Nnamdi Azikiwe University Journals
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Effects of process parameters on methyl ester yield from fruit-peel ash catalyzed lard oil methanolysis
No full textThe viability of utilizing ash from fruit-peel of banana, plantain, and hybrid banana-plantain as low-cost hetero-basic solid catalysts in waste-lard-oil (WLO) methanolysis in lard-oil-methyl-ester (LOME) production was explored together with the effects of process parameters on LOME conversion. The catalyst properties were ascertained using SEM-EDX, XRD, and BET. The effects of temperature, catalyst-amount, time, and methanol-to-WLO molar proportion on LOME yield utilizing the various catalysts were investigated. Each of the catalysts exhibited high catalytic ability in WLO methanolysis resulting in over 95% LOME yield at the optimal settings of 60oC, 2.5 wt. % catalyst amount, and 10.5:1 molar proportion at a reduced period of 1.5 h, and mixing rate of 300 rpm. The CBPA-catalyzed WLO transesterification depicted the highest catalytic capability FAME yield of 98.8%. The catalysts\u27 effectiveness in the WLO transesterification was in relation to the potassium content and the surface area. The LOME physicochemical properties were within the specified biodiesel standard. Thus, a route for an ecologically and economically sustainable fuel could be established with bio-derived catalyst
Effect of alkaline treatment on the tensile properties of oil palm empty fruit bunch (OPEFB) fibre
No full textThe use of oil palm empty fruit bunch has gained worldwide attention and acceptance among the researchers as eco-friendly materials. Hence, oil palm trees has been one the most flourishing and fruitful economic trees in the West Africa and beyond. OPEFB is nothing but a waste material obtained after the extraction of palm fruits. In other to process OPEFB, the fruits are stripped from the bunches leaving the empty bunches as rejects/waste materials. These waste materials, are usually abandoned or dumped in the oil mill industrial site which has created a lot of environmental degradations or implications such as soil acidification, air pollution and others which are harmful to man and his natural environment. It was also observed that the measurement of the tensile properties of fibres enhances the proper utilization as well as the mechanical strength of the fibre strands/bundle. Therefore, this paper addressed the effect of using the following concentrations of NaOH 7%, 10%, 15% and 17% on the mechanical properties of OPEFB fibres with time variations of 24hrs, 72hrs and 120hrs. Finally, the test was performed using D1445 in accordance with ASTM standard which selects 15%w/v of NaOH as the optimized concentrations after 120hrs. Therefore, it was observed that the treatment of OPEFB fibre at 15%w/v of NaOH at 120hrs improves the fibre most when compared with the result of untreated fibres and other treatments and their time variations. The so-called waste can be utilized in the reinforcement of soils thereby converting waste to wealth for the benefit of mankind. In other words, the treated material is competent enough to serve as reinforced composites in geotechnical engineering applications
Influence of Fuel Types and Additives on the Efficiency of Catalytic Converter Materials in Automotive Applications
No full textThe increasing demand for sustainable and low-emission transportation has intensified research into improving the efficiency of catalytic converters (CCs) in automotive applications. One critical challenge lies in understanding how different fuel types and the incorporation of various additives influence the performance and longevity of CC materials. This study aims to investigate the impact of conventional and alternative fuels, along with specific fuel additives, on the thermal stability, conversion efficiency, and degradation behavior of CCs. A combination of thermo gravimetric analysis (TGA), X-ray diffraction (XRD), and scanning electron microscopy (SEM) was employed to examine the structural and compositional changes in the catalyst materials. Engine bench testing and exhaust emission analyses were also conducted to evaluate real-time performance. The results revealed that fuel composition significantly affects the catalytic activity, with certain additives enhancing oxidation reactions while others accelerate material degradation. Converters exposed to biofuel blends exhibited improved NOx and CO conversion efficiencies, while metal-based additives led to notable sintering and poisoning effects. It is recommended that future automotive fuel formulations be optimized not only for combustion efficiency but also for compatibility with advanced catalytic materials to ensure prolonged converter life and reduced environmental impact
Hybridized Deep learning Techniques for Enhanced SMS Spam Detection system
No full textShort Message Service (SMS), popularly call text messaging, has revolutionized communication by enabling rapid and convenient information exchange among users. Despite its widespread use it comes with some flaws that has made it a target for spanners. This justifies the need for spam detection system. The study developed an hybridized spam detection system. The dataset used for the spam detection classification was downloaded from kaggle .com repository. Two Feature extraction (FE) which are: Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT) were used. The study then employed three techniques which are LSTM, CNN -LSTM and Linear Regression. The results of the three-model developed for spam detection revealed that CNN-LSTM model achieves the highest ACC (99%), followed by LSTM (98%) and Logistic Regression (94%). CNN-LSTM also recorded superior performance in precision (98%), Sen (91%), and F1-score (94%). The study concluded CNN-LSTM achieves state-of-the-art accuracy, while LSTM also demonstrates strong performance. Logistic Regression, while providing a good baseline, is generally outperformed by the deep learning approaches. The model is recommended for mobile communication sector to protect privacy violation of the user. More deep learning techniques and FE can be employed in future in order to increase the ACC of the model
Complementing the Quantity of Quarry Dust Filler with Lime in Hot Mix Asphalt
No full textThis study was undertaken to evaluate the effect of quarry dust fillers and hydrated lime on stability and volumetric properties of asphalt. The optimum binder content was determined from asphalt produced with 100% quarry dust regarded as the control mix. The predetermined optimum binder content of the control was used for production of all mixes which includes: 75%granite dust and 25% lime, 50% granite dust and 50% lime, 25% granite dust and 75% lime and 100%granite dust and 0% lime. The aggregates, bitumen and asphalt mix were subjected to various testing. The tests include: sieve analysis test, specific gravity test, aggregate impact and crushing value test, softening point test, water absorption test, flash and fire point test and marshal stability test. Results obtained showed that the optimum binder content was obtained at 6%, the specific gravity of sand, granite dust and granite was 2.67, 2.62 and 2.58 respectively. Marshal Stability result showed that the stability decreased with increase in lime content from 14.5 kN for the control to 5.3 kN for the mixture containing 100% of filler as hydrated lime. The flow and volumetric properties mostly complied with the specification given by Federal Ministry of Works and Housing, FMWH (1997) apart from the voids filled with bitumen (VTB) for which some values were less than the specified. The study underscores that the use of quarry dust fillers remain a better option as it requires lesser amount of bitumen for better strength and volumetric properties
Advanced Techniques for Fuel Blend Optimization Using Machine Learning, Thermodynamic Modeling and Experimental Validation Methods
No full textThe growing demand for cleaner and more efficient energy sources in internal combustion engines necessitates the development of optimized fuel blends. Conventional diesel, despite its high energy density, suffers from suboptimal combustion efficiency and elevated pollutant emissions. This study, titled Advanced Techniques for Fuel Blend Optimization (FBO) Using Machine Learning (ML) Thermodynamic Modeling (TDM) and Experimental Validation Methods (EVMs), aims to enhance engine performance and reduce emissions through the integration of alternative fuel blends (AFBs) and advanced predictive techniques. The primary objective is to identify optimal fuel formulations that outperform conventional diesel in thermal efficiency and environmental impact while leveraging modern machine learning (ML) and thermodynamic tools for performance forecasting and analysis. The methodology involves experimental testing of multiple diesel-diethyl ether (DEE) fuel blends, thermodynamic assessments including exergy and entropy analyses, and the application of three ML models Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) to predict engine parameters such as Brake Thermal Efficiency (BTE), NOₓ and CO emissions. Results show that Blend B4 (70% diesel + 30% DEE) achieved the highest BTE (34.0%), lowest BSFC (230 g/kWh), and significantly reduced emissions. XGBoost outperformed other ML models with R² values above 0.90 and lowest prediction errors (MAPE < 3%). The study concludes that oxygenated, high-cetane blends like B4 offer superior performance and environmental benefits. Furthermore, ML-based predictive models, particularly XGBoost, are reliable tools for real-time engine optimization. It is recommended that future research explore broader fuel types and integrate ML with real-time control systems for smart combustion managemen
Corrosion Kinetics of Mild Steel Coated with Titanium-Based Anti-corrosion Polyethylene Wax from Waste LDPE Water Sachets in Brine and Acidic Media
No full textCorrosion of carbon steel remains a major global challenge across multiple industrial sectors, necessitating efficient and sustainable mitigation strategies. This study presents a novel titanium-based anticorrosion polyethylene wax (TAC-PEW) coating synthesized from discarded low-density polyethylene (LDPE) sachets sourced from sachet water industries. LDPE was pyrolyzed under optimal conditions of 450 °C for 35 min, yielding 60.77 % wax, which was subsequently blended with titanium and other additives to produce TAC-PEW. In acidic media, TAC-PEW provided substantial protection for mild steel with highest inhibition efficiency of 85.7 %. Kinetic analysis showed that the data fitted an exponential decay model with an exceptional correlation coefficient (R² = 0.9999) and a decay constant of 0.2529 hr⁻¹, indicating rapid suppression of the initial corrosion rate.TAC-PEW demonstrates dual environmental and industrial benefits by repurposing non-biodegradable plastic waste into a high-performance, sustainable coating for corrosion control in aggressive environment
Prediction of porosity of weldmetal strength using response surface methodology
No full textAchieving optimal weld quality and strength in the welding industry,requires precise control and prediction of porosity. Traditional methods like Response Surface Methodology (RSM) have been widely used for this purpose. This study aims to predict the porosity of weldmetal strength, This study involved conducting 20 experimental welding runs with varying current, voltage, and weld speed. The resulting data on porosity was used to develop predictive model using RSM. The RSM models were developed using standard statistical techniques. The performance of these models was evaluated based on their predictive accuracy, as indicated by R-squared values of 90.08% showing ability to predict porosity the weld metal strength
Soil-Structure Interaction Effects on the Buckling Behaviour of Braced Multi-Storey Steel Frames
No full textThis study investigated the influence of soil stiffness on the global buckling behaviour of braced 10-storey high steel frame structures. The structural stability of three types of bracings (x-bracing, v-bracing, and single cross bracing) was studied when subjected to gravity and wind actions. On the assumption of an elastic sandy soil deposit that can be described using the soil\u27s elastic properties (modulus of elasticity and Poisson\u27s ratio), the soil-structure interaction effects were modelled and compared using the continuum finite element (CFE) approach and Winkler\u27s support approach. The findings reveal that, regardless of the modelling approach utilized, soil stiffness impacts the critical buckling load of steel frames under gravity loads. It was observed that the difference in the gravity load buckling factor of the frames for loose and stiff soil deposits was larger than 70%. In general, the buckling load factors (BLF) decreased with lower soil stiffnesses, but at greater soil stiffnesses, they converged to the value of theoretical pinned support. Furthermore, the buckling load for lateral loads was discovered to be affected by the modelling approach. With increasing soil stiffness, Winkler\u27s model converged quickly to a pinned support condition for lateral loads, but the CFE technique converged slowly
Impact of Cannabis sativa cultivation on soil physicochemical properties in Akure Forest Reserve, Ondo State, Nigeria
No full textThe conversion of forest estates to other land use, particularly cannabis plantations, could have a detrimental effect on soil health and production. However, the impact of this practice on the physical and chemical properties of soil in this forest reserve is not known. This study was carried out in Akure Forest Reserve, Ondo State. Soil samples were collected at four depths and from four distinct areas of this forest: the undisturbed area, the degraded area without hemp, the degraded area with hemp in 2020, and the degraded area with hemp in 2023. Determination of soil physical and chemical characteristics was done using the Bouyoucos hydrometer, Walkley-Black net oxidation and potentiometer methods. Soil physical properties in this forest varied with depth and among the forest classes. In the undisturbed area of the forest, the study revealed that sand content decreased with depth, clay content increased with depth, while the silt content was the same with depth. Both soil physical and chemical properties were adjudged poor in all the degraded classes, revealing the potential of anthropogenic activities, particularly activities related to hemp planting, to destroy soil structure and health. These activities could lead to the destruction of the soil seed bank, resulting in poor regeneration and deficient tree growth. To avert deforestation and other negative consequences that may be occasioned by hemp planting activities, government should discourage forest destruction for Hemp planting and implement sustainable forest management practices