An investigation into the effect of fabric structure on the compressional properties of woven fabrics

Lateral compression is one of the most important mechanical aspects of fabrics, which reflects their handle. Fabric compressional features depend on the compressional characteristics of constituent yarns and the fabric structure. In order to consider the effect of fabric structural parameters on its compressional properties, woven fabrics with five different weave patterns (plain, hopsack 2/2, twill 2/2, twill 3/1, warp rib 2/2) were produced with three different nominal weft densities (12, 15, 18 cm^?1). The compressional properties of produced fabrics were evaluated at different pressure values using a conventional fabric thickness tester. It was observed that increasing the weft density leads to decrease in the dissipated compression energy as well as the compressibility of the fabric, while the thickness recovery of the fabric increases. Moreover, the plain woven fabric exhibited the lowest dissipated compression energy and compressibility, while the highest thickness recovery. Besides, at the low pressure level, the fabrics with the lower weft densities demonstrate the higher thickness. By increase in the pressure level, the fabric thickness decreases by decreasing the weft density.     

Genetic-based multi-objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

The purpose of this research is to find the optimal operating point in the production process of the cumene. Therefore, the production process was optimized through statistical and genetic algorithm-based methods. The performance of an alkylation reactor was optimized through maximizing the yield of cumene production. Response surface methodology (RSM) with design type of central composite was applied for design of experiment, modelling, and optimizing the process. The analysis of variance (ANOVA) was performed for finding the important operative parameters as well as their effects. The effects of three parameters including temperature, reactor length, and pressure on the alkylation process were investigated. Further, two types of feed-forward neural network were applied to model the alkylation reactor. To develop the neural network model, leave-one-out method was used. The best prediction performance belonged to a fitting network with 2 and 8 neurons in the hidden layer, respectively. This model was used for optimizing the performance of the alkylation reactor. The statistical and artificial intelligence systems were capable of prediction of cumene production yield in different conditions with R² of 0.9098 and 0.9986, respectively. Genetic algorithm-based optimization was performed by the developed neural network model. The maximum accessible value of cumene production yield was 0.7771, which can be achieved when the temperature, length of reactor, and column pressure are 160°C, 2 m, and 4000 kPa, respectively. By finding the optimal operating point in the cumene production process, capital cost, energy consumption, and other operating costs can be significantly reduced.     

Microstructure of a compacted soil submitted to an alkaline PLUME

In the French concept of deep nuclear wastes repository, the galleries should be backfilled with excavated argillite after the site exploitation period. After several thousands of years, the degradation of the concrete lining of the galleries will generate alkaline solute (pH > 12) that will diffuse through the backfill. The object of the paper is to describe the influence of such solute diffusion on the properties of compacted argillite. Since it is planned to introduce additives (bentonite, calcareous sand or lime) in the remoulded argillite to backfill the deep galleries, such mixtures were also studied. Saturated-portlandite water was circulated through compacted samples for 3, 6 and 12 months at 20 or 60 °C. The microstructure before and after fluid circulation was determined with mercury intrusion porosimetry. The results showed that the 50% sand–50% argillite mixture microstructure was slightly affected by the alkaline fluid circulation. Conversely, the microstructure of the other tested materials changed dramatically. The fluid circulation provoked an increase of the macropore void ratio by almost 3 times for the pure argillite, almost 1.5 for the MX-80 and argillite mixture and 2 for the lime-treated argillite. This particular behaviour could be attributed to the dissolution of soil minerals in such a high pH environment. Hence, it is likely that hydraulic and mechanical properties of these mixtures would be dramatically affected by alkaline solute diffusion over a long period of time.