The article presents the results of research aimed at increase of the efficiency of gas cleaning equipment based on the Venturi tube using high-intensity ultrasound. The model based on known laws of hydrodynamics of multiphase mediums of dust-extraction in Venturi scrubbers was proposed. Modification of this model taking into account ultrasonic field allows evaluating optimum modes (sound pressure level) and conditions (direction of ultrasonic field, square and number of ultrasonic sources) of ultrasonic influence. It is evaluated that optimum for efficient gas cleaning is the mode of ultrasonic action at the frequency of 22 kHz with sound pressure level of 145. . . 155 dB at the installation of two radiators with area of 0.14 m2, four radiators with area of 0.11 m2 or six radiators with area of 0.08 m2 at the angle of 45 degrees to the axis of Venturi tube. Numerical calculations showed that realization of ultrasonic action is the most efficient for the reduction (up to 15 times) of the content of fine-dispersed fraction (2 μm and less), which is impossible to extract without ultrasonic action. The received theoretical results were confirmed by industrial testing by typical dust-extraction plant and used as foundations of development of apparatuses with the radiators of various sizes.
In this paper, the authors investigated the size distribution of titanium oxide (TiO2), titanium nitride (TiN) and titanium carbide (TiC) inclusions in a titanium deoxidized 4130 steel and compared it with the 4130 base alloy composition inclusions. TiN and TiC inclusions are of particular interest due to their role as heterogeneous nuclei for various phase reactions in steels. Two types of samples were prepared, a polished sample and a filtered sample. Electrolytic dissolution was employed to make the filter paper samples. The size range of titanium inclusions was found to be more than that of the non-metallic inclusions from 4130 base alloy heat. Titanium inclusions from the filter and polished samples were round in shape. TiC and TiN inclusions were not found in the electrolytic extraction samples. Inclusions and their chemistries were analyzed using scanning electron microscope and energy dispersive spectrometer. The inclusion size range was larger for the titanium deoxidized samples than the base alloy. However, in both steels the majority of inclusions had a size smaller than 10 μm.
This investigation is concerned with the extraction of nugget copper particles from copper recovery plant slag which recycled of copper scrap. For this purpose, the Falcon concentrator was used because of its enhanced gravity properties. The Falcon concentrator has a fast spinning bowl which creates a centrifugal force to separate fine size minerals on the basis of their density differences. In the tests, the tailings of the copper recovery plant were used and the test sample was divided into two groups and one of them was classified in narrow particle sizes. The operational parameters were determined as particle size, centrifugal force and washing water pressures. The water pressure and centrifugal force have an inversely proportional relationship. Because of this phenomenon, the G/P parameter was created. The test conditions were applied to the whole distribution sample and narrow size distribution samples in the same way. The test results indicate that the average grade was elevated from 1.04% to 6.50% with the recovery of 15.07% and 619% enrichment ratio for narrow sizes, whereas grade was elevated to 4.36% with 13.24% recovery and 415.94% enrichment ratio for the whole distribution. As a result, the recovery and grade values of concentrates are not good enough for gravity concentration process for both samples. However, this process was applied to the double recycled material and the lower recovery, grade values can be tolerated because of concentrate is nugget copper metal. The concentrate can also be washed in cleaning table for increasing the grade value for adding to initial feed of plant. This process can, therefore, supply important earnings not only economically but also environmentally.
Due to the difficulty of detecting traces of organic acid mixture in an aqueous sample and the complexity of resolving UV-Vis spectra effectively, a combinatory method based on a self-made radical electric focusing solid phase extraction (REFSPE) device, UV-Vis detection and partial least squares (PLS) calculation is proposed here. In this study, REFSPE was used to enhance the extraction process of analytes between the aqueous phase and the membrane phase to enrich the trace of mixed organic acid efficiently. Then, the analytes, which were eluted from the adsorption film by ethanol with the assistance of an ultrasonic cleaning machine, were detected with UV-Vis spectrophotometry. After that, the PLS method was introduced to solve the problem of overlapping peaks in UV-Vis spectra of mixed substances and to quantify each compound. The linearly dependent coefficients between the predicted value of the model and the actual concentration of the sample were all higher than 0.99. The limit values of detection for benzoic acid, phthalic acid and p-toluene sulfonic acid were found at 9.9 #22;g/L, 12.2 #22;g/L and 13.8 #22;g/L with the relative recovery values between 84.8% and 117.9%. The RSD (n = 20) values of each component are 1.17%, 1.11% and 0.86%, respectively. Therefore, the proposed combined method can determine traces of complex materials in an aqueous sample efficiently and has wonderful potential applications.
In the following paper, geovisualisation will be applied to one spatial phenomenon and understood as a process of creating complementary visualisations: static two-dimensional, surface three-dimensional, and interactive. The central challenge that the researchers faced was to find a method of presenting the phenomenon in a multi- faceted way. The main objective of the four-stage study was to show the capacity of the contemporary software for presenting geographical space from various perspectives while maintaining the standards of cartographic presentation and making sure that the form remains attractive for the user. The correctness, effectiveness, and usefulness of the proposed approach was analysed on the basis of a geovisualisation of natural aggregate extraction in the Gniezno district in the years 2005–2015. For each of the three visualisations, the researchers planned a different range of information, different forms of graphic and cartographic presentation, different use and function, but as far as possible the same accessible databases and the same free technologies. On the basis of the final publication, the researchers pointed out the advantages of the proposed work flow and the correctness of the detailed flowchart.
The report presents the results of selected heavy metals (Zn, Cu, Cd, Ni, Pb) removal from industrial wastewater sludge collected from metallurgy industry. As washing solutions two chelating agents were used: EDTA and citric acid. The study was focused on 0.000 (deionized water), 0.010, 0.050, 0.075, 0.100 M and 0.000, 0.050, 0.100, 0.500, 1.000 M, EDTA and citric acid solutions, respectively. Efficiency of EDTA and citric acid solutions for metal removal was studied by extraction of sludge samples with chelators. Chemical extraction of selected metals was effective for both types of solution. Optimal concentration of EDTA was 0.100M for Zn, Ni and Cd, 0.075 M for Cu and Pb. Optimal concentration of citric acid was 0.500 M for all analyzed metals
An array consisting of four commercial gas sensors with target specifications for hydrocarbons, ammonia, alcohol, explosive gases has been constructed and tested. The sensors in the array operate in the dynamic mode upon the temperature modulation from 350°C to 500°C. Changes in the sensor operating temperature lead to distinct resistance responses affected by the gas type, its concentration and the humidity level. The measurements are performed upon various hydrogen (17-3000 ppm), methane (167-3000 ppm) and propane (167-3000 ppm) concentrations at relative humidity levels of 0-75%RH. The measured dynamic response signals are further processed with the Discrete Fourier Transform. Absolute values of the dc component and the first five harmonics of each sensor are analysed by a feed-forward back-propagation neural network. The ultimate aim of this research is to achieve a reliable hydrogen detection despite an interference of the humidity and residual gases.
In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's “intentions” by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.
Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855~2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.
Liquid-liquid extraction provides an environmentally friendly process as an alternative to azeotropic distillation, pervaporation and reverse osmosis because these techniques require the use of large amounts of energy, may involve volatile organic compounds, and operation at high pressure. Ionic liquids (ILs) continue to gain wide recognition as potential environmentally friendly solvents due to their unique properties. However due to their current high cost, their use in industry is seriously limited without an efficient methodology for recovery and recycle. In this paper we describe an innovative methodology for a liquid-liquid extraction process based on an electrically induced emulsion of an ionic liquid as the extracting solvent dispersed in an organic mixture. This offers a most efficient exploitation of the solvent. On the other hand we present our own design of a pilot (semi-industrial) scale extractor based on this methodology and which demonstrates effective recovery of the ionic liquid. In order to achieve this goal we used a numerical modelling tool implemented using our own simulation software based on the finite element method. We also used our original previous experience with generating and investigating liquid-liquid electrosprays using phase Doppler anemometry. Finally we present recommendations for contactor geometry and for the preferred operating conditions for the extractor.
Retinitis pigmentosa is a genetic disorder that results in nyctalopia and its progression leads to complete loss of vision. The analysis and the study of retinal images are necessary, so as to help ophthalmologist in early detection of the retinitis pigmentosa. In this paper fundus images and Optical Coherence Tomography images are comprehensively analyzed, so as to obtain the various morphological features that characterize the retinitis pigmentosa. Pigment deposits, important trait of RP is investigated. Degree of darkness and entropy are the features used for analysis of PD. The darkness and entropy of the PD is compared with the different regions of the fundus image which is used to detect the pigments in the retinal image. Also the performance of the proposed algorithm is evaluated by using various performance metrics. The performance metrics are calculated for all 120 images of RIPS dataset. The performance metrics such as sensitivity, sensibility, specificity, accuracy, F-score, equal error rate, conformity coefficient, Jaccard’s coefficient, dice coefficient, universal quality index were calculated as 0.72, 0.96, 0.97, 0.62, 0.12, 0.09, 0.59, 0.45 and 0.62, respectively.
The paper presents the results of investigations on a cyclone with additional gas extraction. The experiments were performed in the cyclone with a diameter of 0.2 m equipped with a truncated counter-cone situated in the dust bin inlet. The gas stream flowing through the countercone was 10 and 20% of the gas supplied to the cyclone. The separation efficiencies and pressure loss were measured. The experiment showed that the extraction of gas by the counter-cone deteriorated the cyclone efficiency and forcing the outflow of gas through the counter-cone requires the use of an additional outlet fan.
This paper presents the classification of musical instruments using Mel Frequency Cepstral Coefficients (MFCC) and Higher Order Spectral features. MFCC, cepstral, temporal, spectral, and timbral features have been widely used in the task of musical instrument classification. As music sound signal is generated using non-linear dynamics, non-linearity and non-Gaussianity of the musical instruments are important features which have not been considered in the past. In this paper, hybridisation of MFCC and Higher Order Spectral (HOS) based features have been used in the task of musical instrument classification. HOS-based features have been used to provide instrument specific information such as non-Gaussianity and non-linearity of the musical instruments. The extracted features have been presented to Counter Propagation Neural Network (CPNN) to identify the instruments and their family. For experimentation, isolated sounds of 19 musical instruments have been used from McGill University Master Sample (MUMS) sound database. The proposed features show the significant improvement in the classification accuracy of the system.