Food packaging involves the process of enclosing food to keep it safe from outside elements that could contaminate, harm, or decompose it while it is being transported, stored, or sold. Selection of packaging materials used play a critical role in ensuring food safety and extending shelf life. This makes the exploration of sustainable alternatives, such as Natural Fibre Reinforced Green Composites (NFRGCs), essential. In addition to packaging sustainability, NFRGCs have gained prominence in several vital applications in industries, including semi-structural, structural, automotive, aerospace, medical etc. because of their long-term sustainability. This review work focuses on the present and potential applications of green composite films intercalated with turmeric oil. It highlights the significance of the materials now in use as well as their limitations. The necessity of sustainable packaging stems from customer demand, legal pressures, and environmental concerns. Green composites are being investigated as an alternative to synthetic polymer composites due to their good mechanical properties, affordability, biodegradability, improved life cycle, and eco-friendliness. Turmeric oil-intercalated nanocomposites offer unique benefits for food packaging, including improved mechanical and barrier properties, increased antimicrobial and antioxidant properties, and potential environmental benefits. Despite challenges like cost and scalability, they represent a new frontier in packaging innovation.
Solar Photovoltaic System (SPV) is a rapidly expanding renewable energy source integrated into national power grids and off-grid systems worldwide. Therefore, the effective management of power systems and the energy industry needs accurate solar energy forecasting, ranging from short-term predictions of a few seconds to longer-term forecasts up to one week in advance. Hence it is well recognized that the presence of dust particles on the surface of photovoltaic (PV) modules substantially influences their overall performance. Subsequently, the resultant decrease in energy generation significantly impacts the financial earnings. A range of methodologies have been used to address the limitations associated with solar radiation forecasting. The use of machine learning (ML) algorithms provides a range of techniques for predicting solar radiation. Additionally, integrating artificial intelligence (AI) and ML may collectively enhance the optimization of cleaning intervals for photovoltaic (PV) modules. In the same manner, the development of a sun tracking algorithm aims to accurately determine the location of the sun in order to optimize the reception of solar energy. This article aims to critically review the current research on maintenance and tracking strategies for PV panels while presenting a novel application of AI and ML algorithms. Finally, this review presents a comprehensive analysis comparing traditional approaches with AI techniques to demonstrate the significant mpact of AI algorithms in PV systems.
The escalating threat of global warming necessitates a shift towards clean energy production, with renewable energy sources emerging as a promising solution for sustainable electricity generation. However, the inherent intermittency of renewables requires hybrid systems to maintain reliable electricity. This review explores the optimization of dual-mode renewable hybrid power systems, which operate in both grid-connected and autonomous modes, to address power stability challenges. By analyzing state-of-the-art methodologies, including advancements in control algorithms, architecture sizing, and energy management strategies, this work identifies methods for enhancing system efficiency and cost-effectiveness. Key findings demonstrate that optimized dual-mode systems improve power consistency and operational resilience. The review concludes by outlining a roadmap for future research, highlighting innovations in integration techniques, environmental assessments, and regulatory collaboration as crucial steps toward maximizing the impact of dual-mode systems in achieving global energy sustainability goals.
This paper established a robust theoretical framework and applied fuzzy random and probabilistic theories to evaluate the durability and strength of reinforced concrete bridge structures subject to corrosion. The study systematically predicts moment capacity degradation, failure probability, and service life, while accounting for uncertainties in input parameters through Monte Carlo simulation. The MATLAB programming tool was used to calculate and analyze the bridge structures considering the uncertainty of input parameters and Monter Carlo simulation. A targeted application focused on estimating the service life of an aging bridge affected by chloride intrusion yielded noteworthy findings. Notably, the predicted initial corrosion times from both fuzzy and non-fuzzy analyses were found to be comparable, at approximately 29.96 years and 29.94 years, respectively. Additionally, the analysis indicated that the probability of failure for reinforced concrete bridge structures effectively doubles after 25 years. These results underscore the robustness of the proposed model, emphasising that the incorporation of comprehensive empirical data on the input parameters used to calculate durability and resistance, particularly from field experiments, significantly enhances the reliability and accuracy of lifespan and resistance predictions for reinforced concrete structures in practical applications.
This paper introduces a testbed for a Group Acknowledgement Circular Shift (GACS) MAC protocol model designed to balance current consumption in LoRa-based Wireless Sensor Networks. The model leverages a time-slotted approach to allocate transmission times for end devices, using ATmega328p microcontrollers and Ebyte 22 LoRa Modules. The testbed includes a gateway, an application server, and initially four, then fifteen, end devices. Current monitoring circuits were attached to certain end devices to measure current usage during both GACS and Group Acknowledgement (GA) without Circular Shift (CS) models. In a network of four end devices, the GACS model extended battery life uniformly to 463 days with a 3000 mAh battery, while inconsistent battery life was observed in GA without CS. Similarly, the GACS model enabled 434 days of battery life across 15 devices, promoting uniform battery longevity. Results show that the GACS model enhances current consumption fairness, reduces collisions, and improves packet delivery to 99.81%.
Synthesis of a catalyst (AKC-CE) from activated kaolinite clay (AKC) and chicken eggshells (CE) for biodiesel production using waste-ram-fat (WRF) was undertaken in this study. Characterisation studies of AKC, CE, and AKC-CE samples were investigated using various equipment such as X-ray fluorescence (XRF), X-ray diffraction (XRD), and High-resolution scanning electron microscope (HR-SEM) incorporated with Energy-dispersive X-ray spectroscopy (EDAX). The catalyst produced was tested for biodiesel production using WRF in a one-factor-at-a-time study of the process parameters. The XRD analysis showed that the main components of Al2(Si2O5)(OH)4, SiO2, and CaO are present in the AKC-CE catalyst. These compounds are commonly used as catalysts for biodiesel production.The AKC-CE catalyst’s morphology also demonstrated a significant cavity containing irregular crystals, with numerous white flaky crystals. These characteristics show that the catalyst would be efficient for WRF transesterification for biodiesel production. The highest biodiesel yield of 81.33% was obtained at process parameters of MeOH: WRF molar ratio of 18:1, reaction time of 4 h, catalyst loading of 10% (w/w), and temperature of 70oC. The biodiesel produced with the developed catalyst has properties within the ASTM standard. The AKC-CE catalyst is effective for producing biodiesel from WRF. Using WRF and chicken eggshells as materials for sustainable energy may not pose more threat to food availability in the biodiesel production that meets the ASTM standard as established in the Nigerian Biofuel Policy. Therefore, an increase in the use of non-edible feedstock for biofuel production is recommended.
Metal Injection Moulding (MIM) is an effective near-net-shape process for producing small, intricate parts in high volumes. Many optimizations of the MIM process parameter have been published without using any of the statistical based Design of Experiment (DOE) methodology and ends with unsatisfactory results in a wide range of experimental settings. This paper focuses on analysing the optimization of injection moulding process parameters for titanium alloy Ti-6Al-4V green parts using response surface methodology (RSM) to determine significant parameters. The feedstock is prepared by mixing Ti-6Al-4V powder with palm stearin (PS) and polyethylene (PE) binders in three different powder loadings: 63 vol%, 65 vol%, and 67 vol%. RSM is then used to create a Design of Experiment (DOE) to optimize parameters, including A: injection pressure, B: injection temperature, C: powder loading, D: mould temperature, and E: holding pressure. Analysis of variance (ANOVA) was performed to identify the significant parameters influencing defects in Ti-6Al-4V green parts. The study successfully identified the optimal conditions for producing Ti-6Al-4V components using PS and PE binders: A: 443.139 bars, B: 142.11 ºC, C: 65.38 vol.%, D: 44.694 ºC, and E: 629.561 bar. Scanning electron microscopy (SEM) from optimised of green part shows that the primary backbone polymer PE holds the green part and generates wetting for the feedstock and will ease moulding and shaping while the secondary backbone PS will act as a soft polymer that can provide the moisture to the feedstock.
The exceptional properties of Ag-doped ZnO nanoparticles (NPs) make them highly promising for applications in environmental remediation and optoelectronics. However, the limited tunability of ZnO’s bandgap and its suboptimal performance in doping applications necessitates material modifications. In this study, a hydrothermal method was used to synthesize ZnO and Ag-doped ZnO (NPs). The study optimized the silver molar concentration to between 0.01 to 0.03 mol and characterized the nanoparticles for their optical, structural, and surface micrograph using different characterization techniques. As the wavelength increases from 300 to 1100 nm, nanoparticles show a decrease in absorbance. In both spectral regions, ZnO doped with 0.1 mol of Ag exhibited the highest absorbance, while ZnO doped with 0.2 mol of Ag showed the lowest absorbance. The energy bandgap of ZnO was measured at 2.19 eV, which deviates from the theoretical bandgap of ZnO at 3.3 eV. Introducing silver into the ZnO leads to changes in the band structure because of the creation of additional energy levels while the introduction of silver doping into ZnO lattice leads to a decrease in the bandgap energy to 1.50, 1.39, and 1.29 eV. These enabled electron transitions at lower energy levels because of the formation of defect states or interaction with ZnO. The XRD patterns of zinc oxide and Agdoped ZnO NPs reflect their hexagonal structure. The material showed a diffraction peak at (101) when measured at a 2θ angle of 44.863o. The hexagonal phase displayed distinct diffraction peaks at specific 2θ angles. Within the FTIR spectrum, Zn-O stretching vibrations are typically shown by distinct absorption bands, usually ranging from 400 to 750 cm-1. Introducing silver doping results in the emergence of new vibrational modes and a shift in existing peaks which indicate changes in the local environment around the ZnO lattice. These findings demonstrate that silver doping significantly enhances the optical and structural properties of ZnO an indication of its suitability for applications in advanced technologies requiring tunable bandgap and improved performance.
Twin submerged floating tubes (Twin-SFT) are widely found in structural applications, e.g. underwater cables, piping systems, even recently a lot of research has been directed towards transport tunnels. Currently, research on Twin-SFT is still limited and far behind when compared to single tubes. This research aims to observe the drag force on Twin-SFT. The study investigates the effect of the axis distance between the tubes, the diameter, and the depth of the installation of the double-tube structure on the drag force. The diameter variations of the tubes are 2 in (50.8 mm), 2 ½ in (63.5 mm) and 3 in (76.2 mm), each with a length of 600 mm. The tubes were mounted parallel to the centre distance between the upstream and downstream cylinders at x/L = 1⁄4, x/L = 1⁄2, x/L = 3⁄4 and x/L = 1, where x/L is the ratio of distance to wavelength (L). To assess the influence of the installation depth, the tubes were mounted at depths of 100 mm and 200 mm. The results showed that the drag forces on both cylinders were smallest at a distance of x/L = 1⁄4. Additionally, as the tube’s depth increases, the drag force decreases, while larger tube diameters result in greater drag force.
Corrosion prevention, enhancement of mechanical properties and aesthetic improvement are key motivators for the use of protective coatings in engineering applications. This study investigates the surface characteristics of electrodeposited Cobalt-Nickel-Iron (CoNiFe) nanoparticle coatings on mild steel substrates to assess cobalt alloy potential for improved performance and durability. Through the usage of electrodeposition method, coatings were applied at varying deposition times (15, 30, and 45 minutes), with electrolyte temperatures maintained at constant 50 ± 3 °C and current levels set at 1.0, 1.5, and 3.0 A respectively. Key properties evaluated include surface roughness, hardness, coating thickness, and breaking energy. Results showed that coating thickness ranged from 14.23 μm to 40.80 μm, with the thickest coating recorded at 45 minutes’ deposition time. Hardness value also increased at longer deposition durations, with the highest achieved value of 503.12 HV at 45 minutes’ deposition time. Surface roughness ranged from 1.4210 μm (30 minutes, 1.0 A) to 8.1085 μm (45 minutes, 3.0 A), indicating significant influence of deposition conditions. Additionally, the coating had a maximum breaking energy of 74 Joules at high-temperature quenching conditions (30 minutes). These findings suggest that CoNiFe coatings applied via controlled electrodeposition process can significantly improve the mechanical resilience and lifespan of mild steel, offering promising applications for advanced engineering environments.
This work compared the performance of two Simultaneous Localisation and Mapping (SLAM) algorithms, Hector SLAM and Gmapping, for self-navigation of a mobile robot in a small, slippery surface and controlled environment. The experiment utilised the Bveeta Mini mobile robot within a tiled corridor area. The primary objective was to evaluate and compare the accuracy of these algorithms in self-navigating the robot using acquired robot positions in 2D coordinates. The experiment involved manual mapping of the environment using both Gmapping and Hector SLAM, followed by autonomous navigation tasks with each algorithm. Performance was assessed by comparing the absolute error, absolute relative error, and percentage error between the robot's position obtained from the manual map and its position during autonomous navigation provided by the SLAM algorithms. It was found that the Hector SLAM achieved higher accuracy in all navigation paths than Gmapping. Gmapping suffered from significant errors, particularly in the robot's initial position, likely due to its reliance on odometry data, which was highly susceptible to errors from the slippery surface in the experimental area. In conclusion, both algorithms can be integrated with other advanced SLAM techniques to improve the accuracy of the generated map and robot position.
High Voltage (HV) cables are important components of modern power transmission and distribution networks. However, the reliability of such networks depends, to a large extent, on the health condition of the cables. The presence of voids within the insulation materials which could arise from overstressing, manufacturing defects as well as poor workmanship enhances the electric field within and around the defect which can lead to the occurrence of partial discharges (PDs). Furthermore, severe PD activity can deteriorate the cable dielectric material and cause accelerated asset ageing. Also, experimental monitoring of cable networks is expensive and time-consuming. Hence, in this research, a three-dimensional (3D) finite element model (FEM) of a crosslinked polyethylene (XLPE) cable was simulated in COMSOL Multiphysics software environment to analyse the behaviour of electric field in healthy and defective cable under different cavity sizes and applied voltages. The electric field magnitude at the centre of a 1.0 mm air void was recorded to be 2.539 kV/mm, representing a 24.07% increase over that of the corresponding healthy cable, 2.9539 kV/mm. PD activity was also studied using COMSOL Multiphysics software alongside MATLAB software environment by adopting free parameters corresponding to three ageing phases of the cable obtained from experiments for 50 cycles of the applied voltage. The results show that PD repetition frequency increases as the cable ages, resulting in 9.82, 15.82, and 17.20 PDs per cycle for ageing stages 1, 2, and 3 respectively.
This study aimed to formulate a film using quaternized starch (QSs), polyvinyl alcohol (PVA), and with tetraethyl orthosilicate (TEOS) for potential food packaging. Corn starch as a renewable resource could ensure sustainability and less negative environmental impact when compared to the use of petrochemical-based materials. The starch was quaternized using hexadecyltrimethylammonium bromide (CTAB) to improve its solubility and solve the retrogradation problem. and the loadings of TEOS were varied at 0-5 % wt./wt. polymer. The formulated films were characterized in terms of functional group, mechanical properties, and water uptake. The FTIR analysis revealed that the quaternization process successfully modified the starch, and the films containing TEOS exhibited new vibrational modes associated with the asymmetric and symmetric Si-O-Si stretching, confirming the effective incorporation of the silica network. Film F2 with 1 wt.% TEOS exhibited good mechanical properties with moderate tensile strength (0.870 MPa), high percentage elongation (301%), and low rigidity represented through Young’s modulus (0.814 MPa). The water uptake analysis revealed that increasing the TEOS content significantly improved the water resistance of the hybrid films (10 - 68%) compared to the films without TEOS (99%). The findings of this study contribute to the development of sustainable and eco-friendly packaging materials.
Evaluating students through examination scripts in educational environments is crucial, particularly with 'Mastery Feedback' from educators, enhancing student understanding and self-regulation. However, it has remained a hectic exercise requiring some innovative solutions. This study proposes integrating robotics to automate recording and collating marked scripts to reduce the burden on lecturers and improve productivity. Key objectives include developing a data extraction pipeline using methods like Oriented FAST and Rotated BRIEF (O.R.B.) for image alignment and adaptive thresholding for lighting variations. Additionally, a character recognition model using a Single Input Convolutional Neural Network (SICNN) was designed with three preprocessing techniques—binarisation, thinning, and gradient magnitude calculation— tailored to different image requirements. Training on the 'EMNIST by_merge' dataset showed varied validation accuracies, with the gradient input SICNN model achieving the highest at 89.24% overall and the binary input SICNN model excelling with 99.39% on custom scripts. This approach aims to enhance educational administrative processes and efficiency and thus achieve sustainable education.
The adoption of renewable energy resources for electricity generation usually results in the generation of excessive electrical energy. Efficient utilization of this surplus electrical energy after the battery has been fully charged when in use has the potential to minimize the unit cost of energy generation by these hybrid renewable energy systems. This work presents an optimal sizing methodology for a stand-alone Small Hydro-Solar Photovoltaic (PV)-Battery-Flywheel Energy Storage (FESS) System to electrify three off-grid rural areas: Sangotayo, Budo Umoru, and Idi-Isin in Kwara State, Nigeria. A hybrid optimization model for electric renewable (HOMER) was used to determine the optimum hybrid system configuration with the least levelised cost of energy (LCOE). Interestingly, the optimal system configuration modeled by HOMER, tagged Case 1 is a Small Hydropower-Solar PV-FESS-Converter with Net Present Cost (NPC) of $524, 940 (N 787, 410, 000 at N 1500 to 1USD), LCOE of 0.23$/kWh (N 345), and initial capital cost of $494, 752 (N 742, 128, 000) and composed of 273 kW of CS6U-330P solar PV, 45 pieces of fly100 FESS, 230 kW of Natel49 small hydro generator and 144 kW of PrinDRI100 DC/AC bi-directional inverter.
Encouraging clean and hygienic public areas is made more difficult by urbanization and growing population density. Current cleaning techniques can sometimes be ineffective, not environmentally friendly, unsustainable to handle different variety of debris and mostly expensive as they are imported into the country. This study presents the design and construction of an innovative mechanical sweeper from locally sourced materials to handle street and walkway cleaning challenges. By utilizing simple technique and employing locally available materials, the sweeper collects debris and eliminates negative effects of gasoline powered sweeper on the environment. Sturdy features include a mop function for thorough particle removal, an adjustable lever mechanism for agility, and a flexible brush configuration for cleaning various variety of surfaces. Testing the sweeper in different conditions on different types of trash, confirms the sweeper’s ability to clean adequately as the designed sweeper with a single 24v battery and 0.5 hp electric motor gave a cleaning efficiency above 58%. The simulation and analysis of the sweeper frame using SOLIDWORKS 2018 gave a maximum displacement of 1.621x10-1 mm, maximum strain of 2.415x10-5, maximum stress of 9.167x106 N/m2 and factor of safety of 3. The results of the analysis suggest that the sweeper has been well-designed and can withstand the loading conditions it has been designed for. The application of the sweeper can ensure notable gains in environmental sustainability, operational effectiveness, and cleanliness
Consumptions of contagious metals particularly from mine used water usually result in grievous health diseases and loss of aquatic lives. This research concentrated on elimination of specific contagious metals (Co3+, Fe2+, and Pb2+) from mine used water using Carica Papaya Trunk Enhanced Charcoal produced with HCl (CPTEC – HCl). The contagious metals’ concentrations of the mine used water, predominant functional classes and surface structure of the produced CPTEC-HCl were ascertained utilising some analytical techniques. The adsorption (batches) and desorption experiments applying six parameters were carried out. The suitability of the adsorption facts generated was conducted using adsorption isotherms, kinetics, and thermodynamics through linear and non-linear regressions with two error functions. The contagious metals’ original values were higher than the acceptable requirements. After treatment, CPTEC-HCl eliminated the contagious metals completely (100%) using 0.516 g of the adsorbent with 0.028 mm granule-size at 146.576 rpm, 51.718 minutes, pH of 6.969 at 30 °C. The processes were majorly chemisorptions, largely followed Freundlich, Dubinin-Radushkevich isotherms, Pseudo-second-order kinetic models and highly influenced by the adsorption parameters. The adsorption processes were favourable, feasible, and natural. CPTECHCl is an excellent alternative adsorbent for commercial enhanced charcoal in contagious metals’ elimination from real-world effluent especially mined used water.
Whenever DC motor operates at varying speeds and is expected to rotate in two directions with high precision, it becomes essential that uncertainties in the form of external disturbances and load variations in the system be considered. In such systems, the use of fixed-gain Proportional Integral Derivative (PID) controller alone is suboptimal as the controller lacks the ability to handle the uncertainties which causes the output response of the motor to have excessive overshoot, poor disturbance rejection and poor command-following. This paper presents a particle swarm-optimized model reference adaptive PID controller (PSO MRAC-PID) for precise DC motor speed control. The proposed controller can set the speed of a DC motor to track a predefined reference model and adapt to the effect of uncertainties and external disturbances, using a combination of two basic techniques namely, the Massachusetts Institute of Technology (MIT) rule and particle swarm optimization (PSO). The MIT rule was utilised for the design of the adaptation mechanism and PSO was implemented for optimization of the adaptation gains of the MRAC-PID controller to enhance its overall performance. Comparative analyses with fixed gain PSO-PID and conventional MRAC-PID controllers indicate that the proposed PSO MRAC-PID achieves a 66.7% improvement in settling time, 0.4% improvement in overshoot, 35.3% reduction in control effort and a general improvement in command tracking and disturbance rejection. This outcome suggests that the proposed POS MRAC-PID controller can better suit realworld applications where precision is critical such as motor drives in conveyor belts, crane systems, among others. It also achieves control with greater energy savings as compared to the traditional methods – a crucial feature for greener and cleaner operations.
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.
