Performance evaluation of a motorized legume threshing machine

Grain threshing and separation are important post-harvest unit processes because it reduces excessive wastage and adds values to farm produces amongst other benefits, thus the performance parameters of the legume threshing machine was determined experimentally. The effect of static coefficient of friction was on the Threshing Efficiency (TE), Separation Efficiency (SE), Grain damage and Machine throughput were determined. The study was carried out using a randomized design of three feed rates (FR): 10g/s, 15g/s and 20g/s, four surfaces (S) mild steel (MS), plywood (PLD), carpet (CPT) and Rug (RG) using cowpea (Vigna unguiculata) IT84S-2242 and soybean (Glycine max L) 1448-2E variety. The cleaning efficiency obtained for soybean samples was 96%, while threshing efficiency was 99%. For cowpea threshing and cleaning efficiencies were 97.44 and 97.16%, from carpet surface. Statistical analysis using (ANOVA) showed that impurity level and feed rate affected cleaning efficiency at both 1 and 5% significance, type of surface affected threshing percentage and broken seeds at 5% significance. Using carpet surface resulted in the highest cleaning efficiency, threshing efficiency, low grain damage and grain losses at 100 g batch weight. In conclusion, coefficient of friction could be utilized to increase separation efficiency of thresher, this would aid the development of appropriate technologies for legumes processin

Investigating anomalous seepage trends in selected earth dams: a comprehensive study

Previous geotechnical reports established that the selected three dams, Igbeti, Awon, and Asa dams’ embankments are loosed and permeable and there is possibility of erosion within dam embankment. Geotechnical and seepage analyses of the earth dams were conducted to evaluate the dams’ safety against the leakages through the embankment dam. Samples were collected at three different locations from the upstream and downstream sides of the dams, at depth of 600 mm using auger borer, and geotechnical tests were conducted on the samples, according to BS1377 of 1977 to determine the specific gravity (SG), sieve analysis, cohesion (C), angle of internal friction (?), coefficient of permeability (K), and natural moisture content (NMC). Steady- state analysis, using SEEP2D was employed to investigate the seepage flows within the dams, to simulate flow rates, pore pressure, velocity magnitude, hydraulic gradient, and seepage quantity. Specific gravity values of the samples ranged from 2.36 to 2.79 (upstream) and 2.27- 2.75 (downstream). The particle sizes passing through sieve number 200 (0.002 mm) varied from 1.00 – 21.21% (upstream) and 0.58- 23.71% (downstream), while maximum dry densities are within the limiting values of 19- 23.5 kN/m2 (upstream) and 35.5 – 39.5 kN/m2 downstream. Permeability coefficients obtained varied averagely from 1.36 x 10-5 to 8.18x 10-4 at upstream and 9.32 x 10-6 to 4.45x 10-4 at downstream, and values obtained classified the soils as low- permeability, silty clay embankment materials. Natural moisture contents varied from 10.47% - 26.72% at upstream, 10.71%- 23.60% at downstream. Seepage analyses results for Igbeti, Awon and Asa dams were respectively: flow rates (3.9 x10-7 – 7.8 x10-7; 2.98x10-6 – 3.32x10-6; 6.00x10-6 – 6.60x10-6 m3/s); pore pressure (50000 – 16800; 60000 – 93000; 130000 – 185000 kN/m); velocity magnitude (0.0 – 0.000705; 7.93x10-11 - 6.30x10-6, 9.24x10-11 – 5.85x10-6 m/s). The flow rates through the selected dams showed saturated embankments with the possibility of piping, and excessive leakage. Installation of internal drainage facilities such as sand filters, and toe gravel drains were recommended

Development and performance of a hot-press machine for particle-board production

Waste management in wood and agricultural processing industries conserves resources, energy, and money. Hot-press machines can help small-scale producers afford essential materials like lamination, composites, and woodworking, but current models struggle with tracking process parameters, frequent maintenance, and productivity loss. This research aim at development of a hot-press machine for composite production. Design for the machine include the frame upper and lower platens, hydraulic-jack-base, and mixing-unit, control-box, mould and mould-plate. The components bought off-shelve were hydraulic-jack, heating-element, thermostat, and pressure-gauge. Design done accordance to standard methods. Fabrication process was done at Works and Maintenance Metal Workshop, University of Ibadan. Agricultural-waste Z.mays-cob were sourced, milled, air dried and sieved and retention on sieve number 2.00mm was used for the composite production. Performance evaluation of the machine was done using 60:40; 70:30 and 80:20, of Z.mays-cob particle and Urea-Formaldehyde as the composite-mix ratio. Density, water-absorption, and thickness-swelling were determined for 2 and 24hours respectively. Optimal temperature was 1200C and regulated with thermostat connected to 1500W heating-element, while 3bar pressure-gauge was incorporated onto a 5ton hydraulic-jack. The board densities were significant. Water-absorption and thickness-swelling were found favourably. The hot-press machine was successfully developed with a cost of $95 and was able to produce composite board suitable for interior usage from agricultural-waste.   Keywords: Z.mays-waste, Pressure, Temperature, Composite material, particle boar

Optimal location and sizing of thyristor controlled series compensation on Nigerian longitudinal transmission system using dragonfly algorithm: Optimal location and sizing of thyristor controlled series compensation on Nigerian longitudinal transmission system using dragonfly algorithm

Enhancement of longitudinal transmission system through voltage profile and line flow control is achievable through Thyristor Controlled Series Compensator (TCSC) incorporation in power systems. The use of an existing method such as arbitrary placement of TCSC was found to be ineffective for these purposes compared to the optimal placement approach. Power flow equations of the power system were linearized with the use of the Newton-Raphson (NR) iterative technique at the steady state. Dragonfly Algorithm (DA) was adopted for optimal placement of the TCSC and simulated in MATLAB R2018b environment. The DA was implemented on the Nigerian 28-bus power system for normal loading and at 25% overload. The voltage profile deviations of buses 9, 16, and 22 that were more than ±5% were controlled to fall within the acceptable ranges and the heavily loaded transmission lines were redirected. The optimized placement of TCSC gave a better result when compared with the conventional TCSC placement

Solid Wastes and Greenhouse Gas Emissions Management for Energy Derivation: Case Study of LAUTECH Ogbomoso and Environs

Final disposal of solid wastes at Ladoke Akintola University of Technology (LAUTECH) Ogbomoso, and its environs is by scavenging, dumping sites and open-air burning. This research aimed at studying the solid waste generation and greenhouse gas emissions management for energy derivation at LAUTECH and environs. The university was divided into sixteen zones based on Faculties and other prevailing activities on campus. Waste samples were obtained from bins and dumping sites, for 5 days (Monday, Tuesday, Wednesday, Thursday and Friday) in three years (2021, 2023 and 2024) for waste composition data. Sorted waste samples were taken to the laboratory to carry out moisture and energy content analyses. Methane (CH?) and Carbon dioxide (CO?) emissions from dumping sites and farm areas within LAUTECH and its environs were also measured using gas detectors. The collected primary data was analyzed statistically and discussed. Estimated waste generation in LAUTECH was 6161.47 kg/day, resulting in a daily waste generation rate of about 187 g per head, considering a university population of 33,000. The Energy content of daily wastes was 107.19 MJ, implying an electricity generation up to 0.02977 MWh (approx. 29.77 kWh) from daily steam production. Methane (CH?) levels range from 75 ppm (Rabbit Unit) to 2,107 ppm (layer birds, Abogunde Farms) and CO? concentrations vary between 400 ppm and 470 ppm, across farms. However, methane levels recorded peak values e.g., 11,169 ppm at AA Rano, 8,763 ppm at college, and 6,900 ppm at ALICE. CO? is highest at college (1,171 ppm) and AA Rano (1169 ppm). TVOC and HCHO values remain low at farm sites, while elevated at dumpsites. Considering the high material recyclability, reusability and energy recovery potentials from solid wastes generated from LAUTECH Ogbomoso and environs, there is an urgent need for emissions control in high-risk dumpsites through methods such as methane capture and air quality filtration. These actions are critical for environmental protection and safeguarding public health

Health Risk Assessment of Nitrate Concentration in Soil and Water within the Sango area, Ibadan

  Drinking water contamination by nitrates poses serious health risks, particularly to infants and pregnant women. Rapid urbanization in Sango, alongside poor sewage and industrial waste management, intensifies nitrate pollution, endangering public health. The aim of this research is to assess the nitrate concentration in drinking water sources in Sango area of Ibadan metropolis. Twenty-two sampling points (SW1–SW22) were selected using stratified sampling. Water samples were collected during both rainy and dry seasons. Nitrate concentrations and key physicochemical parameters pH, temperature, turbidity, dissolved oxygen (DO), and electrical conductivity (EC) were measured. Daily nitrate intake was estimated across age groups and compared with WHO guidelines. Nitrate levels ranged from 125 - 285 mg/L (rainy season) and 67.42 - 153.67 mg/L (dry season), significantly above WHO limits. pH ranged from 6.1 - 9.3 and 6.32 - 9.47; turbidity, 28.6 - 49.3 NTU and 20.34 - 34.54 NTU; DO, 5.02 - 8.9 mg/L and 4.54 - 7.63 mg/L; EC, 206.4 - 907.5 µS/cm and 230.72 - 980.44 µS/cm during rainy and dry seasons, respectively. Estimated nitrate intake across all adult age groups exceeded the WHO acceptable daily intake thresholds, indicating significant health risks. The total nitrate intake across body weight categories exceeded WHO's estimated acceptable nitrate intake (mg/kg) in both rainy and dry seasons. Elevated levels, especially during the rainy season, pose significant health risks, surpassing WHO limits across demographics and seasons. Enhancing water treatment infrastructure, promoting rainwater harvesting, and improving filtration systems during periods of peak contamination can significantly reduce nitrate exposure

Performance evaluation of Osprey optimization algorithm-based proportional integral derivative controller for speed control of a brushless direct current motor

ABSTRACT Owing to diversity of application, speed regulation of Brushless Direct Current (BLDC) motor is essential in order to achieve best performance of the motor. In this paper, an appropriately tuned controller such as Proportional Integral Derivative (PID) is employed to achieve effective speed control of the motor. In tuning the parameters of PID controller, conventional techniques often pose great difficulties due to non-linearity often exhibited by DC motors. As a solution, metaheuristic optimization techniques are adopted to optimally tune the PID controller parameters for optimal performance of the BLDC motor in terms of speed. Thus, Osprey Optimization Algorithm (OOA) tuned PID controller (OOA-PID) was used to achieve better performance of BLDC motor speed. Kirchoff’s Voltage Law and Newton’s second law of motion were employed to derive the BLDC motor mathematical model. The PID mathematical equation was also described and an optimization model was formulated using the Integral of Time Multiplied Absolute Error (ITAE) and optimized using OOA. The performance of the OOA-PID controller with BLDC motor was evaluated using performance metrics such as rise time, settling time, overshoot and steady state error. Simulations were done using MATLAB (R2021b). Simulation result shows that an OOA-PID controller gave better response when compared with existing ziegler Nichols PID (ZN-PID) used for the same purpose. &nbsp

Performance analysis of deep learning-based automatic modulation recognition over wireless communication

Automatic Modulation Recognition (AMR) based on Deep Learning (DL) is an efficient technique to improve spectrum utilization by replacing the old way of detecting modulation type through the allocation of modulation information in the signal frame. However, DL models have the problem of low recognition accuracy when dealing with a dataset containing in-phase and quadrature channel data. Hence, in this work, the enhancement of DL models that automatically recognize different types of modulation techniques with an increase in recognition accuracy was carried out. The two utilized dataset were RadioML2016.10a and RadioML.2016.10b. Convolutional Neural Network with RadioML2016.10a (ECNN-1) and RadioML2016.10b (ECNN-2) and Long Short-Term Memory with RadioML2016.10a (ELSTM-1) and RadioML2016.10b (ELSTM-2) were implemented in Python 3 using Google Colab. Adam optimizer was applied to optimize the hyperparameters of DL models. ECNN-1 and ECNN-2 have recognition accuracy values of 81% and 88%. The accuracy values obtained for ELSTM-1 and ELSTM-2 were 79% and 85%. The ROC AUC score for the ECNN-1, ECNN-2, ELSTM-1, and ELSTM-2 were 89.63%, 92.90%, 90.92%, and 92.81%, respectively. The experimental results showed an improvement in modulation recognition accuracy for both enhanced CNN and LSTM models

Sentiment Analysis of Movie Reviews using Word Embeddings and Machine Learning Techniques

In this study, sentiment analysis of movie reviews was carried out using word embeddings and machine learning techniques. Sentiment analysis, as an opinion mining technique, involves using feature extraction methods to understand the opinions and emotions expressed in text—particularly in domains such as movie reviews, where public sentiment plays a strong role in shaping consumer decisions. For sentiment analysis to be effective, text must be converted into a form that a computer can process. This involves transforming words or documents into vectors using word embedding techniques. Common techniques include Bag of Words, TF-IDF, and Word2Vec. In this study, TF-IDF and Bidirectional Encoder Representations from Transformers (BERT) were selected to compare their effectiveness in analyzing sentiment in movie reviews. The research used the IMDb dataset, which is widely recognized and commonly used in text mining tasks. Various machine learning models were applied, including Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM). Results showed that the combination of TF-IDF and SVM produced the highest accuracy, outperforming more complex models such as BERT with LSTM. The findings suggest that simpler word embedding techniques, when paired with effective classifiers, can give strong performance in sentiment analysis

Evaluation of the effect of doubling the stator slot number of a permanent magnet machine

The slot numbers of an electric machine play an important role in the machine’s output performance(s). Thus, the significance of doubling stator slot number of a Double Stator (DS) Permanent Magnet Machine (PMM) is presented in this study; to evaluate its impact on the overall electromechanical output of the considered machine and for better guide on appropriate slot-pole number combinations of the chosen machine type. The number of slots considered is six (6) slots and its corresponding binary is taken to be twelve (12). The machine indices comprise: flux linkage, induced voltage, torque, loss, power, and efficiency. Finite Element Analysis (FEA) is implemented in this investigation using MAXWELL-2D software. The study shows that the 12-slot machine configuration has higher flux linkage, induced voltage, power, and torque values compared to its equivalent 6-slot machine. The predicted shaft torque and power of the 6-slot machine are 0.93 Nm and 353.5 W, respectively, while the corresponding values obtained for 12 slots are 1.42 Nm and 491.3 W. However, greater electromagnetic loss and consequent lower efficiency are obtained from the 12-slot machine type, coupled with high usage of magnetic materials and likely higher cost consequences. The investigated machine is suitable for in-wheel traction applications