Nigerian Journal of Technological Development

Fast indirect GMPPT method for PV systems under uniform and partial shading conditions

K. Ameur — 2024-03-11

Under Partial Shading Conditions (PSCs), conventional MPPT techniques fail to locate the Global Maximum Power Point (GMPP) for PV generators, and when PSCs change suddenly and repetitively, several GMPP tracking techniques takes time to find or miss the target. To overcome these shortcomings, this paper proposes a new and fast technique that can identify and catch very quickly the GMPP. Due to the use of a PID controller, the PV system is improved in terms of response time and becomes very fast. On the other hand, the proposed algorithm is developed upon other known algorithms and enhanced in order to identify the occurrence of PSCs and to find the GMPP. The measured points during identification and searching process are reduced which increases the power efficiency of the PV system. The time required for the algorithm to catch the GMPP is minimized by 25% compared with other works. To examine the performance of the system a hard scenario, that contains several uniform and partial shading conditions, is used. The simulation is implemented in Matlab/Simulink. The obtained results show clearly the advantage of the proposed technique over others.

Multi optimization in slot milling of CFRP composites through grey relational- based Taguchi analysis

A. Bahei El-Deen — 2024-03-11

Carbon fiber-reinforced plastic (CFRP) composite materials are challenging to machine due to their anisotropy and heterogeneity. Thus, the experimental study of milling CFRP composite material is very crucial. In the current study, based on Taguchi’s L9 orthogonal array, slot milling experiments were performed on CFRP composite samples to make a decision on a parametric optimization of multiple responses such as material removal rate (MRR), delamination factor (F_d) and surface roughness (Ra) using grey relational-based Taguchi analysis. The selected milling parameters are cutting speed (A), feed (B), and depth of cut (C). Based on Grey Relational Grade (GRG), Analysis of Variance (ANOVA) was used to determine the parameters' significant contributions and the parameters' optimal levels. The results showed, with a 95% confidence level, that all of the chosen cutting parameters have a substantial impact on all of the measured responses. Based on a confirmatory test performed under ideal milling conditions, MRR has been increased with an improvement of 31.25 %, F_d has been decreased with an improvement of 1.66% and Ra has been decreased with an improvement of 28.3%. These improvements in all measured responses are equivalent to an improvement of GRG by 3%.

Comparative analysis of dual stator machines

C.C. Awah — 2024-03-11

Electromagnetic performance of three different types of double stator permanent magnet machine is analyzed and compared in this study. The analyzed machines in this study are Machine 1, Machine 2 and Machine 3. These machines are designated as: M 1, M 2 and M 3, respectively. The studies are implemented using two-dimensional and three-dimensional finite element analysis (2D-FEA and 3D-FEA) methods. The predicted performance indices are: total harmonic distortion of the voltage, torque ripple, cogging torque, winding inductances, output torque and unbalanced magnetic force (UMF). The studies show that the investigated machine types have negligible reluctance torque and thus, similar axes inductance values. Therefore, the machines’ bulk torque components are contributed mainly by the magnets while the armature excitation sources yield lesser torque components. Amongst the compared machines, M 3 type has an outstanding performance in almost all the performance metrics, compared to M 2 and M 1 types. Nevertheless, M 1 machine type has some good attributes, particularly, with respect to its high output torque per applied magnet volume, in addition to its widest operating speed range ability. Low-speed high-torque applications are most suitable for the investigated machines in practice.

Recent advances in smart manufacturing: a case study of small, medium, and micro enterprises (SMME)

K. A. Bello — 2024-03-11

The digital revolution is the future pathway to experiencing business evolution. It will be difficult for any organization to remain in business without deploying fourth industry revolution (4IR) techniques. This review guides prospective business owners on the need to embrace Smart Manufacturing (SM) timely and appropriate to enhance their business performance indicators. Many manufacturing companies are facing challenges in adopting SM tools in their organization due to a lack of essential resources despite the benefits associated with SM. Therefore, this study systematically reviewed the criteria evaluation techniques in implementing digital factories. This work has analysed small, medium, and micro enterprises (SMME), with the view to enumerate the appropriate criteria to determine the level of digital technology tools adoption framework. It also highlights how to compensate for the inadequate technical and financial resources in SM. The guidelines for SM implementation adoption in SMME is a research gap that was missing in previous studies. SM benefits, challenges, applications, significance, impact, and future perspectives, are discussed. Evaluation Criteria for SM Adoption practices were also expounded. The framework used in this study will help SMME owners to adopt SM.

Biomechanical and physical properties selection of Ti-Ha-CaCO₃ biocomposite prostheses for replacement of bone atrophy

H. K. Ibrahim — 2024-03-11

Traditional prosthetic materials often lack the desired properties to mimic the mechanical behaviour of natural bone, leading to complications and reduced implant longevity. This study aims to conduct a biomechanical and physical properties selection analysis for biocomposite prostheses' suitable for replacing bone atrophy. This involves evaluating the mechanical properties of developed biocomposites with different structures (dense, porous and gradient) to ensure compatibility with the mechanical properties of bone. The radar chart was adopted to compare and evaluate the mechanical strength of various biocomposite implants and identify the most suitable prosthesis for load-bearing bone replacement. The study utilises powder metallurgy, scanning electron microscopy (SEM), and ImageJ software to produce and characterise the pore size distribution of the biocomposites, respectively. The findings of this study revealed the gradient and porous biocomposites exhibited desired mechanical properties with porosity of 20.67 and 27.72 % pore size up to 134 and 256 μm, compressive strength of 174 and 149.29 MPa and compressive modulus of 30.42 and 28.3 GPa respectively. The SEM analysis, coupled with pore size distribution and porosity percentage measurements, offers valuable information for designing and fabricating biomaterials with enhanced properties. The gradient biocomposite was identified to be the best sample for load-bearing bone replacements by the selection analysis because of its high compressive strength and low modulus, which is within the established cortical bone mechanical properties.

Hydrothermic reduction of rutile-ilmenite mineral producing an oxyhydride η-Ti₂FeO_0.2H_2.8: Towards in-situ hydrogen production and storage

I. A. Mohammed — 2024-03-11

As an alternative to the physical storage of hydrogen as compressed gas or liquid hydrogen requiring high-pressure tanks and cryogenic temperatures, the material-based storage of hydrogen in solids involves hydrogen uptake and release from the surface of adsorbents or within interstitials of hydrides. We report a hydrothermic reduction of rutile-ilmenite mineral into hydrogen-rich fibrous products, η-Ti₂FeO_0.2H_2.8, in an ethanol-water system at 120°C for 4 hrs. As part of a project to generate hydrogen from water-ethanol system using advanced catalysts containing graphene oxide (GO) as carbon source, a system of 62.5 μg graphene oxide per g of rutile-ilmenite mineral was employed in a concentration of 50 mg/mL of ethanol-water solution. As well as in the original mineral, XRD of thermal annealed mineral between 500 and 800°C showed no hydride or phase change in rutile-ilmenite. With hydrothermal treatment of GO/rutile-ilmenite (50 mg/mL) in ethanol-water (1:1 v/v) at 120°C, a hydrogen-rich ferrotitanium hydride phase was formed, and there was a change in morphology from plate-like and granular particles into fibrous structures. Like the release of hydrogen by its ‘carriers’ (e.g., CaH₂, NH₄BH₄, NaBH₄, NH₃, formic acid), it is anticipated that hydrogen was generated from the ethanol-water system in-situ, which reduced the rutile-ilmenite mineral into a hydride. EDX results showed that the reduction affected specifically the oxides of Fe and aluminosilicates in the mineral. The study demonstrated a possibility of in-situ hydrogen generation and storage via low-temperature graphene oxide hydrothermic reduction of rutile-ilmenite mineral in an ethanol-water system.

Analysis of 3D printing materials as potential Radiological Phantoms of Lung Organs for medical imaging purposes

M. Yunianto — 2024-03-11

Research has been conducted to analyze and characterize ten 3D printing materials as potential Radiological Phantoms of Lung Organs. Eight filaments of PLA, ABS, HIPS, Carbon, Nylon, TPU, PETG, and Wood were printed using an FDM type 3D printer, and two resins, PLA resin and Water washable resin, were printed using an SLA type 3D printer. The phantoms were printed with thickness variations of 3 mm, 6 mm, and 9 mm. 8 parameters were used to obtain the best material, namely material density, CT number, electron density (Ne), effective electron density (EDG), electron density per volume (EDV), effective atomic number (Zeff), material constituent elements, and elastic Modulus. Based on comparing the values of 8 parameters, the most potential to be used as phantom material for lung organs is PLA.

Automated identification of heart arrhythmias through HRV analysis and machine learning

S. K. Lawal — 2024-03-11

Sudden cardiac death and arrhythmia are responsible for about 15-20% of cardiovascular disease incidences. Conventionally, the prediction and diagnosis of cardiovascular disorders (CVDs) have been mainly through the evaluation of ECG patterns by cardiologists. To improve the accuracy of and automate this process, and facilitate early detection, Heart Rate Variability (HRV) analysis has been promoted as a diagnostic and predictive tool for CVDs. In the present study, a machine learning model capable of detecting the presence of arrhythmia, using HRV indices obtained from ECG signals was built. Unlike similar works in the literature, this study deployed the developed model on Raspberry Pi with Streamlit software. Two ECG datasets from the Physionet database, one with arrhythmia patients (48 half-hour recordings) and another with healthy individuals (18 24-hour recordings), were employed. An ensemble of seven different machine learning models was used on the two sets of datasets to classify ECG recordings into Arrhythmia and Normal Sinus Rhythm (NSR). The best models were able to predict the presence of Arrhythmia in a 3-minute recording with an accuracy of 95.96%, and in a 10-minute recording with an accuracy of 96.20%. These performance measures were calculated using test dataset. The Random Forest models also had the highest precision, AUC, (Area under the Curve) recall, and F1 scores compared to the other models tested. The highest performing model (i.e., Random Forest Model) was then deployed onto a Raspberry Pi with Streamlit as the software interface for usability. This was done to facilitate a smooth user experience for faster and seamless diagnoses for cardiologists.

Consensus issues in multi-agent-based distributed control with communication link impairments

O. S. Akinwale — 2024-03-11

In multi-agent systems, achieving consensus among autonomous agents is a fundamental problem with wide-ranging applications, from autonomous robotics to distributed sensor networks. However, the real-world deployment of such systems often involves communication links prone to impairments, including packet loss, delays, and network congestion. These communication challenges present formidable obstacles to achieving consensus reliably and efficiently. In this paper, consensus protocols were introduced for network with and without communication impairments and convergence analysis were provided in all the cases. The intricate dynamics of consensus issues in multi-agent-based distributed control under the influence of communication link impairments, connectivity and consensus protocol were established. Undirected communication graphs used to model the topology for agents’ connectivity is significant to addressing consensus issues of communicating agents. The paper also discusses the tradeoffs and design considerations in developing consensus strategies resilient to communication failures while optimizing performance. Simulation results show that an isolated agent in a network can achieve consensus only when there is a reference value. It was also established that communication impairments significantly degrade the performance of distributed agents in a network.

A review on CO₂ capture over novel adsorbents: Progress in robust zeolite adsorbent development

H. U. Hambali — 2024-03-11

Indubitably, the combustion of fossils fuels has really hampered the preservation of the environment as it raises the content of CO₂ in the atmosphere which consequentially results in global warming. Adsorption process remains the popular technique owing to its cost-effectiveness, faster reaction rates and flexible design. This review detailed the research progress in preparation of modified zeolite-based and novel adsorbents towards enhanced CO₂ capture. In addition, the review presents an overview on available techniques of capturing CO₂ and mechanism of reaction. Large surface area, distinctive mechanical characteristics and uniform dispersion of the exchangeable cations in the porous framework is prerequisite for high adsorption capacity and stability over zeolite materials. Novel nanostructured and polymeric zeolite composite materials seem promising because they offer solutions to energy-related problems while also contributing to environmental preservation. It is anticipated that this review could offer a conclusive roadmap in the pursuit of a cost-effective, industrially potent adsorbent suited for enhance CO₂ capture.

Detection and confirmation of electricity thefts in Advanced Metering Infrastructure by Long Short-Term Memory and fuzzy inference system models

A. O. Otuoze — 2024-03-11

The successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.

Enhancing adsorption capacity of a kaolinite mineral through acid activation and manual blending with a 2:1 clay

T. O. Abu — 2024-03-11

The efficiencies of raw and modified kaolinite mineral in removing selected heavy metal ions from their respective aqueous solutions were investigated. The mineral was modified through two different methods; i) activation with HNO₃, H₂SO₄, H₃PO₄, CH₃COOH and C₂H₂O₄ acids to form NK, SK, PK, AK and OK acid activated clays respectively and ii) preparations of 3:1 and 1:1 Kaolinite: Bentonite blends to form UBK and EBK composites respectively through manual blending. The adsorbents were characterized by X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Fourier Transform Infra-Red Spectroscopy (FTIR) and Brunauer Emmett Teller (BET) analysis for surface area determination. The surface area increased in some of the modified clays from 114.9457 m²/g (RK) to 288.685 m²/g (EBK), 205.92 m²/g (UBK), 162.227 m²/g (NK), 151.335 m²/g (SK), and 115.837 m²/g (OK) but reduced to 113.872 m²/g (PK) and 112.865 m²/g (AK) after modification. Adsorption studies were subsequently conducted out to remove Pb²⁺, Cd²⁺ and Ni²⁺ ions from synthetic solutions. Pb²⁺ was found to be most removed (383.5 mg g^-1 (RK), 591.13 mg g^-1 (EBK), 576.61 mg g^-1 (UBK), 475 mg g^-1 (NK), 450 mg g^-1 (SK), and 425 mg g^-1 (PK), 375 mg g^-1 (OK) and 375 mg g^-1 (AK)) with highest removals on the composites.