source components and the number of the mixture Gaussian components.     

A neural network model based predictive control approach: application to a semi-batch reactor

Neural networks can be considered to be new modelling tools in process control and especially in non-linear dynamical systems cases. Their ability to approximate non-linear functions has been very often demonstrated and tested by simulation and experimental studies. In this paper, a predictive control strategy of a semi-batch reactor based on neural network models is proposed. Results of a non-linear control of the reactant temperature of a semi-batch reactor are presented. The process identification is composed of an off-line phase that consists in training the network, and of an on-line phase that corresponds to the neural model adaptation so that it fits any modification of the process dynamics. Experimental results when using this method to control a semi-batch reactor are reported and show the great potential of this strategy in controlling non-linear processes.