LAUTECH Journal of Engineering and Technology

Evaluation of biogas yield from co-digestion of varying particle sizes of corncob with poultry manure and process parameters optimization study

2025-03-11T08:28:10+00:00

The need to improve renewable energy generation, advance sustainable waste management techniques, and uncover beneficial agriculture methods, necessitated anaerobic co-digestion of grounded corncob (GC) biologically pretreated with cattle rumen (inoculum) and poultry poos (PP) for biogas generation, as discussed in this study, Corncob biomass (CB) and PP (substrates) were obtained at the teaching and research laboratory in LAUTECH Ogbomoso. CB was pretreated using mechanical grinding and sieving methods and then divided into two portions labelled A and B, using sieve sets of 0.30mm and 0.45mm. The ratio of the combination of substrates: GC: PP: inoculum is 1:0.5:0.5. Standard procedures were used to assess the physicochemical parameters of the substrates and digestates. Central Composite Design (CCD) was used to batch the experimental design of pretreated samples A and B with PP and inoculum to produce biogas which was analysed for methane content using a gas chromatograph mass spectrometer. Response Surface Methodology (RSM) was used to optimize data generated for temperature, pH, retention time, total solids, and volatile solids (VS) using the 'Design-Expert Application' version 11.

The biogas yields for experiments A and B were 1.368 L/kg VS and 1.221 L/kg VS, while the Methane compositions were 60.44% and 57.58%, respectively. The optimized data for A and B were; temperature (40 ^oC, 40 ^oC); pH (8.0, 6.0); retention time (30, 30 days); total solids (12, 4 g/kg); and volatile solids (12, 12 g/kg) respectively. The model's coefficient of determination (R²) was high (0.9267) for A, indicating strong modelling and prediction accuracy, thus, recommending the usage of corncob for bioenergy generation.

Influence of stator tooth thickness on losses and efficiency of a double stator electric machine

2025-02-23T07:32:22+00:00

Owing to the significant impacts of the electric machine's structural dimensions on its output characteristics and values, the effect of stator tooth thickness and shape is considered in this study. Finite element analysis is employed in the predictions. The machine parameters considered are magnetic properties, efficiency, and losses. It is revealed that if the analyzed machine has unequal stator tooth thickness without a stator tooth tip, then the resulting magnetic properties and eddy current loss values would be high; however, with improved overall machine efficiency. Consequently, a similar machine configuration that has equal stator tooth thickness with a stator tooth tip would exhibit larger total loss and a corresponding lower overall efficiency. The predicted magnetic coercive force values of the machine with and without equal stator tooth thickness are: 1415.51 kA/m and 1505.92 kA/m, respectively. Again, the resulting total losses of the machine with and without equal stator tooth thickness are 19.71 Watts and 19.16 Watts, respectively. Moreover, the improved machine type with unequal stator tooth thickness generated an efficiency of 92.83 % against its counterpart, which produced a corresponding efficiency of 92.60 %.

Development of an enhanced support vector machine face recognition system

2025-02-16T15:29:54+00:00

Face recognition biometric authentication focuses on uniquely recognizing human facial appearance based on inherent physical traits of the face for application in access control. The use of face for recognition has been proven to be highly reliable and effective. This research performed a performance evaluation of SVM-based variants in the recognition of facial images. Six facial expression images, each from sixty individuals, were locally acquired using a Canon EOS 2000D digital camera at 200×200-pixel resolution, 240 images were used for training, while 120 images were used for testing. The acquired images were converted into grayscale and normalized using the histogram equalization method. Features classification was carried out using a Support Vector Machine for PCA-PSO and PCA, respectively. The performance of the two techniques was evaluated and compared at a 0.42 threshold using Recognition Accuracy (RA), Precision (P), Sensitivity (S), and Recognition Time (RT). The validation of the techniques was done using t - a t-test at a significant 5% level. The RA, P, S, and RT were 97.50%, 97.80%, 98.89%, 1487.16 s, and 3.80s for PCA-PSO-SVM, while the corresponding values for PCA-SVM were 95.83%, 96.70%, 97.78%, 1861.79 s, and 22.96 s, respectively. The paired t-test was P = 0.001 with a mean difference of 2.5%. The PCA-PSO-SVM technique performed better than PCA–SVM for all metrics. A face recognition system based on PCA-PSO-SVM is a more reliable security surveillance system than PCA-SVM

Mechanical and structural impacts of fly-ash reinforcement on graphite-aluminum composites

2025-02-20T11:03:49+00:00

Materials with light-weight, optimum thermo-mechanical properties are in great demand for thermal management systems. Graphite-Aluminum (Gr-Al) composites are being reinforced to achieve the above objectives. This paper reports the effects of fly-ash powder on the mechanical and structural characteristics of spark plasma-sintered Gr-Al composites. Graphite powder (53 ?m) and 20%wt aluminum powder (1-2 ?m), were reinforced with 0, 10, 20, and 30 %wt fly-ash powder (53 ?m), designated as GA, GAF-1, GAF-2, and GAF-3, respectively. Sintering was conducted at a heating rate of 50 oC/min, temperature of 550 oC, pressure of 5x10-2 mbar, and 10-minute holding time. Characterization was based on morphology, microhardness, displacement, relative density, peak intensity ratios, and tensile strength. The fly-ash particles had a cup-like shape, with dominance clearly noticeable in GAF-3. GAF-1 had the highest increase in peak intensity of 12.67%. GA showed the highest displacement rate (0.983 mm/min within the first 4 minutes of heating) and instantaneous relative density (0.97). However, an increase in fly-ash led to a 11.22% increase in porosity and a 35.85% increase in the micro-hardness while GAF-3 gave the highest ultimate tensile strength of 388.92 MPa. Reinforcing the Gr-Al matrix with Fly-ash makes it suitable for industrial applications.

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

2025-03-21T15:26:05+00:00

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.

Impact of spay-dried banana powder incorporation and incubation duration on sensory attributes of yoghurt

2025-03-04T13:39:31+00:00

Banana is a widely consumed fruit known for its natural sweetness, rich aroma, and functional properties. Incorporating banana-derived ingredients into dairy products like yoghurt can enhance sensory quality while offering potential health benefits. This study investigates the effect of spray-dried banana powder incorporation (0%, 20%, 25%, 30%) and incubation time (4h, 6h) on the sensory attributes of yoghurt, including appearance, aroma, taste, texture, and overall acceptability. Sensory evaluation, conducted using semi-trained panelists, revealed that banana powder significantly enhanced all sensory parameters compared to control samples (0% banana). The best-rated formulations, A2 (20% banana, 6h) and C2 (30% banana, 6h), exhibited improved flavour balance, a creamy texture, and high overall acceptability. In contrast, yoghurts without banana powder (D1, D2) had the lowest scores, likely due to their higher acidity and lack of fruit-based sweetness. A longer incubation time (6h) further improved taste and aroma retention, possibly due to increased fermentation-derived volatile compounds. Additionally, banana powder contributed to enhanced thickness and viscosity due to its fiber and pectin content, improving texture. These findings suggest that banana powder at 20–30% with a 6-hour incubation period enhances yoghurt's sensory quality, making it a promising functional ingredient for dairy products.

Influence of elastobar and zycoprime nanochemicals on concrete properties with coarse aggregate partially replaced by periwinkle shell

2025-02-20T05:39:12+00:00

Solid waste management, particularly of periwinkle shells, poses a persistent environmental challenge in Nigeria's riverine regions. While past studies have explored the use of periwinkle shells in concrete, limited attention has been given to the potential benefits of nano-chemical additives. This study investigated the properties of concrete mixed with periwinkle shells and nano-chemicals (Elastobar and Zycoprime) enhancing its performance. Periwinkle shells were obtained from Ilaje, Lagos State, and subjected to particle size distribution analysis to determine their coefficient of curvature (Cc) and uniformity coefficient (Cu). Their porosity, permeability, specific gravity, aggregate impact value (AIV), and aggregate crushing value (ACV) were determined according to standard methods. Concrete of grade M30 was prepared with partial periwinkle shell replacement at 0, 20, 40, 60, 80, and 100% aggregate. Tests included slump, bulk density, compressive strength, tensile strength, and water absorption at 28 days. The microstructure of the optimal mix was analyzed using Scanning Electron Microscopy (SEM). The periwinkle shell had Cu (1.31), Cc (1.80), porosity (45%), permeability (1.2 × 10?² cm/s), specific gravity (2.68), AIV (40.2%) and ACV (37.6%). The slump, bulk density, compressive strength for slumps above 20 mm, tensile strength, and water absorption ranged from 51.56 to 76.12 mm, 2345.78 to 2360.25 kg/m³, 10.91 to 42.10 MPa, 1.49 to 4.50 MPa, and 3.6 to 26% respectively. SEM images revealed a denser microstructure with fewer voids in the optimal mix. Incorporating Elastobar and Zycoprime with periwinkle shells in concrete enhances its performance, making it suitable for mass concrete and load-bearing applications.

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

2025-01-27T08:07:52+00:00

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.

Determination of optimal sampling strategy and water quality characterization of Ikere reservoir, Iseyin, south-west Nigeria

2025-02-05T20:24:28+00:00

Water resources are essential for sustaining human life and socioeconomic activities, with reservoirs serving as critical water bodies. However, limited data on the Ikere reservoir’s current water quality hinders effective management. This research aims to assess the quality of water variation to develop an optimal sampling strategy for the Ikere Gorge Dam, Iseyin, Oyo State. Nigeria. Laboratory analysis was conducted on six (6) water samples from both the rainy and dry seasons at the study area, adhering to APHA (2017) Standard Methods for the Examination of Water and Wastewater, and encompassing physicochemical and biological parameters as well as heavy metals. The results were compared to Nigeria's Food and Drug Administrative Control (NAFDAC, 2020) and World Health Organization (WHO, 2017) standards. Principal Component Analysis (PCA) and Cluster Analysis (CA) were employed for the statistical analysis of the water quality parameter data to determine the optimal sampling strategy within the study area. The physicochemical, heavy metals, and biological parameters for the rainy and dry seasons, including pH, electrical conductivity, temperature, turbidity, total dissolved solids, E. coli e.t.c showed values ranging from 6.41 to 6.77, 72.23 to 91.37 µS, 24.10 to 29.23°C, 1.30 to 10.23 NTU, 0.01 to 0.10 mg/L, and 12.33 to 47.67 MPN/100mL. Parameters such as turbidity, phosphates, DO, and E. coli exceeded WHO and NAFDAC standards. This indicates potential health risks and environmental pollution. PCA results indicate the variance distribution across five principal components, with significant clustering patterns. In conclusion, integrating CA and PCA is essential for effective water quality assessment at Ikere Gorge Dam. CA identified distinct clusters, while PCA revealed key factors like COD and hardness reflecting natural influences and turbidity and copper indicating pollution.

Investigation of process parameters for producing bio-oil from luffa cylindrical fiber in a fixed bed reactor using pyrolysis process

2025-03-14T07:40:05+00:00

This study was performed to assess the impact of pyrolysis parameters on the yield of pyrolytic bio-oil during the thermal degradation of luffa cylindrical fiber in a fixed-bed reactor. The study revealed that the optimal bio-oil output of 29 wt% was attained at a reactor temperature of 600 °C, a biomass particle size of 4 mm, and a nitrogen gas flow rate of 1.5 L/min. The Gas Chromatography-Mass Spectrometry (GC-MS) analysis of the bio-oil revealed the presence of phenols, alcohols, carboxylic acids, ketones, alkenes, alkanes, aldehydes, and aromatics, indicating that the pyrolysis of luffa cylindrical fiber could be a viable approach for producing renewable fuels and chemicals while mitigating environmental pollution concerns.

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

2025-02-23T07:16:45+00:00

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.