A Flat Polymeric Membrane Sensor for Carbon Dioxide/Nitrogen Gas Mixture

A gas sensor was developed to measure the concentration of binary gas mixtures. This sensor works based on the permeability change of different gas mixtures across the polymeric membranes. Although high values of permeability and selectivity are needed for an ideal separation, the performance of this sensor mainly depends on the permeability factor. Polysulfone and silicone rubber were applied as the membrane base and coat, respectively. Moreover, in contrast to existing polymeric sensors that use hollow fibers, the present sensor is made of flat membranes. This new design is cheaper, smaller, and easier to use in comparison to the hollow fiber polymeric sensors. In order to test the sensor applicability, nitrogen and carbon dioxide were used as model gases. The effect of pressure on the response time and sensor accuracy was studied for the aforementioned gases. The response time (T_95%) of this low price sensor was 50?s, and the tolerance of measuring concentration was approximately 1.4% at 2?bar feed pressure. Also, increasing the feed pressure can improve the response time or accuracy of the sensor.     

The Ablation and Oxidation Behaviors of SiC Coatings on Graphite Prepared by Slurry Sintering and Pack Cementation

SiC coatings were generated on graphite using slurry sintering (SS) and pack cementation (PC). The samples’ ablation features were assessed by an oxyacetylene torch. The rates of mass ablation of the PC–SiC and SS–SiC coatings were approximated 2.17?×?10^?3 and 9.52?×?10^?3 g s^?1, respectively, decreased by 84.1 and 29.6% compared to the uncoated samples. It was mainly attributed to the formation of a SiO₂ layer on the surface. The continuous SiO₂ molten film formed via the PC–SiC oxidation generates a sealing mechanism which can be an obstacle against the oxygen diffusion and hinder more ablation. This is while discontinuous SiO₂ film formed from the thin SS–SiC cannot protect the graphite effectively. The non-isothermal oxidation test shows that without the SiC coating, the sample weight is lost largely from 25 to 1500 °C, and its weight loss was 2.2% after the TGA. However, after coating, the samples possessed excellent oxidation protection and weight losses of SS–SiC and PC–SiC coatings are down to 1.3 and 0.6%, respectively. The more oxidation of the graphite substrate occurred due to the formation of macrocracks in the coating during the TGA and also the formation of holes on SiO₂ glass layer owing to release of CO or CO₂.     

Synthesis and characterization of alumina supported sub-nanoporous SiO2–10 wt%TiO2 membrane for nitrogen separation

Membrane of SiO₂–10TiO₂ was produced by dip coating on mesoporous-titania-coated macroporous alumina support. The amorphous SiO₂–10TiO₂ top layer with thickness of 0.9 μm was obtained as a homogenous and defect free surface. Gas permeation tests show a decrease in permeation in order of CH₄ > N₂ for the support, and N₂ > CH₄ for the multilayer membrane. The membrane delivering promised single gas separation factor for N₂/CH₄ (1.75). It was also observed that the membrane separation factor in binary gas mixture was 12–15% of the single gas separation factor, while the permeation revealed a decrease in gas mixture.     

An innovative method of die design and evaluation of flow balance for thermoplastics extrusion profiles

In this article, a computational and experimental method for flow balancing of a U‐shaped die profile with nonuniform thicknesses is presented. The approach was to implement a flow restricting mechanism along the melt flow path. A parametric study based on the restrictor dimensions was carried out to attain a preliminary optimal design. Simulations were performed using Fluent software to analyze the flow velocity at the die exit. Experimental study was then carried out at various restrictor positions for the purpose of attaining a desirable flow balance. The velocity at various segments of the die exit was measured utilizing an innovative procedure by implementing the “separating blades.” Experimental findings were compared with those of simulations which showed an acceptable agreement. The results suggest that a flexible die can be designed to achieve a flow balance under various processing conditions. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers     

Bayesian approach to parameter estimation of the generalized pareto distribution

Several methods have been used for estimating the parameters of the generalized Pareto distribution (GPD), namely maximum likelihood (ML), the method of moments (MOM) and the probability-weighted moments (PWM). It is known that for these estimators to exist, certain constraints have to be imposed on the range of the shape parameter,k, of the GPD. For instance, PWM and ML estimators only exist fork>−0.5 andk≤1, respectively. Moreover, and particularly for small sample sizes, the most efficient method to apply in any practical situation highly depends on a previous knowledge of the most likely values ofk. This clearly suggests the use of Bayesian techniques as a way of using prior information onk. In the present work, we address the issue of estimating the parameters of the GPD from a Bayesian point of view. The proposed approach is compared via a simulation study with ML, PWM and also with the elemental percentile method (EPM) which was developed by Castillo and Hadi (1997). The estimation procedure is then applied to two real data sets.     

Retrieving relevant time‐course experiments: a study on Arabidopsis microarrays

Understanding time‐course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta‐analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time‐course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time‐course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time‐course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.Inspec keywords: botany, lab‐on‐a‐chip, genetics, bioinformatics, information retrieval, data mining, data analysis, associative processingOther keywords: relevant time‐course experiment retrieval, time‐course Arabidopsis microarray, time‐course gene regulation, stimulus response, systems biology, computational method, gene behaviour model, gene networked interaction, latent parameter, model parameter estimation, meta‐analysis, data analysis, time‐course gene expression experiment, information retrieval, computational framework, time‐course experiment query, relevant experiment list, repository, environmental factor, query experiment, experimental content similarity     

Optimum lipid production using agro-industrial wastewater treated microalgae as biofuel substrate

Abundant wastewater discharges from palm oil industries in tropical nations being a valuable resource of biodiesel need proper exploration. Research hinted that such wastewater as economical nutrient source or substrate can support the cultivation of microalgae. In this experiment, we have tested the growth and lipid production of five different microalgal strains in palm oil mill effluent (POME). POME as a biofuel substrate is demonstrated to be lucrative for microalgae-assisted lipids production. POME is rich in macro- and micronutrients can be used as a growth medium for algal growth in order to reduce the growth medium cost and environmental pollutions. Among the five microalgal strains tested, Chlorella sorokiniana revealed optimum biomass and lipid production. The productivity was evaluated in terms of chlorophyll content, growth rate, biomass, and lipid content, which discerned to be 0.099/day, 8.0 mg/L day and 2.68 mg/mg cell dry weight (CDW). Furthermore, in this study, an optimization study was carried out to enhance the microalgae to produce high lipid content using carbon-to-nitrogen ratio and different light/dark periods. The presence of nitrogen combined glucose (with a carbon-to-nitrogen ratio 100:7) as an alternative source to carbon displayed higher lipid production of 2.68 (mg/mg CDW) by C. sorokiniana. This study confirms that 8:16 h light/dark condition at C:TN ratio of 100:7 supported to produce high lipid content of 17 mg lipid/mg CDW. The above results revealed that POME could be a suitable growth media for the alga C. sorokiniana to improve the maximum lipid yield for biofuels production.     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

Modified self-organising maps with a new topology and initialisation algorithm

Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement.