Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

Physico-chemical properties of non-newtonian shear thickening diisopropyl-ethylammonium-based protic ionic liquids and their mixtures with water and acetonitrile

New protic ionic liquids (PILs) based on the diisopropyl-ethylammonium cation have been synthesized through a simple and atom-economic neutralization reaction between the diisopropyl-ethylamine and selected carboxylic acid. Densities and rheological properties were then measured for two original diisopropyl-ethylammonium-based protic ionic liquids (heptanoate and octanoate) at 298.15 K and atmospheric pressure. The effect of the presence of water or acetonitrile on the measured values was also examined over the whole composition range at 298.15 K and atmospheric pressure. From these values, excess properties were calculated and correlated by using a Redlich-Kister-type equation. Finally, a qualitative analysis of the evolution of studied properties with the alkyl chain length of the anion and with the presence or not of water (or acetonitrile) was performed. From this analysis, it appears that selected PILs and their mixtures with water or acetonitrile have a non-Newtonian shear thickening behavior, and the addition of water or acetonitrile on these PILs increases this phenomena by the formation of aggregates in these media.

Multiloop robust H∞ control design based on the dynamic PLS approach to chemical processes

In this paper, a multiloop robust control strategy is proposed based on H∞ control and a partial least squares (PLS) model (H∞_PLS) for multivariable chemical processes. It is developed especially for multivariable systems in ill-conditioned plants and non-square systems. The advantage of PLS is to extract the strongest relationship between the input and the output variables in the reduced space of the latent variable model rather than in the original space of the highly dimensional variables. Without conventional decouplers, the dynamic PLS framework automatically decomposes the MIMO process into multiple single-loop systems in the PLS subspace so that the controller design can be simplified. Since plant/model mismatch is almost inevitable in practical applications, to enhance the robustness of this control system, the controllers based on the H∞ mixed sensitivity problem are designed in the PLS latent subspace. The feasibility and the effectiveness of the proposed approach are illustrated by the simulation results of a distillation column and a mixing tank process. Comparisons between H∞_PLS control and conventional individual control (either H∞ control or PLS control only) are also made

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Transesterification of vegetable oils on basic large mesoporous alumina supported alkaline fluorides-Evidences of the nature of the active site and catalytic performances

KF, LiF and CsF/A(2)O(3) catalysts with different loadings from 1 to 20 wt% were prepared using aqueous solutions of the alkaline fluoride compounds by wet impregnation of basic mesoporous MSU-type alumina. The catalysts were activated under At at 400 degrees C for 2 h and monitored by in situ XRD measurements. The catalysts were also characterized using several techniques: N-2 adsorption/desorption isotherms at -196 degrees C, FTIR, DR-UV-vis, CO2-TPD, XRD, Al-27 CP/MAS NMR. These characterizations led to the conclusion that the deposition of alkaline fluorides on the alumina surface generates fluoroaluminates and aluminate species. The process is definitivated at 400 degrees C. The fluorine in these structures is less basic than in the parent fluorides, but the oxygen becomes more basic. The catalysts were tested for the transesterification of fatty esters under different experimental conditions using conventional heating, microwave and Ultrasound irradiation. Recycling experiments showed that these catalysts are stable for a limited number of cycles. (C) 2009 Elsevier Inc. All rights reserved.

Differential physico-chemical tolerances and intraguild predation among native and invasive amphipods (Crustacea); a field study

We used field surveys and transplant experiments to elucidate the relative roles of physico-chemical regime and intraguild predation in determining the generally mutually exclusive distributions of native and invader freshwater amphipod species. Field surveys showed that the native Gammarus duebeni celticus dominates the shoreline of Lough Neagh, N. Ireland, with some co-occurrence with the N. American invader G. tigrinus. However, the latter species dominates the deeper areas of the mid-Lough. Transplant experiments showed no difference in survival of the native and invader in single species 'bioassay tubes' placed along the shoreline. However, there was significantly higher survival of the invader compared with the native in single species tubes placed in the mid-Lough. In mixed species tubes on the shoreline, the native killed and ate the invader, with no reciprocal interaction, leading to significant reductions of the invader. However, the invader had significantly higher survival than the native in mixed species tubes in the mid-Lough, with no evidence. of predation between the two species. These results indicate that, whereas differential intraguild predation may determine domination of the shoreline by the native, differential physico-chemical tolerances may be major determinants of the domination of the mid-Lough by the invader. This study emphasises the need to consider the habitat template in conjunction with biotic interactions before attempting to draw conclusions about mechanisms determining relative distribution patterns of native and invasive species.