Tolerable Risk for Hydrogen Sulfide in Sewage Treatment Plant- STP

Hydrogen sulphide is a toxic gas, it can cause a range of physiological responses from simple annoyance to permanent injury and death. There are a number of approaches to deal with the impacts of toxic gases. This study focused on minimizing the hazard exposure for hydrogen sulfide in the different operational zones for activated sludge process in sewage waterplant. Research tools/ approaches conducted were interviews, toxic gas testers, analysis report interpretation & quantitative risk assessment method. The study was conducted on Arabian Peninsula during the period (September 2019- September 2021). The (13) operational locations tested for toxic gas concentrations were inlet chamber, outlet channel, coarse /fine screens, primary sedimentation tank, activated sludge tanks, secondary sedimentation tanks, gas desulfurization unit, disc filters, chlorine dosing unit, sludge dewatering, sludge silos and digester tanks. The study found that the highest concentration for H₂S in the inlet chamber/ outlet channel. The severity hazards in the sewage treatment plant using activated sludge process are the asphyxiation by H₂S was extremely high can cause harm to public health, followed by the radiation hazard followed by electrical hazard, then (working at height, mechanical, traffic, health, chemical, physical, ergonomic, environmental, microbial and natural). The frequency of hazards occurrence is asphyxiation by H₂S was extremely high followed by the radiation hazard and health hazard including the infection with Covid 19 virus followed by mechanical hazard then (electrical, traffic, ergonomic, natural, chemical, physical and natural). Control measures were recommended to minimize the risk of asphyxiation by H₂S in the working environment at the STP.

Experimentation of the Incineration of Paper Waste at the University Press of Ouagadougou (PUO)

Waste management and recycling is major problem in our developing countries for several reasons, including population growth. In Burkina Faso, various techniques for treating this garbage exist and among them, we can cite incineration. Incineration is a heat treatment of garbage that reduces the volume of the latter. This work relates to the experimental study of the incineration of paper waste from the incinerator of the University Press of Ouagadougou. The results of this study show that this device is very useful in that it allows the incineration of paper garbage produced by the printing press. It, therefore, has an environmental advantage because its use promotes better management of paper waste.

Optimization of the Refractive Index of Antireflection Coatings on Monocrystalline Silicon Solar Cells for Photovoltaic Application

In this work, the following materials have been chosen as anti-reflection layer, namely hafnium (HfO2), magnesium fluoride (MgF2), silicon oxynitrides (SiOxNy), silicon oxides (SiOx), silicon nitride (Si3N4) and hydrogenated silicon nitride (SiNx:H). The calculations were made on the basis of values of layer thicknesses and refractive indices that allow the phase and amplitude conditions to be respected and amplitude conditions. Numerical simulations have shown that low reflectivities at the surface of the surface of the plane cell coated with a simple layer, can be obtained. For example, for simple coatings materials based on Si3N4 and HfO2, we obtain a value of reflectivity around 3 and 2 % respectively. The structures with multilayer coatings such as MgF2/SiNx:H/Si, give a reflectivity of around 1 %. Thus, the refraction index of the coating is an important parameter that plays a major parameter that plays a major role in the optical properties of materials. The closer the refractive index is close to the index of the substrate or the layer above the substrate, the higher the reflectivity.

Method for Determining the Optimum Insulation Thickness of the Plaster Tow Material: Influence of the Heat Exchange Coefficient in Transient Regime

In this paper, we have applied a numerical method to determine the optimum insulation thickness of the tow plaster plane material. The influence of the exchange coefficients at the level of the two faces of the material has been highlighted. The optimum insulation thickness of the material is at the area where the thermal resistance value of the material is the maximum. We added the relative thermal resistance to show how the optimum insulation thickness changes when the exchange coefficients change values.

Developing Double Walls System to Improve Building Energy Performances in Dry Tropical Climates

This paper deals with building envelope thermal performance through a comparative study of the use of two types of construction materials, such as CEB and cement blocks, in order to introduce the use of double walls in sustainable buildings' construction. The building envelope participates in providing thermal comfort to users and in the optimal management of building energy consumption. This study begins with a survey of public preferences for building materials used in Burkina Faso. The results indicate that 76% of the people surveyed opt for cement blocks over local materials. Concerning the thermal and specific energy performance, three variants of building envelope were studied: CEB walls, cement blocks and the double-wall (CEB + Cement blocks). It appears that the CEB walls are more efficient than the cement block walls. The introduction of double envelopes leads to the thermal resistance of 357.37m².K/W and reduces the heat flow from 85.32% to 90.24% compared to the wall made with CEB and cement blocks. This approach, which consists in mixing construction materials for good thermal insulation, allows improving the envelope thermal performance and the overall building energy performance.

Simulation of the Injection of 25 MW Photovoltaic Energy Production: Analysis of the Impacts on the Grid of the Société Béninoise d'Energie Electrique (SBEE)

In order to make up its energy deficit and reduce its energy imports from neighbouring countries, Benin is opting for the construction of photovoltaic solar micro-power plants in the sunniest regions and to consider injecting it into the existing electricity grid if this locally produced energy is not entirely consumed. With this in mind, a decentralised electricity production project has been initiated. In particular, the project, which is the subject of this presentation, aims to simulate and analyse the impacts of injecting 25 MW of photovoltaic energy production into the existing national electricity grid of the Société Béninoise d'Energie Electrique (SBEE). For this purpose, the dimensioning of the 25MW power plant has been carried out and injected at a specific point of the 20kVA line of the existing electricity network in the NEPLAN software environment, while respecting the requirements for injecting photovoltaic energy into an existing electricity network. Only extreme operating configurations have been studied: the synchronous hollow and synchronous point configuration. Simulation results showed overloads on certain transformer stations in the network, which indicates that adjustments must be made before the actual injection of the electricity produced. Besides, the power grid did not experience any disturbance in the voltage plan and power flows. Finally, the simulations carried out led to the conclusion that the integration of solar PV plants will make it possible to limit the import of energy from Ghana and Nigeria.

Automatic MRI Brain Tumor Segmentation Techniques: A Survey

Image segmentation plays a crucial role in recognizing image signification for checking and mining medical image records. Brain tumor segmentation is a complicated assignment in medical image analysis. It is challenging to identify precisely and extract that a portion of the image has abnormal tissues for further diagnosis and analysis. The method of segmenting a tumor from a brain MRI image is a highly concentrated medical science community field, as MRI is non-invasive. In this survey, brain MRI images' latest brain tumor segmentation techniques are addressed a thoroughgoing literature review. Besides, surveys the several approved techniques regularly applied for brain tumor MRI segmentation. Also, highlighting variances among them and reviews their abilities, pros, and weaknesses. Various approaches to image segmentation are described and explicated with the modern participation of several investigators.

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines. Today we are witnessing the tangible successes of Deep Learning in our daily lives in various applications, including education, manufacturing, transportation, healthcare, military, and automotive, etc.<strong> </strong>Deep Learning is a subfield of Machine Learning that stems from Artificial Neural Networks, where a cascade of layers is employed to progressively extract higher-level features from the raw input and make predictive guesses about new data. This paper will discuss the effect of attribute extraction profoundly inherent in training<strong> </strong>approaches such as Convolutional Neural Networks (CNN). Furthermore, the paper aims to offer a study on Deep Learning techniques and attribute extraction methods that have appeared in the last few years. As the demand increases, considerable research in the attribute extraction assignment has become even more instrumental. Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.

Optimal Algorithm Selection in Multimodal Medical Image Registration

Many medical applications benefit from the diversity inherent in imaging technologies to obtain more reliable diagnoses and assessments. Typically, the images obtained from multiple sources are acquired at distinct times and from different viewpoints, rendering a multitude of challenges for the registration process. Furthermore, different areas of the human body require disparate registration functional capabilities and degrees of accuracy. Thus, the benefit attained from the image multiplicity hinges heavily on the imaging modalities employed as well as the accuracy of the alignment process. It is no surprise then that a wide range of registration techniques has emerged in the last two decades. Nevertheless, it is widely acknowledged that despite the many attempts, no registration technique has been able to deliver the required accuracy consistently under diverse operating conditions. This paper introduces a novel method for achieving multimodal medical image registration based on exploiting the complementary and competitive nature of the algorithmic approaches behind a wide range of registration techniques. First, a thorough investigation of a wide range of registration algorithms is conducted for the purpose of understanding and quantifying their registration capabilities as well as the influence of their control parameters. Subsequently, a supervised randomized machine learning strategy is proposed for selecting the best registration algorithm for a given registration instance, and for determining the optimal control parameters for such algorithm. Several experiments have been conducted to verify the capabilities of the proposed selection strategy with respect to registration reliability, accuracy, and robustness.

Brain Tumor Diagnosis Support System: A Decision Fusion Framework

The early and accurate detection of brain tumors is important in providing effective and efficient therapy and thus can result in increased survival rates. Current image-based tumor detection and diagnosis methods depend heavily on the interpretation of the neuro specialists and/or radiologists. Therefore, it is quite possible for the interpretation process to be time-consuming, and prone to human error and subjectivity. Automatic detection and classification of brain tumors have the potential to achieve efficiency and higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. Thus, it is prudent to explore the variability in the performance of these techniques as a means to achieve consistent high accuracy performance. This paper presents a framework for fusing multiple tumor classifiers. The fusion process is based on the Dempster Shafer evidence fusion theory. Several tumor classifiers are employed. Experimental results will be presented to validate the efficiency of the proposed framework. It is concluded that fusing the classification decisions made by the various classifiers it is conceivable that efficient and consistent high accuracy classification performance can be attained.