Nigerian Journal of Technological Development

Editorial Information Vol. 21, Issue 3, September 2024

Nigerian Journal of Technological Development — Sun, 29 Sep 2024 00:00:00 +0000

Editorial Information Vol. 21, Issue 3, September 2024

Preparation and characterization of waste fish bone-derived gelatin/chitosan plastic film incorporated with green tea extract

Sun, 29 Sep 2024 00:00:00 +0000

Fish-derived gelatin has recently been spotlighted as an alternative source following the negative issues of mammalian gelatin, but unfortunately it has poor mechanical properties that limit its application. Herein, an environmental-friendly film was prepared from milkfish (Chanos chanos) bone waste-derived gelatin and modified by adding chitosan, glycerol and sorbitol plasticizers as well as green tea extract to improve its mechanical properties. Gelatin was processed from milkfish bone waste using various concentrations of H₂SO₄ solution (2, 3, and 5%) (v/v) and soaking times (18 and 36 h). The measurement test of pH, ash content, and moisture content were conducted to the gelatin product, as well as the characteristic functional groups were investigated using Fourier-Transform Infrared Spectroscopy (FTIR). The effect of green tea addition was also evaluated on the thickness, tensile strength, and elongation of the resulting films. The results show that treatment using H₂SO₄ concentration of 3% with 36 hours of immersion time produces the best gelatin quality with a pH value of 5.9, water content and ash content are 14.286% and 3.09%, respectively. The plastic film from this study has mechanical properties similar to polypropylene plastic so that it is expected to be a potential material for food packaging applications.

Impact of Design Dimension Optimization on Capacitive Sensor Performance for Particulate Matter Detection and Measurement

Vaishali Kulkarni, Suvarna Chorage — Sun, 29 Sep 2024 00:00:00 +0000

Particulate matter (PM) emissions from exhaust gases are major pollutants and cause serious health problems. Diesel particulate filters (DPF) are used for monitoring and trapping particulates from exhaust gases. For sensing these particulates sensors are used downstream of a DPF. The present study proposes an interdigitated electrode (IDE) capacitive sensor for detecting and measuring deposited particulates on the sensor surface. The sensor dimensions are optimized to detect and measure the least deposition of particulates. The paper presents improvement in sensor performance, a high selectivity of 65.70% with an accuracy of 87.26%. Dimension optimization extracts capacitance of 581 pF manifolds 10 times more than the reference sensor from the literature. The study presents a sensor with a thin sensing layer manufactured by optical lithography and a lift-off method for IDE. Measurements and testing showed that the sensor measures the lowest particulate mass of 0.0045 mg in 3.4 ms.

Effect of Modulation Domain Coupled Kalman-Spectral Filter on Speech Enhancement over Wireless Voiced Communication System

Damilare Akande, Esther Olawole, Zachaeus Adeyemo, Festus Ojo, samson Ojo — Sun, 29 Sep 2024 00:00:00 +0000

Noise suppression in speech signal has received great attention in signal processing research community. However, existing methods of noise suppression such as the time-domain Kalman filter suffers from the psychoacoustic and physiological characteristic feature while the spectral subtraction technique performed in the modulation domain is associated with remnant noise in the enhanced speech signal. Therefore, in this paper, a Modulation-Domain coupled Kalman-Spectral Filtering (MD-KSF) in a single channel system is proposed to address the aforementioned shortcomings in the two existing techniques. The simulation of the proposed coupled MD-KSF was performed in MATLAB software environment. The evaluation of the proposed coupled MD-KSF technique in terms of Spectral waveform, Mean Square Error (MSE) and Log Spectral Distance (LSD) was performed. Furthermore, Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) tests were employed to validate the quality and intelligibility of the proposed coupled MD-KSF using the NOIZEUS corpus data set. The proposed technique shows significant noise suppression over the existing techniques.

A Novel Acinetobacter sp. Isolated from Oil-contaminated Soil for Microbial Enhanced Oil Recovery

Sun, 29 Sep 2024 00:00:00 +0000

The viscosity of hydrocarbon reservoirs increases with age. So, a significant amount of oil is trapped in the underground reservoirs after recovery using primary and secondary methods. To improve the recovery, tertiary methods have been used. However, there is limited research on Microbial Enhanced Oil Recovery (MEOR) as a tertiary method due to microbes' inability to survive the high-temperature reservoir conditions. Consequently, the goal of this research is the creation of a novel bacteria strain for MEOR. The isolated bacteria colonies were inoculated in a batch fermentation broth, and the biosurfactant produced was screened using the oil displacement, emulsification index, modified drop-collapse test, surface tension, and interfacial tension criteria. The effects of pH (7.2 – 10.52), and salinity (15 – 35 %) at optimum temperature were studied on the selected isolate, which was identified by partial 16 rRNA gene sequence analysis. Gas Chromatography-Mass Spectroscopy was used to characterize the biosurfactants produced. The new isolate reduced heavy oil viscosity by 17% and produced a recovery factor in the range of 13-16.72%. The new bacteria is a thermophile and survived at a temperature of 65 °C, indicating promise for use in MEOR.

Polychlorinated Biphenyl Levels and Associated Health Risks in Indoor Atmosphere of Beauty Salons

Ayodele Sarat Atanda, Jamiu Adetayo Adeniran, Tunmise Latifat Adewoye — Sun, 29 Sep 2024 00:00:00 +0000

Exposure to high concentrations of Polychlorinated Biphenyl (PCBs) has negative impacts on human wellbeing and the climate. This study determined the concentration, sources and health risks of PCBs in selected beauty shops in Ilorin, Kwara state. Air samples from fourteen beauty salons were taken using Solvent-Impregnated Polyurethane Foam (SIP-PUF) passive samplers for 30 days. The concentration of PCBs was determined with Gas Chromatography-Mass Spectrometry (GC–MS) operated in a Selected Ionization Mode (SIM). The health risk assessment of the PCBs was estimated using the Toxicity Equivalence Quotient (TEQ), Incremental Life Cancer Risk (ILCR), and Hazard Quotient (HQ) prescribed by United State Environmental Protection Agency (USEPA). The Positive Matrix Factorization (PMF) method was used in the source apportionment study. The average concentration of the PCBs ranged from 0.11–3.32 µg/m³. The average TEQs for the PCBs in the beauty salon ambient air ranged between 2.96 × 10⁻¹ - 9.30 × 10⁻¹ ng WHO-TEQ/m³. ILCRs and HQs estimated for the beauty shops considered were lower than 10^-6and 1 (USEPA set limit) for both adults and children. Eight factors were identified to be associated with the PCB sources; the most predominant sources are combined paint and pigment and mixed sources, which accounted for 20.08 and 28.91% of the total PCBs, respectively

Dynamic Mode Decomposition-Based Features for Ovarian Cancer Gene Expression Classification Using Machine Learning

Sun, 29 Sep 2024 00:00:00 +0000

Machine learning (ML) algorithms have been deployed in recent years as models for the analysis of complex data. The ubiquity of ML algorithms stems from their ability to learn the patterns and structures inherent in data. Additionally, they are adaptable to a wide range of data types, irrespective of size, which allows them to learn and predict the future pattern of data. In this work, dynamic mode decomposition (DMD), an ML algorithm, is deployed to analyse the pattern of gene expression data from patients with and without ovarian cancer. Ovarian cancer is one of the deadliest gynaecologic cancers worldwide. The obscure nature of the symptoms makes early detection of ovarian cancer difficult. Early diagnosis increases the chances of survival for patients. Furthermore, the modes computed from DMD are used as features and separately fed into three ML classifiers- support vector machine (SVM), decision tree (DecTr), and K-nearest neighbour (KNN) to classify the gene expression into either cancer or non-cancer categories. Multiple metrics- sensitivity, accuracy, precision, and error rate are used to assess the performance of the models, to have a balanced illustration of a model’s performance. The DecTr outperforms the other two classifiers- SVM and KNN, across the metrics used in evaluating the performance of the models.

Thermal Performance of Vapour Compression Refrigeration System using Bimetallic Strontium Hexaluminate Nanorefrigerants

Sun, 29 Sep 2024 00:00:00 +0000

Nanoparticles are added to standard compressor lubricants to improve performance and reduce the energy consumption of vapour compression refrigeration systems (VCRS). This work evaluated the compatibility, viability, and utility of a bimetallic oxide strontium hexaluminate (SrAl12O19) nano-lubricant with nominal sizes of 20–40 nm by characterising and evaluating its thermophysical properties while assessing its effect on the performance
and energy consumption of existing, unmodified VCRS. Eco-friendly R600a were used as the system's refrigerant and the performance, energy consumption, and energy efficiency of VCRS were investigated by altering the concentration of SrAl12O19 (1%–20%) in the compressor lubricant. The results showed that as the temperature increases, the
viscosities significantly decreased and increased as the concentration of nanoparticles is increased. In contrast, the density and acidity of the nanolubricant increased as the volume concentration of nanoparticles increased. The addition of nanoparticles into the compressor oil enhanced the performance of the VCRS performance and reduced the energy
required to operate the system; however, these performance metrics decreased as the concentration of nanoparticles increased further. When the concentration of nanoparticles increased, exergy efficiency reached the maximum at 5% volume concentration.

Synthesis And Efficiency Evaluation of Chitosan-Alginate Hydrogel Polyelectrolyte Complex Filter for Treatment of Oil Spillage

Muhammad Baba Saje, T. O. Uthman, S. Surgun — Sun, 29 Sep 2024 00:00:00 +0000

Crude oil spills pose considerable environmental concerns thereby demanding the development of effective, sustainable, and eco-friendly remediation solutions. In this study, we synthesized and evaluated the efficiency of a biodegradable polymer filter using sodium alginate and chitosan for the treatment of oil spillage. The sodium alginate-chitosan hydrogel polyelectrolyte complex (SACHPC) filter was synthesized via dipping method using a layer-by-layer self-assembly technique integrated with cotton wool as the filter medium. The efficiency of the filter was evaluated based on flow rate of recovered oil and reusability of the filter. Simulated oil spillage of 0.5 M was subjected to filtration using the SACHPC filter. The flow rate was determined based on the time taken for 1 L of oil to pass through the filter. The SACHPC filter exhibited underwater superoleophobic behaviour with exceptional
filtration efficiency (> 98.3%) in acidic, neutral and alkaline conditions (pH 3, 7 and 11). The flow rate of the filter was 120 mL/min, while the third-use filter exhibited a decrease flow rate of 50 ml/min. Moreover, the filter achieved an impressive crude oil recovery rate of 85%. X-ray diffraction analysis confirmed crude oil adsorption in the presence of an amorphous phase while scanning electron microscopy analysis revealed the structural morphology of the
SACPHC filter. Overall, the SACHPC filter maintained high filtration efficiency thereby offering a sustainable strategy for oily wastewater purification and oil spill clean-up.

Discretization of Continuous Spaces using Barycentric Subdivision Method with Metric Space Constraints on the Nearest Neighbour Edges

Eliazar Elisha Audu, Akaa Eteng, Iyemeh Uchendu — Sun, 29 Sep 2024 00:00:00 +0000

The Rao-Wilton-Glisson (RWG) is a commonly used basis function in the numerical solution of the electric field integral equation (EFIE) using the Method of Moments (MoM) and Galerkin approach. This method relies on triangular patches to approximate the surface current. Traditionally, barycentric subdivision of a primary triangle into n-sub-triangles has been used with RWG basis function to solve the EFIE using MoM. This paper presents a method of approximating a surface using triangular patches by sub-dividing a primary triangle into (2n-1) subtriangles. which creates a denser mesh than the widely used nine (9) points quadrature method. The structure is approximated by small square patches, which are further sub-divided into two primary triangles. By applying barycentric sub-division, the primary triangles are decomposed into sub-triangles to create a mesh over the surface of the structure. Using graph theory, the triangular meshes are defined by the function, G (V, E), where V and E are the vertices and edges of the triangles in the mesh space, respectively. The connectivity matrix of shared edges is found by imposing a constraint on the edge length using metric spaces. This method approximates a square patch with 32 scalene triangles and shows that it can be used to reconstruct equilateral patches and doubly split double ring into mesh structures

Predictive Modelling and Functional Group of Clay Soil Treated with Steel Slag and Calcium Carbide Residue

Ogundare Damilola Ayodele, Oyetunde Oluwafemi Adeleke, Ayodeji Akinbuluma — Sun, 29 Sep 2024 00:00:00 +0000

Swell-shrink subgrade soils like clay are rich in sulphate and exhibit a high rate of volume change by swelling and shrinking during wet and dry cycles, respectively, leading to heaves, cracks, and differential settlement of pavement layers. The aim of this study was to establish predictive simulation models and functional group of clay soil treated with steel slag and calcium carbide residue. Physical and geotechnical tests (such as California bearing
ratio (CBR), compaction characteristics, and consistency parameters) were carried out on the unstabilised and stabilised clay soil. Using SPSS statistical software (version 23), multiple linear regression analysis was applied to the experimental data that relate CBR to compaction parameters and additive contents (steel slag and calcium carbideresidues). The generated models gave a reliable coefficient of determination, R2, of 0.990, and 0.998, which can be
used to successfully predict both unsoaked and soaked CBR of the stabilised clay soil, respectively. Fourier transform infrared spectroscopy (FTIR) analysis was examined on the clay soil, the additives, and the stabilised clay soils. The FTIR analysis showed weak peaks at 1115 and 1103 cm-1 for all the stabilised clay soils, and this revealed that both
additives drastically reduced the sulphate content in the clay soil, which consequently reduced the expansion, cracking, and disintegration rates. As a result, the clay soil's chemical bonding was enhanced through physico-chemical mechanisms.

Analysis of Impact Attenuation in 3D Printed Hip Protectors: A Support Vector Regression Approach

Sun, 29 Sep 2024 00:00:00 +0000

This study examined the impact attenuation of 3D printed hip protectors using thermoplastic
polyurethanes with different shore hardness values for preventing osteoporotic hip fractures. Rigorous testing at various energy levels were carried out to determine the protector’s abilities to attenuate impact. Subsequently, the prediction of impact attenuation capacity based on key design parameters was achieved from developed Support Vector Regression (SVR) model generated from the data of the impact attenuation capabilities. The key design
parameters were shell thickness, infill density and shore hardness. The results demonstrated a significant correlation between the impact attenuation ability and the infill density of the hip protectors with R2 of 91% for the training set and 99% for the test set. The generated RMSE values are 0.0012 and 0.0208, respectively. Remarkably, the SVR model exhibited excellent agreement with the experimental test results, affirming the efficacy of SVR in the design of hip protectors to enhance protective performance and cut the cost of experimentation.

A Hybrid Method for Identifying Critical Failure Modes in a Ball Mill

Sun, 29 Sep 2024 00:00:00 +0000

The reliability of a ball mill is crucial for the seamless operation of ore processing and other industrial plants, where unexpected equipment failure or downtime can result in significant financial losses and reduced production efficiency. Thus, this paper examined a hybrid method for identifying critical failure modes in a ball mill using the Failure Modes, Effects, and Criticality Analysis combined with the Complex Proportional Assessment with
Grey Numbers (COPRAS-G). The COPRAS-G method was employed to assess the criticality of various failure modes by determining the weights of significant failure modes in the bearing, gearbox, motor, and shaft components of a ball mill. It was observed that gear seal failure is the most critical failure with percentage contribution (Ni) of 100%, while bearing wear is the least critical failure with percentage contribution (Ni) of 62%. The result suggests that failure
modes of gear seal failure, gear pitting, motor bearing fatigue, and shaft fracture are regarded as the main contributors to failure with percentage contribution (Ni) of 100%, 97%, 87% and 83% respectively. Current ball mill maintenance includes corrective, preventive and predictive maintenance. For failure modes with high criticality, predictive maintenance was advised, while for moderate and low criticality, corrective and preventive maintenance were advised.

Optimization of Non-Thermal Treatment Methods and Extrusion Temperature on Quality of Rice Pasta Enriched with Cirina butyrospermi

Sun, 29 Sep 2024 00:00:00 +0000

Food security remains a critical global challenge, necessitating the exploration of sustainable and nutrient-rich food alternatives such as edible insects. This study investigated and optimized the effect of treatment methods (boiling, ultrasound, and a combined boiling and ultrasound treatment) on Cirina butyrospermi and extrusion temperature (70℃, 80℃ and 90℃) on some quality (crude protein, in-vitro protein digestibility, calcium, zinc, iron,
cooking time, water uptake ratio, lightness, and colour change) of rice pasta. The effect of treatment methods on Cirina butyrospermi powder and extrusion temperature on crude protein ranged from 15.85 to 18.27%, in-vitro protein digestibility (80.42-88.55%), calcium (48.53-56.25 mg/100g), zinc (2.42-3.56 mg/100g), iron (3.34-5.13 mg/100g), cooking time (8.50 - 19.50 min), water uptake ratio (1.15 - 1.90), lightness value (4.19 - 50.73) and colour change
(4.47 - 51.13), respectively. The combined boiling and ultrasound treatment on Cirina butyrospermi powder and higher extrusion temperature gave rice pasta the highest protein, calcium, zinc, and iron content while lower extrusion temperature along with boiling treatment, increased in-vitro protein digestibility. Optimum conditions for desirable
rice pasta were achieved at 80°C extrusion temperature with combined boiling and ultrasound treatment. Conclusively, exploring edible insects like Cirina butyrospermi under an accurate treatment method and extrusion condition could aid in the development of nutrient-rich rice pasta.

Power Quality disturbance monitoring in PV integrated power system with mode decomposition and ensemble extreme learning machine

Anasuya Roy Choudhury, Ranjan Kumar Mallick, Pravati Nayak, Ramachandra Agrawal — Sun, 29 Sep 2024 00:00:00 +0000

Power quality disturbance (PQD) monitoring has become an important issue in modern power system due to integration of several renewable energy sources such as photovoltaic (PV), wind energy system (WES), Fuel cells etc. This research presents a mode decomposed based ensemble extreme learning machine (EELM) to recognise and classify the PQD events with higher accuracy in terms of rapid learning speed and smaller computational burden in a complete PV based power system, The PQD signals are decomposed using a variational mode decomposition (VMD) to obtain effective band limited intrinsic mode functions (BIIMF) which leads to compute robust features and improves classification accuracy. For Power Quality Disturbances detection and classification, an ensemble extreme learning machine is suggested since an ensemble outperforms any single contributing model in terms of performance and prediction. The proposed VMD-EELM approach is validated in a modified IEEE 13 Bus system integrating PV with ten types of PQD. It is being demonstrated that the suggested method can reliably identify and track PQD occurrences both with and without noise.