Nanotechnology and supercritical fluids

Supercritical fluid (SCF) technology has become an important tool of materials processing in the last two decades. Supercritical CO2 and H2O are extensively being used in the preparation of a great variety of nanomaterials. The interest in the preparation and application of nanometer size materials is increasing since they can exhibit properties of great industrial interest. Several techniques have been proposed to produce nanomaterials using supercritical fluids. These processes, taking advantage of the specific properties of supercritical fluids, are generally flexible, more simplified and with a reduced enviromental impact. The result is that nanomaterials with potentially better performances have been obtained. We propose a critical review of the supercritical based techniques applied to the production of nanoparticles materials.Keywords: Supercritical fluids; Nanoparticles; SCF technology; RESS; SAS

Applications of SSAFT EOS for determination of the solubilities of solid compounds in supercritical CO2.

Using statistical thermodynamics such as Simplified SAFT equation of state (SSAFTEoS) for estimating phase equilibrium and fluid properties of different materials have been used widely. SSAFT EoS has been developed for associative and non-associative compounds. At high pressure inter molecular forces are very important, on the other hand, in spite of the fact that SSAFT EoS has strength theoretical foundation, it can predict the behavior of high pressure systems. In this research, four solid solubility of benzoic acid, naphthalene, pyrene and Phenanthrene in supercritical carbon dioxide have been studied, SSAFT EoS has been used for modeling. At the end the results have been compared with experimental data. The highest and the absolute average deviation error (AAPD), for carbon dioxide - Phenanthrene and benzoic acid - carbon dioxide systems have been reported 2.22 and 4.43 respectively.Keywords: SSAFT EoS; Supercritical carbon dioxide; Solid compounds; Solubility

Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters

Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) and also least square support vector machine (LSSVM) was designed to support real-time environmental monitoring of water quality parameters, i.e. chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a Lake reservoir. Daily measurements of Chl-a and DO for June 2012–May 2013 were employed where the partial autocorrelation function was applied to screen the relevant inputs for the model construction. The variables were then split into training, validation and testing subsets where the first stage of the model testing captured the superiority of the ELM over the LSSVM algorithm. To improve these standalone predictive models, a second stage implemented a two-layer decomposition with the model inputs decomposed in the form of high and low frequency oscillations, represented by the intrinsic mode function (IMF) through the CEEMDAN algorithm. The highest frequency component, IMF1 was further decomposed with the VMD algorithm to segregate key model input features, leading to a two-layer hybrid VMD-CEEMDAN model. The VMD-CEEMDAN-ELM model was able to reduce the root mean square and the mean absolute error by about 14.04% and 7.12% for the Chl-a estimation and about 5.33% and 4.30% for the DO estimation, respectively, compared with the standalone counterparts. Overall, the developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets. © 201