Application of polyuronides for removing heavy metals from vegetable oils

Summary Laboratory experiments have been carried out for the removal of heavy metals from hydrogenated vegetable oils using hydrated polyuronides (degree of swelling from 4 to 12.8 ml/g) such as alginic acid, pectic and pectinic acids. The effect of the type of polyuronide, degree of esterification and oil treatment on the degree of demetalization has been studied. It has been shown that with increase in the degree of esterification of the polyuronide the efficiency of demetalization decreases. The second and third treatment of the hydrogenated oil with pectinic acid resulted in a high degree of heavy metal removal. The possibility of efficient demetalization of hydrogenated oils by treatment with water solutions of pectinic acids has also been demonstrated. The degree of metal ion removal increases with decreasing concentration of pectinic acids in the water solution.

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Einsatz von Polyuroniden zur Entfernung von SchwermtaUen aus pflanzlichen Ölen III. Einsatz von Alginsäure, Pecto- und Pectinsäuren zur Entmetallisierung von hydriertem Sonnenblumenöl

Zusammenfassung Es wurden Laboruntersuchungen zur Entfernung von Schwermetallen aus hydrierten pflanzlichen Ölen mit hydratisierten Polyuroniden (Quellungsgrad von 4 bis 12,8 ml/g) wie z. B. Alginsäure, Pecto- und Pectinsäuren durchgeführt. Es wurde der Einfluß des Polyuronids, des Veresterungsgrades und der Bearbeitungsart des Öles auf den Entmetallisierungsgrad verfolgt. Es wurde gezeigt, daß mit dem Veresterungsgrad des Polyuronids die Entmetallisierung herabgesetzt wird. Bei einer zwei- und dreifachen Bearbeitung des hydrierten Öles mit Pectinsäure wird die Entferung der Schwermetalle weitgehend erreicht. Es wurde auch die Möglichkeit für eine effektive Entmetallisierung von hydrierten Ölen durch Bearbeitung mit wäßrigen Lösungen von Pectinsäuren bewiesen. Der Grad der Entfernung der Metallionen erhöht sich mit einer Verringerung der Konzentration der Pectinsäuren im Wasser.

     

Numerical modelling of laminar-turbulent transition in the boundary layers under the influence of low free-stream turbulence intensities

Predictions of laminar-turbulent transition in the two-dimensional and axisymmetric boundary layers under the influence of low free-stream turbulence levels are presented. A ‘near-wall’ version of the k- turbulence model is used for simulation of turbulent transport. The calculations are based on Patankar and Spalding's finite difference algorithm. It is known that for high free-stream turbulence levels, the k- model simulates the transition well but it fails for low ones. In the present work, calculations are successfully, made for low turbulence levels using Abu-Ghannam and Shaw's correlation for determination of the flow transition point and Dhawan and Narasimha's intermittency factor in the transition zone. The examples include various levels of free-stream turbulence as well as a variety of boundary layers on a rotary ellipsoid. The numerical results are compared with experimental data and the agreement is good.     

Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming

This article presents a combination of unsupervised and supervised learning techniques for the generation of word segmentation rules from a raw list of words. First, a language bias for word segmentation is introduced and a simple genetic algorithm is used in the search for a segmentation that corresponds to the best bias value. In the second phase, the words segmented by the genetic algorithm are used as an input for the first order decision list learner CLOG. The result is a set of first order rules which can be used for segmentation of unseen words. When applied on either the training data or unseen data, these rules produce segmentations which are linguistically meaningful, and to a large degree conforming to the annotation provided.     

Ozone Decomposition over Alumina-Supported Copper,Manganese and Copper-Manganese Catalysts

Samples of three series alumina supported catalysts of copper, manganese and copper-manganese oxides are investigated with respect to ozone decomposition at low temperatures. The catalysts are found to be active in the reaction studied and the most active are mixed copper-manganese catalysts—they do not change their activity for prolonged time. The reason for the enhanced activity is suggested to be the presence of copper and manganese in various oxidation states.     

Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding

Real-time estimation of weld quality from process data is one of the key objectives in current weld control systems for resistance spot-welding processes. This task can be alleviated if the weld controller is equipped with a voltage sensor in the secondary circuit. Replacing the goal of quantifying the weld quality in terms of button size by the more modest objective of indirect estimation of the class of the weld, e.g., satisfactory (acceptable, “normal” button size), unsatisfactory (undersized, “cold” welds), and defects (“expulsion”), further improves the feasibility of the mission of indirect estimation of the weld quality. This paper proposes an algorithmic framework based on a linear vector quantization (LVQ) neural network for estimation of the button size class based on a small number of dynamic resistance patterns for cold, normal, and expulsion welds that are collected during the stabilization process. Nugget quality classification by using an LVQ network was tested on two types of controllers; medium-frequency direct current (MFDC) with constant current controller and alternating current (AC) with constant heat controller. In order to reduce the dimensionality of the input data vector, different sets of features are extracted from the dynamic resistance profile and are compared by using power of the test criteria. The results from all of these investigations are very promising and are reported here in detail.     

Microstructural evaluation of the mucin balls and their relations to the corneal surface—Insights by in vivo confocal microscopy

Purpose

The purpose of the current study was to observe and correlate the characteristics of mucin balls to the ocular surface properties, and furthermore, to report the effect of different mucin balls size and number on structural alteration of the anterior cornea.

Features of the attenuation and single-sided imaging potential of near-infrared laser radiation in tissue-like liquid turbid media

Tissue-like phantoms are important tools in studying light propagation and scattering in biological tissues and in the development, testing and calibration of novel optical diagnostic and therapeutic methods and instruments. This motivates the interest in characterizing the optical properties of tissue-simulating turbid media. In the present work, using an original approach, the specific features have been revealed experimentally and interpreted physically (as owing to the polydispersity of the ensemble of scatterers) of the generally non-linear behaviour of the extinction (total attenuation) coefficient of Intralipid-20% dilutions in distilled water, depending on the Intralipid concentration, for near-infrared laser radiation of different wavelengths. At relatively low incremental concentrations, the values obtained of the extinction coefficient are shown to increase linearly being in agreement at each wavelength of concern with results for the integral scattering coefficient predicted by empiric formulae found by other authors. Comparative estimation has been performed as well, depending on the radiation wavelength, of the potentialities of single-sided optical sensing of tissue-like media. The estimates obtained outline the advantage of the wavelengths around the upper limit of the tissue optical window.     

Über die polymerisation der kokschemischen inden-cumaron-fraktion

The influence of the catalysts BF₃O(C₂H₅)₂, FeCl₃ and J₂ on the polymerization of inden-cumaron-fractions was investigated. The dependence of the polymerization time on the resin yield was shown. Kinetic constants and reaction orders were determined.     

Ultrahigh Performance Nanoengineered Graphene–Concrete Composites for Multifunctional Applications

There is a constant drive for development of ultrahigh performance multifunctional construction materials by the modern engineering technologies. These materials have to exhibit enhanced durability and mechanical performance, and have to incorporate functionalities that satisfy multiple uses in order to be suitable for future emerging structural applications. There is a wide consensus in the research community that concrete, the most used construction material worldwide, has to be engineered at the nanoscale, where its chemical and physiomechanical properties can be truly enhanced. Here, an innovative multifunctional nanoengineered concrete showing an unprecedented range of enhanced properties when compared to standard concrete, is reported. These include an increase of up to 146% in the compressive and 79.5% in the flexural strength, whilst at the same time an enhanced electrical and thermal performance is found. A surprising decrease in water permeability by nearly 400% compared to normal concrete makes this novel composite material ideally suitable for constructions in areas subject to flooding. The unprecedented gamut of functionalities that are reported in this paper are produced by the addition of water‐stabilized graphene dispersions, an advancement in the emerging field of nanoengineered concrete which can be readily applied in a more sustainable construction industry.     

Summarizing data using a similarity based mountain method

We consider the problem of summarizing a collection of data values. Here we use a mountain method like approach based on the similarities of the data. Fundamental to our work is the possibility of allowing for multiple summarizing values. We present an algorithm, in the spirit of the mountain method, that uses the similarity between the data points to find focus points which serve as the seed for finding summarizing centers. Central to this algorithm is a process of reducing the energy of the data points which we show can be implemented most generally using a t-norm. We provide an application of the algorithm to the problem of binning data which is used in data mining and the development of histograms. Here we allow the location of the bins to be determined by the data rather then fixed a priori.