The Influence of Surfactant on the Surface Characterization of Celestite in Spherical Oil Agglomeration

This study investigates the relationship between the critical surface tension of wetting of celestite and agglomeration recovery as well as the zeta potential in dependence of pH and amount of collector (Na‐Oleate). For this purpose, effects of pH and collector amount on the agglomeration recovery are investigated and zeta potential measurements and Fourier Transform Infrared Spectrophotometer (FTIR) analyses are carried out to determine the adsorption type of Na‐Oleate on celestite surface. The sessile drop technique is used for the measurement of contact angle and determination of the critical surface tension of wetting (Y_C) of celestite. The maximum agglomeration recovery is obtained at pH 7. The critical surface tension of wetting of celestite is very close to the surface tension of kerosene, which is 25.95 mN/m. Furthermore, at the optimum Na‐Oleate amount of 10 kg/ton, the critical surface tension of wetting of celestite is approximately equal to the surface tension of kerosene.     

A combo technology of autotrophic and heterotrophic denitrification processes for groundwater treatment

In this study,a sequential process (heterotrophic up-flow column and completely mixed membrane bioreactors) was proposed combining advantages of the both processes.The system was operated for 249 days with simulated and real groundwater for nitrate removal at concentrations varying from 25 to 145 mg·L-1 NO3-N.The contribution of heterotrophic process to total nitrate removal in the system was controlled by dozing the ethanol considering the nitrate concentration.By this way,sulfur based autotrophic denitrification rate was decreased and the effluent sulfate concentrations were controlled.The alkalinity requirement in the autotrophic process was produced in the heterotrophic reactor,and the system was operated without alkalinity supplementation.Throughout the study,the chemical oxygen demand in the heterotrophic reactor effluent was (23.7 ± 22) mg·L-1 and it was further decreased to(7.5 ± 7.2) mg·L-1 in the system effluent,corresponding to a 70％ reduction.In the last period of the study,the real groundwater containing 145 mg·L-1 NO3-N was completely removed.Membrane was operated without chemical washing in the first 114 days.Between days 115-249 weekly chemical washing was required.     

Investigation of kinetics of agglomerate growth in oil agglomeration process

In this study, the kinetics of agglomerate growth in a batch oil agglomeration process has been studied using bituminous coal. The effect of operating variables such as kerosene concentration, pulp density and speed of agitation on the agglomeration process was investigated. It has been found that the second-order kinetic equation describes the growth of agglomerates adequately. The growth of the agglomerates in the oil agglomeration process shows a self-preserving growth. Using this, a characteristic curve has been developed. For the prediction of the size distribution of the agglomerates, the d₅₀ values of the agglomerates must be known. Therefore, a model has been developed by using the kinetic equation for estimation of d₅₀ values of agglomerates for this coal. It was shown that the size distribution of the agglomerates for any levels of the process variables studied can be predicted using the equation of characteristic curve and d₅₀ values. Knowledge obtained from this study will be helpful for technological advancement of this kind of study.     

Application of the Box-Wilson experimental design method for the spherical oil agglomeration of coal

In this study, the Box-Wilson statistical experimental design method was employed to evaluate the effects of important variables such as bridging liquid (oil) concentration, salt (CaCl₂·2H₂O) concentration and stirring speed on the agglomeration of bituminous coal. Response function coefficients were determined by the regression analysis of experimental data and the predictions were found to be in good agreement with the experimental results. The optimum kerosene concentration, CaCl₂·2H₂O concentration and stirring speed were determined as 30 wt%, 1 M and 1683 rpm, respectively, when considering combustible recovery and ash content.In addition, contact angle and solution surface tension measurements were carried out to evaluate of agglomeration success with the contact angle values and surface tension values. The surface tension of CaCl₂ 2H₂O solutions and the average contact angle increased with increasing CaCl₂·2H₂O concentration.     

Removal of Cd(II), Hg(II), and Pb(II) ions from aqueous solution using p(HEMA/chitosan) membranes

An interpenetration network (IPN) was synthesized from 2‐hydroxyethyl methacrylate (HEMA) and chitosan, p(HEMA/chitosan) via UV‐initiated photo‐polymerization. The selectivity to different heavy metal ions viz Cd(II), Pb(II), and Hg(II) to the IPN membrane has been investigated from aqueous solution using bare pHEMA membrane as a control system. Removal efficiency of metal ions from aqueous solution using the IPN membranes increased with increasing chitosan content and initial metal ions concentrations, and the equilibrium time was reached within 60 min. Adsorption of all the tested heavy metal ions on the IPN membranes was found to be pH dependent and maximum adsorption was obtained at pH 5.0. The maximum adsorption capacities of the IPN membrane for Cd(II), Pb(II), and Hg(II) were 0.063, 0.179, and 0.197 mmol/g membrane, respectively. The adsorption of the Cd(II), Hg(II), and Pb(II) metal ions on the bare pHEMA membrane was not significant. When the heavy metal ions were in competition, the amounts of adsorbed metal ions were found to be 0.035 mmol/g for Cd(II), 0.074 mmol/g for Hg(II), and 0.153 mmol/g for Pb(II), the IPN membrane is significantly selective for Pb(II) ions. The stability constants of IPN membrane–metal ions complexes were calculated by the method of Ruzic. The results obtained from the kinetics and isotherm studies showed that the experimental data for the removal of heavy metal ions were well described with the second‐order kinetic equations and the Langmuir isotherm model. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Deep learning frameworks to learn prediction and simulation focused control system models

Deep learning (DL) methods have brought world-shattering breakthroughs, especially in computer vision and classification problems. Yet, the design and deployment of DL methods in time series prediction and nonlinear system identification applications still need more progress. In this paper, we present DL frameworks that are developed to provide novel approaches as solutions to the aforementioned engineering problems. The proposed DL frameworks leverage the advantages of autoencoders and long-short term memory network, which are known being data compression and recurrent structures, respectively, to design Deep Neural Networks (DNN) for modeling time series and nonlinear systems with high performance. We provide recommendations on how deep AEs and LSTMs should be utilized to end up with efficient Prediction-focused (Pf) and Simulation-focused (Sf) DNNs for time series and system identification problems. We present systematic learning methods for the DL frameworks that allow straightforward learning of Pf-DNN and Sf-DNN models in detail. To demonstrate the efficiency of the developed DNNs, we present various comparative results conducted on the benchmark and real-world datasets in comparison with their conventional, shallow, and deep neural network counterparts. The results clearly show that the deployment of the proposed DL frameworks results with DNNs that have high accuracy, even with a low dimensional feature vector.

     

Improved gait recognition by multiple-projections normalization

Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition. The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

Online tax payment systems as an emergent aspect of governmental transformation

The achievement of governmental transformation through the use of electronically delivered services is a worthy goal that requires significant planning and research to achieve. In order to reach transformational paradigm shifts in governmental operation, it will first be necessary to understand and optimize present governmental e-Service provisions. Of these, the revenue function of taxation is paramount. This paper describes factors related to the use and acceptance by accounting professionals of information technology intended to facilitate electronic tax filing systems. Though tested in the context of governmental tax management systems in Turkey, our findings on the use and acceptance of e-Tax systems are relevant and applicable to a great number of nations and contexts as the ongoing electronic transformation of the governmental revenue system contributes to efforts to transform governments through alternative services delivery venues and channels. We discover that intention to use automated systems as part of the governmental treasury function transformation is hindered by factors that mediate actual plans to do so, mostly in terms of normative pressures and perceptions of behavioral control, which training and education may well ameliorate. Hence, transformation of the treasury function in Turkey is only partially complete and will require additional support, direction and training on the part of the government in its interactions with the tax professionals who interact with the emergent automated system.     

Feature tracking and matching in video using programmable graphics hardware

This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.