International Journal of Engineering Research in Africa Vol. 57

Abstract: Contamination of surface water bodies by a wide range of organic and inorganic pollutants has been a serious problem in the recent time, these have an effect on human and aquatic animals. The water quality deterioration calls for regular monitoring of the water quality in order to maintain the health and sustainability of the aquatic ecosystems. Accurate monitoring of discharged pollutants into the rivers may be time taking and labour intensive. Water quality models are significant tools for simulating water quality and controlling the surface water pollution. The purpose of this study is to develop a simplified mathematical model which is hybrid cells in series model (HCIS) to simulate the spatial and temporal variation of nitrate concentration in natural rivers. The HCIS model was formulated to serve as an alternative method to the Fickian based models. Analytical solutions for the first order reaction kinetics of nitrate with the advection and dispersion process were derived using Laplace transformation technique. The model considered the effect of nitrate concentration at several points along the river downstream by considering the transformation of nitrite to nitrate through nitrification process. In addition, the uptake of nitrate by algae for its growth and conversion of nitrate to nitrogen gas due to denitrification process were considered. The HCIS-NO₃ model was applied to uMgeni River, South Africa to investigate the nitrate concentration along the river. Furthermore, the quantitative measures based on the coefficient of determination (R²) and standard errors (SE) were used to evaluate the performance of the model. The result shows that the simulated values agreed with the measured values of nitrate concentration in the river which resulted in a R² value of 0.72 and a low standard error. Analytical solutions of HCIS - NO₃ model were compared with the numerical solutions of the Fickian based ADE model for hypothetical problems. Comparison of the responses indicates that the HCIS - NO₃ and ADE- NO₃ models were in good agreement. The study shows that the hybrid model is a simple and effective tool for simulating pollutant transport in natural rivers.

Abstract: This study was carried out to measure some physical and mechanical properties of the sunflower seeds variety “DW667”. The physical properties (length, width, thickness, equivalent diameter, sphericity, surface area of seed, one thousand seed mass, bulk and true density, porosity) and mechanical properties (compressive load and displacement deformation for vertical and horizontal orientations) were measured at 4%, 10%, 15%, 20% and 25% Dray basis (d.b.) moisture contents. Higher moisture content from 4%to25% increased length, width, thickness, equivalent diameter, sphericity, surface area of seed, one thousand seed mass, bulk and true density, porosity and deformation displacement at the vertical and horizontal orientations of seeds increased from 10.57 to , 4.50 to , 2.85 to , 5.13 to , 49 to 50 %, 82.95 to 94.53 mm², 33.70 to , 286.80 to 314.98 kg/m³, 406.47 to 483.61 kg/m³, 29.22 to 34.54 %, 1.63 to 2.63 mm and 0.70 to 1.87 mm, respectively. While the required compressive force for rupture seeds decreased from 25.3 to 12.39 N and 11.5 to 5.63 N for vertical and horizontal orientations, respectively with moisture contents uprising from 4 to 25 %. The findings of this study will open new windows in farm mechanization for the designing and improvement of treatment machines for this type of seed.

Abstract: Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.

Abstract: Abstract-This paper proposes a novel hybrid technique that combines the priority list (PL) with the binary crow search algorithm (BCSA) for solving the unit commitment problem (UCP). Firstly, the PL method aims to sort the generating units in ascending order according to their average full load costs which are the total costs that are computed at the maximum generation outputs. Secondly, the BCSA is developed and employed to search for the optimal schedule of the generating units to face the next hourly demand with minimum total operating costs that are related to the optimal power generation schedule at certain loading level. BCSA is a new meta-heuristic optimizer, which is featured of the crow's intelligence. It has only two adjustable parameters that make its implementation very simple and easy compared to other optimization techniques. Its effectiveness and feasibility were confirmed by 4, 10, and 26-unit systems and the results are compared with those obtained by GA, PSO, APSO, and BDE. The simulation results demonstrate the capability of the proposed PLBCSA in solving the UC problem with good convergence rate compared with the previous methods in the literature. Around of 2-4% reduction in the total costs is achieved using the proposed PLBCSA for the 26-unit test system compared with GA, PSO and the implemented PL-BPSO solutions.

Abstract: Energy harvesting wireless sensor network (EH-WSN) harvests energy from the environment to supply power to the sensor nodes which apparently enhances their lifetime. However, the unpredictable nature of the resources throws challenges to the sustainability of energy supply for the continuous network operation. This creates a gap between unstable energy harvesting rates & energy requirements of the nodes of the network. The state-of-the-art algorithms proposed so far to address this problem domain are not able to bridge the gap fully to standardize the framework. Hence there is considerable scope of research to create a trade-off between EH techniques and specially designed protocols for in EH-WSN. Current study evaluates the performance and efficiency of some futuristic techniques which incorporate advanced tools and algorithms. The study aims to identify the strength and weaknesses of the proposed techniques which can emerge specific research requirement in this field. Finally, we propose a research direction towards Multi-source Hybrid EH-WSN (MHEHWSN) which is able to maximize energy availability and functional efficiency. The scope of this study is to develop a notion of a framework which eliminates the limitations of very recent techniques of EH-WSN by including multiple energy resources to extract required energy even in presence of unpredictability. However, keeping in mind the ease of use and less complex structure Multi-source hybrid EH technique requires a careful design paradigm.