LAUTECH Journal of Engineering and Technology

Co- Digestion of Neem (Azadirachta indica) Shoot Biomass with Poultry Droppings: Effect of Pretreatment Methods on Biogas Production

2025-05-15T17:07:27+00:00

Inadequate energy supply, environmental pollution, and declining soil fertility are major challenges in developing nations like Nigeria. Despite the abundance of biomass, much of it ends up as unmanaged solid waste. This study evaluated the effects of pretreatment on biogas yield from the co-digestion of Neem (Azadirachta indica) shoot biomass with poultry waste and cow rumen (inoculum). Materials were sourced from LAUTECH and prepared by washing, blending (mechanical pretreatment). The blended neem shoot was pretreated in a water bath to about 60 degrees Celsius for 1hr 20 minutes (thermal pretreatment). Chemical pretreatment was adopted to aid in the degradation of the lignin content. 4g of NaOH was dissolved in distilled water and then added to the thermally blended biomass. Two batches were prepared from the chemically treated Neem shoots, with poultry waste (batch A) and cow rumen (batch B), and put into airtight biodigesters. Physicochemical parameters (pH, TN, TP, TC, BOD, COD, MC, TS, C/N, FS) of both slurry and digestate were analyzed using standard methods. Biogas production, pH, and temperature were monitored over 30 days, and gas composition was determined via Gas Chromatography-Mass Spectrometry. Batch A showed biogas yields of 0.1016–0.0326 L/day, pH 8.39–8.41, and 35.7–35.8°C; Batch B yielded 0.1628–0.0488 L/day, pH 8.36–8.39, and 35.7–35.8°C. Methane content was 61.29% in Batch A and 63.29% in Batch B. ANOVA indicated significant differences in yields (p = 0.0256 for A, p = 0.0200 for B). Results showed that co-digestion, particularly with cow rumen, improved methane output. The produced methane is suitable for use in cooking, heating, and electricity generation, offering a sustainable solution for waste-to-energy conversion in Nigeria.

Solid Wastes and Greenhouse Gas Emissions Management for Energy Derivation: Case Study of LAUTECH Ogbomoso and Environs

2025-04-29T12:44:24+00:00

Final disposal of solid wastes at Ladoke Akintola University of Technology (LAUTECH) Ogbomoso, and its environs is by scavenging, dumping sites and open-air burning. This research aimed at studying the solid waste generation and greenhouse gas emissions management for energy derivation at LAUTECH and environs. The university was divided into sixteen zones based on Faculties and other prevailing activities on campus. Waste samples were obtained from bins and dumping sites, for 5 days (Monday, Tuesday, Wednesday, Thursday and Friday) in three years (2021, 2023 and 2024) for waste composition data. Sorted waste samples were taken to the laboratory to carry out moisture and energy content analyses. Methane (CH?) and Carbon dioxide (CO?) emissions from dumping sites and farm areas within LAUTECH and its environs were also measured using gas detectors. The collected primary data was analyzed statistically and discussed. Estimated waste generation in LAUTECH was 6161.47 kg/day, resulting in a daily waste generation rate of about 187 g per head, considering a university population of 33,000. The Energy content of daily wastes was 107.19 MJ, implying an electricity generation up to 0.02977 MWh (approx. 29.77 kWh) from daily steam production. Methane (CH?) levels range from 75 ppm (Rabbit Unit) to 2,107 ppm (layer birds, Abogunde Farms) and CO? concentrations vary between 400 ppm and 470 ppm, across farms. However, methane levels recorded peak values e.g., 11,169 ppm at AA Rano, 8,763 ppm at college, and 6,900 ppm at ALICE. CO? is highest at college (1,171 ppm) and AA Rano (1169 ppm). TVOC and HCHO values remain low at farm sites, while elevated at dumpsites. Considering the high material recyclability, reusability and energy recovery potentials from solid wastes generated from LAUTECH Ogbomoso and environs, there is an urgent need for emissions control in high-risk dumpsites through methods such as methane capture and air quality filtration. These actions are critical for environmental protection and safeguarding public health.

Beneficiation Consequence on the Influence of Potential Hydrogen Variation in the Froth Flotation of Farin-Lamba (Plateau State) Cassiterite

2025-04-28T14:15:59+00:00

This study explores the influence of pH variation on the froth flotation performance of cassiterite ore obtained from the alluvial deposits of Farin-Lamba, Plateau State. Bulk ore samples were acquired via random sampling across the active mining site and subsequently homogenized to ensure uniformity. A 20 kg head sample was prepared, from which a representative 5 kg sub-sample was subjected to comminution and sieving. The processed material underwent chemical analysis, particle size distribution assessment, and beneficiation through froth flotation under controlled pH conditions. Initial analysis showed a tin oxide (SnO₂) grade of 20.22%. Sieve analysis across various mesh sizes identified -180+125 µm fraction as the optimal liberation size, recording the highest assay value of 23.28% SnO₂. Accordingly, sieve fractions -250+180, -180+125, and -125+90 µm were selected for flotation experiments conducted using a Denver D-12 mechanical agitator at 1200 rpm and gas flow of 0.5-1.0 m²/h. Experiments were carried out at pH 5, 7, and 9 to determine the best condition Farin-Lamba cassiterite to be beneficiated. Post-flotation analysis revealed that the highest SnO₂ concentration of 65.62% was achieved at pH 9 within the -180+125 µm size fraction, indicating this condition as optimal for tin beneficiation from Farin-Lamba cassiterite.

Optimization of Polypropylene Dosage for Improved Rheological, Physical and Mechanical Properties of Agbabu Bitumen

2025-05-31T16:02:07+00:00

The vast majority of road infrastructure deformations are irreversible. They shorten the lifespan of flexible pavements and add to road safety concerns. Viscoelasticity is required for natural bitumen to function as a binder in pavements. However, when exposed to climate and heavy loads, natural bitumen's ability to undergo elastic deformation reduces. Consequently, road researchers have focused on modifying bitumen using polymers and nanomaterials to enhance pavement performance. Polymeric bitumen is extremely sensitive to Polypropylene (PP) dosage. Excessive use of PP leads to high viscosity. This study examines the impact of PP doses on the physical, rheological, and mechanical properties of Agbabu natural bitumen. The bitumen was dehydrated and analyzed for conventional characteristics using established techniques. The purified material was treated with PP at varying dosages (1.5 - 6 wt percent), and a binary mixture of bitumen and PP was optimized using D-Optima experimental design and response surface approach. The result shows that the mechanical properties, flash, and softening points of the raw bitumen were enhanced after modification. However, the penetration point of the modified bitumen decreases while viscosity increases as PP dosage increased from 2 to 3.75 wt percent. Therefore, the optimum dosage of 2.75 wt percent PP is recommended.

Evaluation of the Environmental and Social Benefits of Conversion Process of Open Cycle to Combined Cycle Gas Power Plant

2025-05-30T12:08:40+00:00

Worldwide concern on reducing global warming consequences and combating energy crisis has motivated the development of power generation technologies to move towards sustainable energy production with higher efficiency and low environmental impacts. This study evaluated the environmental and social benefits of converting open cycle to combined cycle gas power plants in electric power generating system in Nigeria. All the current operational open and combined cycle gas power plants were considered. Green House Gas (GHG) emission data were collected for both open and combined cycle plants. The results showed that after conversion from open cycle to combined cycle, society bears a lesser cost of generating electricity as there is a minimum difference of 3.78 N/kWh (Calabar NIPP), which is about 23.34% change in cost and a maximum of 4.00 N/kWh (Omotosho Pacific Energy plant), which is about 25.20% change in cost for a minimum range of emission cost (40USD/tCO₂e). There is a minimum difference of 8.54 N/kWh (Calabar NIPP), which is about 28.57% change in cost and a maximum of 8.76 N/kWh (Omotosho Pacific Energy plant) which is about 29.64% change in cost for a maximum emission cost (100USD/tCO₂e). The study concluded that it costs less to reduce GHG and air pollution damage during the process of conversion from open cycle to combined cycle gas. Also, it is more beneficial to generate electricity using combined gas turbine and the society bears less cost for a higher electricity generation by a combined cycle when compared with an open cycle.

Pozzolanic Potential of Anacardium Occidentale Nutshell Ash (Aonsa) and Its Impact on the Mechanical Properties of Concrete

2025-05-08T15:39:24+00:00

Investigating agricultural residue as pozzolans offers a two-fold benefits. It mitigates indiscriminate disposal what appears as a waste to some end users and promotes the sustainability of concrete. Tons of empty shells from Anacardium Occidentale Nutshells (AON), a by-product of the Anacardium Occidentale processing industry, are disposed indiscriminately in the environment where they eventually become nuisance. However, there is a notable paucity of all-inclusive studies exploring the pozzolanic potential and the impact of Anacardium Occidentale nutshell ash (AONSA) on the mechanical properties of concrete.

This study aims to contribute to the available knowledge base on the pozzolanic potential of AON being an agricultural residue, which serves as an outlet for AON. Mechanical properties of AONSA incorporated concrete was investigated. AONSA was obtained from the incineration of Anacardium Occidentale nutshell sourced from local Anacardium Occidentale processing unit in Ogbomoso and subjected to air-drying after getting rid of the nuts. Open ignition under ambient air took place to obtain AONS ash and this was calcinated in an Engineering Laboratory in LAUTECH.

AONSA samples A and B were placed differently inside furnace at 800 for 5 hours and 500 for 7 hours for calcination. X-Ray Diffraction (XRD) analysis and X-Ray Fluorescence (XRF) analysis and Scanning Electron Microscopy (SEM) analysis, were employed to assess the pozzolanicity of AONSA while notable observations of the effects of time and elevated temperatures on AONSA were noted.

The percentage XRD result of Quartz, Muscovite, Glauconite, Osumilite, Illite and Albite present in samples A and B are 6.4 (2), 47.8 (9), 18.3 (6), 23.6 (7), 3.9 (7), 0 and 24.3 (10), 21.3 (9), 29.9 (11), 0, 19.8 (9), 4.7 (2) respectively. XRF has these results for MgO, SiO₂, P₂O₅, SO₃, K₂O, CaO, TiO₂, MnO, Fe₂O₃, NiO, CuO, ZnO and Y₂O₃ for samples A and B are 7.620, 2.849, 4.761, 1.868, 20.88, 4.309, 583.5ppm,0.119, 10.073, 4.3 ppm, 388.9 ppm, 4.3 ppm, 388.9 ppm, 0.443, 75.0 and 5.691, 2.788, 4.817, 1.750, 21.893, 425.3 ppm, 0.115, 9.718, 0.0, 387.8 ppm, 0.851, 7.6 ppm, respectively. The element in SEM for samples A and B are K, Fe, Mg, Ca, P, Na, S, Si, Al, Cl, Ti with these percentages: 39.04, 12.24, 18.95, 6.81, 6.98, 6.93, 2.81, 3.02, 2.04, 1.18, 0.0 and 45.34, 7.28, 20.90, 5.93, 6.08, 6.27, 2.25, 2.48, 2.46, 1.01, 0.00, respectively.

The presence of silicate, lime, and aluminum oxides oxide in AONSA contributes to its suitability as a pozzolan. The SEM analysis shows that with increase temperature at 5 hours, the atomic weight of Potassium got reduced compared to decrease in temperature at 7 hours. The availability of SiO₂and Y₂O₃ in AONSA is essentially the same as that of cement. Furthermore, availability of MgO in AONSA is roughly 5.6912%, which is lower than 6% specified for cement in IS: 12, 269 – 1987. The amount of lime (CaO) in AONSA is roughly one-third that of Ordinary Portland Cement, as compared to alternative cement substitutes like fly ash.

Development of an IOT Based Fingerprint Biometric Attendance System

2025-06-19T11:09:00+00:00

This research explores the design and implementation of an IoT-based fingerprint biometric attendance system. The traditional methods of attendance tracking often suffer from inaccuracies, time fraud, and significant administrative burdens. In response to these challenges, biometric systems have gained popularity for their ability to uniquely identify individuals through their physical or behavioral characteristics, offering a more reliable and secure approach to attendance management. The system proposed in this research utilizes fingerprint recognition, one of the most widely adopted biometric modalities, due to its high accuracy, ease of use, and cost-effectiveness. Integrating this system with the Internet of Things (IoT) expands its capabilities. The system comprises an ESP32 microcontroller, a fingerprint module, an OLED display, and a locally hosted PHP-based web interface. The OLED display serves as an immediate feedback mechanism for users, confirming whether their attendance has been successfully recorded by displaying the appropriate message. The web interface is designed for administrative use, allowing for the management of attendance records, user enrollment, and data exportation for further analysis. The results of this research demonstrate that the proposed IoT-based fingerprint biometric attendance system is a feasible and efficient solution. It offers a user-friendly interface for both students and administrators, significantly improving the accuracy and security of attendance tracking. The system’s modular design and scalability also allow for future enhancements and adaptations to meet specific needs.

Development of a Fingerprint-Based Gender Detection System Using an Optimized Convolutional Neural Network

2025-06-06T13:35:42+00:00

Biometrics is a technology that identifies or verifies individuals based on unique physical or behavioral traits, offering a reliable form of authentication in sectors like healthcare, law enforcement, and security. Existing gender detection systems using fingerprints face challenges due to poor image quality and complex ridge patterns, while Convolutional Neural Networks (CNNs), though promising, are hindered by issues like overfitting, slow convergence, and getting trapped in local minima. Therefore, this study developed a fingerprint-based gender detection system through the optimization of CNN with Whale Optimization Algorithm. A dataset of 2,200 gender-labelled fingerprint images (1,320 male and 880 female) was acquired from Kaggle.com. The images underwent preprocessing involving cropping, grayscale conversion, histogram equalization for enhancement, and edge detection filtering to eliminate noise. Optimized CNN model was formulated using Whale Optimization Algorithm (WOA) by tuning CNN hyperparameters: number of neurons and dropout rate. The resulting WOA-CNN was employed for feature extraction (edges, texture patterns, shapes) and detection of fingerprint images. The model was implemented in MATLAB R2023a. Performance was evaluated using accuracy, sensitivity, specificity, false positive rate, precision, and recognition time, with an 80-20% training-testing split. CNN achieved 95.86% accuracy, 96.44% sensitivity, 95.00% specificity, 5.00% false positive rate, 96.66% precision, and 99.90 s recognition time. WOA-CNN achieved 97.23% accuracy, 97.58% sensitivity, 96.70% specificity, 3.30% false positive rate, 97.80% precision, and 87.40 s recognition time. This research showed WOA-CNN outperformed CNN in all metrics. It is recommended for use in biometric authentication, security checkpoints, and forensic investigations.

Synthesis and Characterization of Laggera Aurita-Derived Acetic Acid-Activated Carbon (LAAC) and it’s Potential for Toxic Element (TE) Metal Removal from Water

2025-05-11T21:46:09+00:00

The increasing contamination of water resources by toxic heavy metals necessitates the development of cost-effective and sustainable adsorbents. This study investigates the synthesis, characterization, and adsorption potential of Laggera aurita-derived activated carbon (LAAC) for the removal of Pb(II), Cd(II), and Cu(II) from aqueous solutions. LAAC was prepared via acetic acid activation followed by pyrolysis at 500°C and characterized using FTIR, SEM, EDXRF, BET and XRD surface area analysis. FTIR confirmed the presence of functional groups (–OH, –COOH, –C?C–, and S–H) that facilitate toxic element (TE) adsorption through hydrogen bonding, ?-electron interactions, ion exchange, and chelation. SEM revealed a nanostructured surface (nanotubes and nanospheres) with high affinity for Pb²?, Cd²?, and Cr(VI) due to increased active sites. BET analysis indicated a microporous structure (334.6 m²/g), enhancing TE retention via ion trapping and complexation with –COOH/–OH groups. Horvath-Kawazoe (HK) analysis further demonstrated an ultramicropore volume (0.5939 cc/g), enabling molecular sieving and Pb²? capture through dehydration mechanisms. EDXRF revealed CaO, 5.334%, SiO?, 4.836%, CeO?, 5.009%, P?O?, 1.902%, and SO?, 2.2966%. CaO provides alkaline sites that enhance cation exchange for metals like Pb²?, Cd²?, and Cu²?. XRD confirmed the nanocrystalline nature (3.82 nm crystallite size), contributing to high surface reactivity. These findings highlight LAAC as a promising, sustainable adsorbent for heavy metal removal, with future research needed to optimize activation parameters and assess real-world applicability.

Development of a Coati-Optimized Convolutional Neural Network for infected citrus fruit detection and classification system

2025-06-06T13:42:45+00:00

Pest and disease management plays a significant role in minimizing losses to crops, particularly in citrus fruit production. Traditional methods for detecting and classifying infected citrus fruits are complex and tasking, while Convolutional Neural Networks (CNNs) offer promising solutions but still face challenges such as high computational requirements and data dependency. Therefore, this study developed an improved convolution neural network for infected citrus fruit detection and classification system using Coati Optimization Algorithm (COA). A dataset of 1,790 citrus images, containing samples of black spot, greening spot, citrus canker, and healthy fruits, was acquired from www.kaggle.com. The images underwent preprocessing involving cropping to remove unwanted elements, conversion to grayscale to simplify processing, normalization to enhance data consistency and reduce redundancy, and filtering to minimize noise. An optimized CNN model was formulated using COA to tune the hyperparameters (weight and learning rate) of CNN to produce Coati Optimization Algorithm–based Convolutional Neural Network (COA-CNN). The preprocessed images serve as input to the COA-CNN model. The COA-CNN was used for the extraction of edges, corners, texture, patterns and shapes, and classification of citrus fruits as infected or healthy. The developed system was implemented using MATLAB R(2023a). The system’s performance was evaluated using accuracy, false positive rate, sensitivity, specificity, and recognition time. A comparative analysis of CNN and COA-CNN was also carried out. The accuracy, false positive rate, sensitivity, specificity, and recognition time for CNN were 95.83%, 6.02%, 96.63%, 93.98% and 202.17 s, respectively, while the corresponding values for COA-CNN were 96.92%, 4.22%, 97.41%, 95.78% and 136.86 s. This research showed that COA-CNN performed better and is recommended for citrus disease detection and classification systems.