Periodica Polytechnica (Budapest University of Technology and Economics)
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Mathematical-model Analysis of the Potential Exposure to Lead, Zinc and Iron Emissions from Consumption of Premium Motor Spirit in Nigeria
Get PDFEnvironmental pollution has been on the increase due to emission from vehicles using fossil fuels. This research investigated the exposure of air, soil and water bodies to trace metal emissions: Pb, Zn and Fe, as a result of the consumption of premium motor spirit (PMS) in Nigeria. The exposure of air, soil, and water bodies to these emissions also lead to exposure of humans, food and animals to the emissions. This was done to estimate the emission rates, emission rate per capita, and emission rates per land areas (or land distribution). The results showed that: the annual emission rates ranged between 4.66 kg/y for Pb in 2012 in Jigawa State and 5.050∙103 kg/y for Fe in 2015 in Lagos State; the emission rates per capita ranged between 0.52∙10−6 kg/(y∙person) for Pb in 2012 in Kwara State and 2.33∙10−3 kg/(y∙person) and this was recorded in Lagos State in the year 2015; while the rate per land area ranged between 0.093∙10−3 kg/(y∙km2) for Pb in 2012 in Taraba State and 1.38 kg/(y∙km2) for Fe in 2015 in Lagos State. Results showed that residents of Lagos are at the highest risk of trace metal poisoning because they had the highest emission rates per capita, followed by Abuja, Osun, and Ogun. The states at the lowest risk are Yobe and Taraba, with Yobe as the lowest. It is recommended that regulations concerning the trace metal contents of fuels imported and distributed in Nigeria should be created and implemented to curb these risks
Lead Oxide Modified Graphite Electrodes for Electrochemical Degradation of Congo Red Dye in Aqueous Solution
Get PDFCongo red (CR) dye in aqueous solution was decolorized by an electrolysis process using graphite (G) and lead dioxide modified graphite (G/PbO2) as anode materials in a two-electrode batch reactor. The electrodeposited lead dioxide film was characterized by scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS) and atomic force microscopy (AFM). Comparative performance assessment of the anode materials under different process parameters reveals that the lead dioxide film improved the electrocatalytic effect of the modified electrode. The adjustment of the deposition bath pH from 1.5 to 3 resulted in the formation of uniform agglomeration and disappearance of particulates, while addition of sodium dodecyl sulphate (SDS) gave better adhesion of film to substrate. The degradation rate (DR) observed for the G/PbO2 (1.0 × 10−2 cm2) was higher than that of the unmodified electrode (0.87 × 10−2 cm2). Increase in applied voltage from 25 to 30 V at 23 mA/mm2 improved the degradation efficiency (DE) from 84.7% to 91.32% for graphite and from 96.09% to 99.98% for G/PbO2, with 0.5 M KCl. The prime degradation time of 45 min was recorded for graphite anode which reduced to 30 min for G/PbO2 anode. CR degraded to compounds with smaller molecular weight and better stability as observed from GC-MS analysis and computational total energy study, respectively. The modification of the graphite electrode surface by electrodepositing PbO2 film improved the DE and the prime reaction time. These findings present significant suggestions for the design of advanced electrodeposition and electrocatalytic systems for wastewater treatment applications
Performance of Acetone Extract of Anthocleista grandiflora as a Potential Bioinhibitor on Corrosion Behavior of Carbon Steel in Seawater Environment
Get PDFIn this study, we evaluated the potential of Anthocleista grandiflora leaf (AGL) plant extract as an environmentally friendly and cost-effective corrosion inhibitor for carbon steel in seawater. We employed various experimental methods, including gravimetric analysis, potentiodynamic polarization, electrochemical impedance spectroscopy, scanning electron microscopy (SEM) and Fourier transform infrared spectrophotometry (FTIR). Our findings indicate that increasing the concentrations of the AGL extract results in higher charge transfer resistance (Rct ) and reduced double-layer capacitance (Cdl ), suggesting the effective adsorption of AGL extract on the surface of carbon steel. The inhibition efficiencies were notably high, 98.7%, 92.40%, and 90.7% determined with gravimetric analysis, potentiodynamic polarization, and electrochemical impedance spectroscopy, respectively. Polarization analysis revealed that the AGL extract acted as a mixed-type inhibitor. Moreover, the results obtained from different techniques exhibited a consistent agreement. The SEM images revealed that the surface layer formed by the AGL extract on the mild steel surface further devoids the surface from pitting as the extract concentration increases. Comparative analysis with similar bio-based inhibitors suggested that the tested AGL extract holds a significant promise as a corrosion inhibitor for carbon steel in seawater. Therefore, our findings support the recommendation of utilizing this AGL extract as an effective anti-corrosion agent in marine industries, owing to its green, low-cost, and efficient characteristics
Mathematical Modeling of a Corrugated Geogrid and Geocell Reinforced Flexible Pavement Base with Interlayer Shear Performance Analysis
Get PDFTo combat permanent deformation in flexible road pavements and extend lifespan while reducing maintenance costs, an investigated solution "Corrugated Geogrid and Geocell Reinforced Flexible Pavement Base Design" is proposed. It enhances durability by improving inter-layer bonding and increasing the friction coefficient with a Corrugated Imminent Hexagonal Composite Geogrid, boosting pavement bearing capacity. The necessity of this research is the integration of corrugated geogrids and geocells into the design of flexible pavement, in conjunction with a comprehensive evaluation of interlayer shear performance, effectively tackles a range of engineering obstacles and enhances the pavement system's durability, stability, and affordability. Geogrid's corrugated structure resists shoving, offers stabilization, and, combined with Fusion Geoblanket and Perforated Geocell, prevents potholes. Typically, geocellular brace parts are utilized in civil engineering and technical applications, and they are frequently connected to geocellular systems or geocells. These systems are frequently employed in the building of retaining walls, slope protection, erosion control, and soil stabilization. The intended use and product design influence the installation procedures and specific functioning approach. The proposed model prevents permanent pavement failures, ensuring durability, low maintenance, and improved lifespan, assessed through Finite Element Analysis. Thus, the proposed method develops a layout for flexible pavement that's going to prolong its life expectancy and prevent long-term deformation failure
Predicting Autogenous Shrinkage of Concrete Including Superabsorbent Polymers and Other Cementitious Ingredients Using Convolution-based Algorithms
Get PDFIn this paper, the effectiveness of ensemble convolution-based deep learning models is evaluated for predicting autogenous shrinkage/swelling of cementitious materials. Various ensemble learning techniques are employed, including Simple Average Ensemble, Snapshot Ensemble, and Stacked Generalization, to develop predictive models. The models are trained and evaluated using performance metrics such as Root Mean Squared Error, Coefficient of Determination, Overall Index of model performance, Mean Absolute Error, and 95% Uncertainty. The results show that the integrated stacking model (ISM) outperforms other models in terms of predictive accuracy. Furthermore, the SHapley Additive exPlanation (SHAP) technique was used to interpret the ISM model. The analysis reveals that the most influential factors affecting shrinkage predictions include time, aggregate to cement ratio (A/C), superabsorbent polymer (SAP) content, water to binder ratio, cement content, water to cement ratio, and silica fume content. Also, the ISM model was compared with models developed previously by other researchers, namely, K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB). With the lowest RMSE and MAE values, the ISM model has exceptional accuracy, demonstrating its capacity to create predictions that closely resemble observed values. Additionally, it has the highest coefficient of determination value, demonstrating its effectiveness in explaining a sizable percentage of the data variance. The Overall Index (OI) statistic shows that the ISM model performs exceptionally well, indicating that it captures more of the underlying information in the data. Additionally, it displays lower 95% confidence intervals, demonstrating greater assurance in its forecasts
Experimental Investigation of High-performance Fiber-reinforced Cementitious Composite and its Effect on RC Beams by Numerical Method
Get PDFIn the present study, experimental investigations were initially conducted on high-performance fiber-reinforced cementitious composite (HPFRCC). A total of 9 samples were examined and subjected to a 4-point bending test. The sample lengths were standardized at 500 and 1700 mm. The variables under scrutiny included the impact of Micro Steel, Macro Steel, and polyvinyl alcohol fibers. Furthermore, the models were scrutinized under two conditions: with and without glass fiber reinforced polymer (GFRP) bars. The number of samples was 9. In the second part of the article, subsequent to verifying the experimental samples from the current study alongside a concrete beam, numerical analyses were carried out to assess the influence of HPFRCC on the behavior of RC beams. Similarly, the impact of GFRP diameter, as well as the height of HPFRCCs, on the seismic performance of RC beams, was investigated by conducting 36 numerical analyses. The analyses were carried out using nonlinear static methods, with monotonic loading. The model outputs encompass elastic stiffness, ultimate strength, relative stiffness, and energy dissipation. The experimental results showed that the use of macro steel fibers in models without GFRP rebars has better results on the flexural behavior of HPFRCC. Moreover, by reinforcing RC beams with HPFRCC, a 70% increase in energy dissipation was observed. The elastic stiffness and ultimate strength of the strengthened beam are directly proportional to the ratio of the HPFRCC's elastic flexural stiffness to that of the original beam. These results increase proportionally as this ratio rises
Palmprint Identification Using Dolphin Optimization
Get PDFPalm print recognition is a rapidly evolving area in the field of biometrics, providing a high level of security for various applications Advances in scanning technology and software have led to faster and more accurate palm print analysis, we have proposed in this paper a system for palm recognition using the Dolphin Optimization Algorithm (DOA) as a computational technique inspired by nature aimed at solving complex improvement problems. We reduced the number of image features using the Histograms of oriented gradients (HOG) algorithm, we named this method as (DOA) and we also proposed a hybrid method by integrating the DOA algorithm with the Support Vector Machine (SVM) model to improve prediction accuracy by combining DSA's ability to search for global optimal solutions with the effective classification capabilities of SVM, this allows The hybrid approach creates a robust and the proposed hybrid method was named (SVM-DOA), and we also proposed a hybrid method by integrating the DOA algorithm with fuzzy c – mean (FCM) and we named the proposed hybrid method (DOA-fuzzy membership), we verified the validity of the proposed method on public database images of palm print. experiment show that the average accuracy rate of the dolphin swarm algorithm (DSO) is (96.8%), while the average accuracy rate of the proposed hybrid algorithm (DSO-SVM) is (97.8%), and the average accuracy of the proposed hybrid algorithm (DSO-fuzzy membership) is (98.1%)
Algorithmic Decomposition of Railway Objects for Distributed Interlocking System
Get PDFRailway interlocking systems can be implemented as distributed systems, where each part of a station is handled by a separate logical unit. The logical units of such systems form a network and communicate by interchanging messages. Such distributed architectures are well known in large industrial control systems. There are several design practices and also algorithmic task partitioning methods that are applicable in distributed control systems. Some of such methods can also be adapted in designing of railway interlocking systems as well. In the case of such systems, the communication time between components must be kept low. Namely each separate controller in a given route must be able to exchange their internal state within a limited time in order to permit the train movement authorization. This limitation could cause high traffic load, if every logical unit would be interconnected with each other. Therefore, the main goal of the minimization is to reduce the number of connections between logical units. This can achieved by distributing and assigning the topological railway objects to certain logical units
Investigation of Proton Radiation Effect on Indium Gallium Nitride Light Emitting Diodes
Get PDFThis paper investigates the effects of proton radiation on the electrical and optical properties of InGaN light-emitting diodes (LEDs). InGaN LEDs are known for their high brightness and efficiency, making them useful in various applications. However, they are vulnerable to radiation damage, which can degrade their performance over time. In this study, InGaN LEDs were exposed to proton radiation with the fluences of 1 × 1013 cm-2, 3 × 1013 cm-2 and 3 × 1014 cm-2 and their electrical and optical properties were measured before and after irradiation. Results show that proton radiation causes asignificant increase in the reverse leakage current. The light intensity also increases due to radiation. These changes are attributed to radiation-induced defects created in the LED material. The findings of this study provide important insights into the reliability and durability of InGaN LEDs in space and other radiation environments
Robust Intelligent Nonlinear Predictive Control Based on Artificial Neural Network for Optimizing PMSM Drive Performance
Get PDFIn the realm of high-performance motor drive systems, achieving stable and optimal motor operation is crucial, particularly in environments characterized by disturbances. The implementation of model predictive control (MPC) represents a strategic methodology. However, conventional predictive controllers frequently encounter challenges due to uncertainties regarding the motor's internal parameters and external load characteristics, subsequently impacting the effectiveness of the control algorithm. This paper proposes a new robust predictive control combined with an artificial neural network (RMPC-ANN) approach applied to a permanent magnet synchronous motor (PMSM) to tackle challenges posed by external perturbations and parameter variations. The development of the proposed robust predictive controller involves optimizing a novel finite horizon cost function based on Taylor series expansion, which incorporates dual integral action into the control law. Crucially, this approach eliminates the necessity for measuring and observing external perturbations and parameter uncertainties. Additionally, for attaining high-precision speed control, the speed loop regulation relies on a multi-layer feedforward ANN algorithm. A comprehensive comparison was conducted using MATLAB/SIMULINK, assessing performance across diverse operating conditions. To further substantiate the numerical simulation results, a hardware-in-the-loop (HIL) configuration is implemented on the OPAL-RT platform, demonstrating the robustness and efficiency of the proposed control strategy