Study of droplet behaviour along a pulsed liquid–liquid extraction column in the presence of nanoparticles

In this article, droplet size and its distribution along a pulsed liquid–liquid extraction column, is studied where SiO₂ nanoparticles with concentrations of 0.01, 0.05 and 0.1 vol.% and different hydrophobicities are applied to the dispersed phase. Using ultrasonication, nanoparticles were dispersed in kerosene as the base fluid. Nanofluids' stability was ensured using a UV–vis spectrophotometer. Some 22,000 droplets were measured by photographic technique and results were compared with systems containing no‐nanoparticles (Water–Acetic acid–Kerosene). Addition of nanoparticles changed the droplet shape from ellipsoidal to spherical. Also, there was a marked influence on droplet breakage and droplet coalescence at 0.01 vol.%, and 0.05 vol.% or higher volume fractions, respectively. © 2012 Canadian Society for Chemical Engineering     

Polymeric mixed matrix membranes containing zeolites as a filler for gas separation applications: A review

Polymeric membrane technology has received extensive attention in the field of gas separation, recently. However, the tradeoff between permeability and selectivity is one of the biggest problems faced by pure polymer membranes, which greatly limits their further application in the chemical and petrochemical industries. To enhance gas separation performances, recent works have focused on improving polymeric membranes selectivity and permeability by fabricating mixed matrix membranes (MMMs). Inorganic zeolite materials distributed in the organic polymer matrix enhance the separation performance of the membranes well beyond the intrinsic properties of the polymer matrix. This concept combines the advantages of both components: high selectivity of zeolite molecular sieve, and mechanical integrity as well as economical processability of the polymeric materials. In this paper gas permeation mechanism through polymeric and zeolitic membranes, material selection for MMMs and their interaction with each other were reviewed. Also, interfacial morphology between zeolite and polymer in MMMs and modification methods of this interfacial region were discussed. In addition, the effect of different parameters such as zeolite loading, zeolite pore size, zeolite particle size, etc. on gas permeation tests through MMMs was critically reviewed.     

An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach

Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.     

Biocompatible graphene-embedded PCL/PGS-based nanofibrous scaffolds: A potential application for cardiac tissue regeneration

Research in the field of tissue engineering, especially heart tissue engineering, is growing rapidly. Herein, the morphological, chemical, mechanical and biological properties of poly (caprolactone) (PCL)/poly (glycerol sebacate) (PGS) and PCL/PGS/graphene nanofibrous scaffolds are investigated. Initially, PGS pre-polymer is synthesized and characterized by nuclear magnetic resonance and Fourier transform infrared spectroscopies. Then, in order to use the benefits of PGS, this polymer is mixed with PCL. Blending PGS with PCL resulted in the enhancement of ultimate elongation and reduction in the elastic modulus due to the intrinsic properties of PGS. The hydrophobicity of PCL nanofibers is reduced by adding PGS as hydrophilic polymer (105 ± 3° vs. 44 ± 2°). Also, the addition of graphene to the blend nanofibers is balanced the hydrophilicity. Degradation rate of pure PCL nanofibers is very slow but it is increased in the presence of PGS. All nanofibrous scaffolds are biocompatible and non-toxic. The highest cell adhesion (covered area = 0.916 ± 0.032) and biocompatibility (98.79 ± 1%) are related to PCL/PGS loaded with 1% wt of graphene (PCL/PGS/graphene ₁). Thus, this sample can be a good candidate for further examinations of cardiac tissue engineering.     

Prediction of thiophene removal from diesel using [BMIM][AlCl4] in EDS Process: GA-ANFIS and PSO-ANFIS modeling

In this study, the treatment capability of 1-butyl-3-methylimidazolium tetrachloroaluminate [BMIM] [AlCl4] ionic liquid was investigated in a diesel fuel containing thiophene. The effects of operating parameters including the volumetric ratio of ionic liquid to model fuel, extraction temperature and time have been studied. Then, a neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed to predict the sulphur removal from diesel fuel using [BMIM][AlCl4] in EDS process. The outputs were in a good agreement with the experimental data, and the model tuned by PSO predicts the data better than ANFIS and ANFIS tuned by GA.     

Performance prediction of a specific wear rate in epoxy nanocomposites with various composition content of polytetrafluoroethylen (PTFE), graphite,short carbon fibers (CF) and nano-TiO2 using adaptive neuro-fuzzy inference system (ANFIS)

Specific wear rate of composite materials plays a significant role in industry. The processes to measure it are both time and cost consuming. It is essential to suggest a modeling method to predict and analyze the effectiveness of parameters of specific wear rate. Nowadays, computational methods such as Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as applicable tools from modeling point of view. ANFIS present integrate performance of neural network (NN) and fuzzy system (FS). Present paper investigates performance prediction of a specific wear rate of epoxy composites with various composition using ANFIS. The obtained results showed that ANFIS is a powerful tool in modeling specific wear rate. The obtained mean of squared error (MSE) for testing sets in present paper obtained 0.0071.     

采用响应面方法和中心复合旋转设计对低品位锰矿浸出过程建模与优化(英文)

采用实验设计软件对从低品位锰矿中浸出提取锰的过程进行优化。在中心复合响应面实验设计中,考察了4个主要影响浸出过程的参数,即硫酸浓度、草酸浓度、浸出时间和温度。将锰和铁的浸出率作为考察指标。采用统计分析和方差分析确定了最优条件,即最高的锰和铁浸出率、最短的浸出时间和最低的温度。结果表明,硫酸浓度是影响浸出过程的最显著的参数,在最优条件下:硫酸浓度7%,草酸浓度42.5g/L,浸出时间60min,反应温度65℃,锰和铁的浸出率可分别达到93.44%和15.72%。     

Study of the heating rate effect on the glass transition properties of (60 − x)V2O5–xSb2O3–40TeO2 oxide glasses using differential scanning calorimetry (DSC)

The glass-transition temperature (T_g) and crystallization temperature (T_Cr) have been determined for the system (60 − x)V₂O₅–xSb₂O₃–40TeO₂ with 0 < x < 10 (in mol%) using differential scanning calorimetry (DSC) at heating rates φ = 3, 6, 9 and 13 K/min. The effect of the heating rate and the Sb₂O₃ content on T_g is discussed. It was observed that the transition region shifts to higher temperatures when the measuring time is reduced (or, conversely, when the applied temperature rate is increased). Using differential scanning calorimetry, the compositional dependence of T_g has been determined and so, an empirical equation has been deduced which relates the glass-transition temperature with the Sb₂O₃ content.     

Characterization and activity of alkaline earth metals loaded CeO2–MOx (M = Mn,Fe) mixed oxides in catalytic reduction of NO

Nanocrystalline CeO₂–MO_x mixed oxides (M = Mn, Fe) with different M/(M + Ce) molar ratio are prepared by sol–gel combustion method. X-Ray Diffraction (XRD), Transmission Electron Microscopy (TEM), Temperature Programmed Reduction with H₂ (H₂-TPR) and N₂-adsorption (BET) analyses are conducted to characterize the physical–chemical properties of the catalysts. The activity of catalysts for reduction of NOx with ammonia has been evaluated. The CeO₂–MnO_x catalysts showed better low temperature activity than CeO₂–FeO_x. The superior activity of CeO₂–MnO_x with Mn/(Mn + Ce) molar ratio of 0.25 respect to other catalysts (with 83% NO conversion and 68% N₂ yield at 200 °C) is associated to nanocrystalline structure, reducibility at low temperature and synergistic effect between Ce and Mn that are observed by XRD, TEM and H₂-TPR. The CeO₂–FeO_x catalysts were found to be active at high temperature, being Ce–Fe the best catalyst yielded 82% NO conversion at 300 °C. The effect of alkaline earth metals (Ca, Mg, Sr and Ba) loading on the structure and catalytic activity of cerium mixed oxides are also investigated. Loading of Ba enhanced the NO reduction activity of mixed oxides due to the increase of number of basic sites. Highest performance with 91% NO conversion and 80% N₂ yield attained over CeO₂–MnO_x (0.25)-Ba (7%) catalyst at 200 °C.