Textile Dye Removal Using Photocatalytic Cascade Disk Reactor Coated by ZnO Nanoparticles

Dye effluents with low BOD/COD ratio and varied chemical structures are usually very recalcitrant to microbial degradation. Therefore, different process was used for the treatment of dye effluent. Heterogeneous photocatalyst process has been widely used as leading green technology for dye removal. The process utilizes a semiconductor photocatalyst (such as TiO2 or ZnO) and UV light to oxidize the recalcitrant organic compounds to inorganic ions, carbon dioxide and water. Photocatalytic process needs to photorectors and hydraulic parameters play an important role in mass transfer phenomenon in photocatalytic reactors. These fundamental parameters are flow rate, relative roughness and Reynolds number. This research experimentally evaluates flow rate and artificial relative roughness in order to determine the factors influencing the removal efficiency and reaction rate. For this purpose, a cascade photocatalytic reactor is constructed which consists of similar Plexiglas plates coated by various roughness. Numerical simulation usually overcomes complex reactor models which takes reasonable cost respect to experimental study. Here, OpenFOAM software is also utilized to perform a numerical study. Regime and velocity of sewage are simulated in photocatalytic flow with/without considering relative roughness.

Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

Handwriting recognition is a challenge that interests many researchers around the world.As an exception,handwritten Arabic script has many objectives that remain to be overcome,given its complex form,their number of forms which exceeds 100 and its cursive nature.Over the past few years,good results have been obtained,but with a high cost of memory and execution time.In this paper we propose to improve the capacity of bidirectional gated recurrent unit(BGRU)to recognize Arabic text.The advantages of using BGRUs is the execution time compared to other methods that can have a high success rate but expensive in terms of time andmemory.To test the recognition capacity of BGRU,the proposed architecture is composed by 6 convolutional neural network(CNN)blocks for feature extraction and 1 BGRU+2 dense layers for learning and test.The experiment is carried out on the entire database of institut für nachrichtentechnik/ecole nationale d’ingénieurs de Tunis(IFN/ENIT)without any preprocessing or data selection.The obtained results show the ability of BGRUs to recognize handwritten Arabic script.

About Multichannel Speech Signal Extraction and Separation Techniques

The extraction of a desired speech signal from a noisy environment has become a challenging issue. In the recent years, the scientific community has particularly focused on multichannel techniques which are dealt with in this review. In fact, this study tries to classify these multichannel techniques into three main ones: Beamforming, Independent Component Analysis (ICA) and Time Frequency (T-F) masking. This paper also highlights their advantages and drawbacks. However these previously mentioned techniques could not afford satisfactory results. This fact leads to the idea that a combination of those techniques, which is depicted along this study, may probably provide more efficient results. Indeed, giving the fact that those approaches are still be considered as being not totally efficient, has led us to review these mentioned above in the hope that further researches will provide this domain with suitable innovations.

Image Classification using Statistical Learning Methods

In general, digital images can be classified into photographs, textual and mixed documents. This taxonomy is very useful in many applications, such as archiving task. However, there are no effective methods to perform this classification automatically. In this paper, we present a method for classifying and archiving document into the following semantic classes: photographs, textual and mixed documents. Our method is based on combining low-level image features, such as mean, Standard deviation, Skewness. Both the Decision Tree and Neuronal Network Classifiers are used for classification task.