Porous dye affinity beads for nickel adsorption from aqueous solutions: A kinetic study

We investigated a new adsorbent system, Reactive Red 120 attached poly(2‐hydroxyethyl methacrylate ethylene dimethacrylate) [poly(HEMA–EDMA)] beads, for the removal of Ni²⁺ ions from aqueous solutions. Poly(HEMA–EDMA) beads were prepared by the modified suspension copolymerization of 2‐hydroxyethyl methacrylate and ethylene dimethacrylate. Reactive Red 120 molecules were covalently attached to the beads. The beads (150–250 μm), having a swelling ratio of 55% and carrying 25.5 μmol of Reactive Red 120/g of polymer, were used in the removal of Ni²⁺ ions. The adsorption rate and capacity of the Reactive Red 120 attached poly(HEMA–EDMA) beads for Ni²⁺ ions was investigated in aqueous media containing different amounts of Ni²⁺ ions (5–35 mg/L) and having different pH values (2.0–7.0). Very high adsorption rates were observed at the beginning, and adsorption equilibria were then gradually achieved in about 60 min. The maximum adsorption of Ni²⁺ ions onto the Reactive Red 120 attached poly(HEMA–EDMA) beads was 2.83 mg/g at pH 6.0. The nonspecific adsorption of Ni²⁺ ions onto poly(HEMA–EDMA) beads was negligible (0.1 mg/g). The desorption of Ni²⁺ ions was studied with 0.1M HNO₃. High desorption ratios (>90%) were achieved. The intraparticle diffusion rate constants at various temperatures were calculated as k_20°C = 0.565 mg/g min^0.5, k_30°C = 0.560 mg/g min^0.5, and k_40°C = 0.385 mg/g min^0.5. Adsorption–desorption cycles showed the feasibility of repeated use of this novel adsorbent system. The equilibrium data fitted very well both Langmuir and Freundlich adsorption models. The pseudo‐first‐order kinetic model was used to describe the kinetic data. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100:5056–5065, 2006     

Development of flame retardancy properties of new halogen‐free phosphorous doped SiO2 thin films on fabrics

In this study, flame retardancy properties of fabrics treated with phosphorous (P) doped and undoped SiO₂ thin films were developed by sol–gel technique. As to this aim, P‐doped and undoped SiO₂ film were coated on cotton fabric from the solutions prepared from P, Si‐based precursors, solvent, and chelating agent at low temperature in air using sol–gel technique. To determine solution characteristics, which affect thin film structure, turbidity, pH values, and rheological properties of the prepared solutions were measured using a turbidimeter, a pH meter, and a rheometer machines before coating process. The thermal, structural, and microstructural characterization of the coating were done using differential thermal analysis/thermograviometry, fourier transform infrared spectroscopy, X‐ray diffractometry, and scanning electron microscopy. In addition, tensile strength, wash fastness, flame retandancy, and lightness properties of the coated fabrics were determined. To compensate the slight loss of tensile strength of samples, which occurred at the treated fabrics with P‐doped Si‐based solutions, the cotton fabrics were coated with polyurethane films during second step. In conclusion, the flame retardant cotton fabric with durability of washing as halogen‐free without requiring after treatment with formaldehyde was fabricated using sol–gel processing for the first time. Moreover the cotton fabrics, which were treated with P‐doped Si‐based solutions and then coated with polyurethane at second step, still has got nonflammable property. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

The CO-catalyzed conversion of H2S to H2 + sulfur part 2. The thermal decomposition of COS

The pyrolysis of COS has been studied over the temperature range 300 to 750°C using a variety of catalysts. The observed product distribution confirmed that two parallel reaction paths: 2 COS → 2 CO S₂ (2) and 2 COS → CO₂ + CS₂ (4) are involved in the decomposition. The decomposition yield increased with rising temperature, accompanied with a shift in selectivity. At temperatures lower than ～700°C the disproportionation reaction 4 was predominant, whereas at temperatures higher than 700°C, reaction 2 was favoured. In the high-temperature region (700 to 750°C) it was possible to achieve full suppression of reaction 4 with added CS₂. The pyrolysis of COS was also studied in a reactor packed with quartz chips without catalysts at high temperatures. Between 800 and 900°C, up to 99% conversion (with respect to the thermodynamic limit) could be achieved, with the almost complete absence of the disproportionation reaction 4. The results point to the commercial potential in the two-step reaction sequence: for the economic conversion of hydrogen sulfide to hydrogen and sulfur.     

Effects of polyamide 6 incorporation to the short glass fiber reinforced ABS composites: an interfacial approach

The properties of 30 wt% short glass fiber (SGF) reinforced acrylonitrile-butadiene-styrene (ABS) terpolymer and polyamide 6 (PA6) blends prepared with extrusion were studied using the interfacial adhesion approach. Work of adhesion and interlaminar shear strength values were calculated respectively from experimentally determined interfacial tensions and short beam flexural tests. The adhesion capacities of glass fibers with different surface treatments of organosilanes were evaluated. Among the different silanes tested, γ-aminopropyltrimethoxysilane (APS) was found to be the best coupling agent for the glass fibers, possibly, because of its chemical compatibility with PA6. Tensile test results indicated that increasing amount of PA6 in the polymer matrix improved the strength and stiffness of the composites due to a strong acid–base interaction at the interface. Incorporation of PA6 to the SGF reinforced ABS reduced the melt viscosity, broadened the fiber length distributions and increased the toughness of the composites. Fractographic analysis showed that the incorporation of PA6 enhanced the interactions between glass fibers and the polymeric matrix.     

Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an incremental learning algorithm to update the parameters of the neuro-fuzzy controller. In this way the latter is able to gradually replace the conventional controller from the control of the system. The proposed new learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters, leading the learning error toward zero. In the simulations and in the experimental studies, it has been tested on the control of antilock breaking system model and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.     

Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction

Accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In the last decade, a variety of modeling approaches have been developed and applied to predict HPC compressive strength from a wide range of variables, with varying success. The selection, application and comparison of decent modeling methods remain therefore a crucial task, subject to ongoing researches and debates. This study proposes three different ensemble approaches: (i) single ensembles of decision trees (DT) (ii) two-level ensemble approach which employs same ensemble learning method twice in building ensemble models (iii) hybrid ensemble approach which is an integration of attribute-base ensemble method (random sub-spaces RS) and instance-base ensemble methods (bagging Bag, stochastic gradient boosting GB). A decision tree is used as the base learner of ensembles and its results are benchmarked to proposed ensemble models. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining average determination of correlation, the best models for HPC compressive strength forecasting are GB–RS DT, RS–GB DT and GB–GB DT among the eleven proposed predictive models, respectively. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining determination of correlation (R²_max), the best models for HPC compressive strength forecasting are GB–RS DT (R²=0.9520), GB–GB DT (R²=0.9456) and Bag–Bag DT (R²=0.9368) among the eleven proposed predictive models, respectively.     

High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R²), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R²_BANN=0.9278, R²_GBANN=0.9270) are superior to a conventional ANN model (R²_ANN=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R²_WBANN=0.9397, R²_WGBANN=0.9528).     

Effect of DEM Resolution,Source, Resampling Technique and Area Threshold on SWAT Outputs

Application of an inappropriate Digital Elevation Model (DEM) might lead to uncertainty in modelling of the hydrological cycle. The novelty of this work is the development of a comprehensive framework to evaluate the effect of DEM resolution (12 to 500 m), source (TanDEM-X, SRTM, AW3D30 and ASTER GDEM2), resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority) and area threshold (1000 to 50,000 ha) on Soil and Water Assessment Tool (SWAT) outputs based on five criteria: (1) river network extraction, (2) streamflow simulation, (3) topography, slope and basin characteristics, (4) hydrological and (5) water quality simulations. Kelantan River Basin, a tropical basin in Peninsular Malaysia was selected as study area. The major findings are summarized as follows: (1) TanDEM-X had better river network extraction capability than ASTER GDEM2, (2) better monthly streamflow simulations were obtained between 20 m and 60 m DEM resolutions, with the smallest area threshold (1000 ha), (3) TanDEM-X and SRTM DEMs outperformed ASTER GDEM2 on monthly streamflow simulation, (4) DEM resolution, source and resampling technique were insensitive to most of the hydrological components, except the lateral flow, (5) area threshold was sensitive to SWAT-simulated surface runoff, soil water content and evapotranspiration, (6) DEM scenarios had a larger impact on sediment yield simulations compared to the total nitrogen and total phosphorus simulations. We recommend a preliminary assessment of DEM uncertainties on SWAT outputs to obtain more reliable modelling outputs.     

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.