LAUTECH Journal of Engineering and Technology

Engr Evaluation of IoT-Based Thermodynamic Monitoring for Predictive Weather Analysis in Tropical Microclimates

Fri, 15 May 2026 00:00:00 +0000

Accurate short-term weather forecasting in tropical regions demands high-resolution tracking of rapidly changing thermodynamic variables. This study evaluates a low-cost IoT weather monitoring system over 60 days, using an ESP8266 microcontroller coupled with a DHT22 sensor to measure temperature (°C), relative humidity (%), dew point (°C), and heat index (°C) every 5–10 seconds, generating >44,000 data points. Analysis showed temperature, dew point, and heat index exhibited excellent stability (CV < 1%), while relative humidity varied moderately (CV ? 2–3%). Pearson correlation revealed strong interdependence: temperature–heat index (r = 0.98), dew point–heat index (r = 0.93), and relative humidity–dew point (r = 0.91). A rule-based classification identified Moderate-Cloudy conditions 95–97% of the time, validating real-time microclimatic assessment. By integrating high-frequency measurement with derived thermodynamic parameters, this system provides robust predictive insights, offering a scalable, low-cost alternative to conventional weather stations. Applications include precision agriculture, disaster mitigation, and climate-resilient urban planning, showcasing the engineering potential of compact IoT-based monitoring systems.

Evaluating the Impact of Virtual Reality (VR) Technology in Enhancing Waste Sorting Skills

R. M. Balogun-Adeleye, C. P. Ibeakanma, F. O. Okewole — Fri, 15 May 2026 00:00:00 +0000

The inefficiency of solid waste management in rapidly urbanizing regions such as Lagos, Nigeria, presents a persistent environmental challenge largely driven by inadequate waste sorting at the source. Conventional educational interventions often fail to translate theoretical knowledge into effective waste sorting skills. This study explores the potential of virtual reality (VR) as a tool to improve waste-sorting performance among university students through an interactive, gamified learning experience. A total of 274 participants were exposed to a custom-designed VR module featuring a tutorial, a timed sorting simulation, and a visual feedback system. A baseline assessment of waste management knowledge and technology acceptance was also investigated. This study reported an increase in waste-sorting accuracy (84.5%) with the use of the VR system. The use of visuals to represent different waste categories and the negative impacts of improper waste sorting also increased their knowledge and attitudes towards waste management after exposure to the VR learning environment. This research highlights the potential of VR technology as a viable and impactful tool for bridging the knowledge-skill gap in environmental education and sustainable waste management.

Seasonal Variation and Machine Learning-Based Prediction of Atmospheric Refractivity over Southwestern Nigeria

Fri, 15 May 2026 00:00:00 +0000

Atmospheric radio refractivity strongly influences radio wave propagation, link reliability and signal quality in tropical regions. In south-western Nigeria, pronounced wet-and-dry seasonal transitions lead to substantial variability in refractivity, yet data-driven predictive tools remain limited. This lack of reliable prediction poses a challenge for communication system planning and performance optimisation in the region. This study investigates the seasonal variation in surface atmospheric refractivity over south-western Nigeria and evaluates the effectiveness of machine learning techniques in predicting it. Four years of meteorological data from an automatic weather station were used to compute refractivity using the Smith–Weintraub formulation. Seasonal and monthly patterns were analysed using descriptive statistics, correlation analysis, and time-series techniques, including trend and anomaly detection. Supervised machine learning models, including Support Vector Regression, Random Forest, Gradient Boosting, and a Multi-Layer Perceptron, were trained to predict daily refractivity from meteorological inputs. Results show seasonal dependence, with higher refractivity during the wet season driven by increased humidity. Among the models, Support Vector Regression achieved the highest predictive accuracy (R² ? 0.9999, RMSE ? 0.066), followed by Random Forest. The findings demonstrate that machine learning is a reliable and effective approach for predicting atmospheric refractivity and capturing its seasonal variability in tropical environments.

Auto-Thermal Oxy-Steam Co-Gasification of Multi-Layer Plastic Packaging Waste and Diverse Biomass for Hydrogen-rich Syngas: A Simulation-Based Screening Study

Fri, 15 May 2026 00:00:00 +0000

The non-recyclability of multi-layer plastic waste (MLP) and the underutilization of biomass present significant environmental challenges. This study developed and validated an Aspen Plus model for auto-thermal oxy-steam co-gasification of biomass-plastic mixture to produce hydrogen-rich syngas in a downdraft gasifier. Five different biomass-plastic mixtures involving MLP and five locally available biomass [Sawdust (SD), Rice Husk (RH), Palm Kernel Shell (PKS), Lemmon Grass (LG), Sugarcane Bagasse (SB)] were formulated and compared for feedstock selection using hydrogen yield under different MLP concentrations. Using the optimal feedstock, a parametric analysis was conducted to determine the ranges of Equivalent Ratio (ER) and Steam to Feedstock Ratio (SFR) that maximize hydrogen yield while maintaining thermal self-sufficiency. Among the fuel mixtures, the most promising was MLP-LG. The results showed that hydrogen yield increases with higher MLP concentration and SFR, but decreases when the ER exceeds 0.15. The optimum operating ranges for co-gasification of LG-MLP were determined to be 2.6 to 3.0 for SFR and 0.1 to 0.2 for ER, corresponding to a hydrogen yield of 133.57 to 133.73 g/kg feed. This study offers a practical approach to screening various plastic-biomass mixtures for their hydrogen-production potential prior to laboratory testing and industrial applications.

Development and Evaluation of Non- Invasive Blood Pressure Monitor for Laboratory Rats

S. C. Nwaneri, B. E. Sajere, A. T. Olabinjo, F. O. Awobajo — Fri, 15 May 2026 00:00:00 +0000

Accurate blood pressure measurement in small laboratory animals is crucial for cardiovascular and pharmacological research. However, blood pressure monitors for laboratory rats are expensive and not locally fabricated. This study was conducted to develop and evaluate a low-cost, locally fabricated tail-cuff blood pressure monitoring system for laboratory rats. The device was developed using a high-sensitivity MPXHZ6400AC6T1 pressure sensor, an Arduino Uno microcontroller, a relay-controlled air pump, a solenoid valve, and an LCD unit for real-time visualization of systolic and diastolic readings. Pilot testing was performed on five adult Wistar rats under standardized laboratory conditions to determine the workability of the device. The developed tail-cuff system recorded a mean systolic blood pressure (SBP) of 135.4 ± 3.86 mmHg and a mean diastolic blood pressure (DBP) of 100.7 ± 3.42 mmHg, with consistent and reproducible results across trials. When compared with results obtained from a reference tail-cuff device and invasive methods reported in the literature, the developed system showed close alignment with invasive benchmarks. The minimal 5–7% deviation between the developed tail cuff and invasive methods indicates that the system provides valid readings while overcoming the overestimation tendencies observed in conventional tail-cuff systems. Overall, the findings confirm that the developed automated tail-cuff blood pressure monitoring system provides a stable, accurate, and reproducible non-invasive measurement technique for small animals. By integrating automated pressure control, precise sensing, wireless data handling, and digital signal optimization, the system successfully bridges the gap between invasive accuracy and non-invasive practicality

Walrus Optimization-tuned Deep Belief Network for Credit Card Fraud Detection

Fri, 15 May 2026 00:00:00 +0000

Detection of credit card fraud (CCFD) has become a critical research area as financial losses continue to increase annually. The traditional rule-based and conventional machine learning model struggles to address the challenges of concept drift and an imbalanced dataset. While deep belief network (DBN) algorithms can learn highly complex features, they require meticulous hyperparameter tuning, which can lead to suboptimal convergence. Hence, this study employs walrus optimization algorithm to automate the DBNs hyperparameters. Ten thousand (10,000) of the imbalance dataset containing 3000 fraudulent and 6000 non-fraudulent transactional datasets were obtained and pre-processed using imputation, min-max, and one-hot encoding techniques. The DBNs were developed as a stack of Restricted Boltzmann Machines (RBMs). The optimised DBNs (WOA-DBNs) were then developed and applied to credit card fraud detection, with data divided 60:40, 70:30, 75:25, and 80:20 (train: test), generated randomly. The implementation was performed using MATLAB 2023a. The performance of DBN-CCFD was evaluated and compared with the performance of WOA-DBN-CCFD. At the highest training ratio of 80:20, WOA-DBN-CCFD shows False Positive Rate (FPR), sensitivity, specificity, precision, F1-Score, accuracy and detection time of 8.25%, 97.81%, 91.75%, 97.93%, 97.87%, 96.60% and 26.01s as against DBN-CCFD of 12.25%, 96.81%, 87.75%, 96.93%, 96.87%, 95.00% and 33.97s respectively. This performance metric indicates that the developed WOA-DBN-CCFD shows modestly better performance in credit card fraud detection, with lower FPR and detection time, while maintaining higher values on other metrics.

A, Optimization of Hydro-Solar-Biogas Hybrid Systems for Off-Grid Rural Electrification: A Review With Focus on Developing Countries.

Fri, 15 May 2026 00:00:00 +0000

Access to reliable, affordable, and sustainable electricity remains a major challenge for many rural communities in developing countries. Although governments continue to expand national power grids, a large number of rural settlements are still either unconnected or experience frequent power outages due to inadequate infrastructure, transmission losses, and poor system maintenance. This persistent energy gap constrains socioeconomic development, restricts access to quality healthcare and education, and contributes to increased rural–urban migration. Hybrid Renewable Energy Systems (HRES), particularly those integrating hydro, solar, and biogas resources, present a viable solution for decentralized and off-grid electricity supply that aligns well with the resource conditions of many rural areas. However, designing and operating these hybrid systems involves significant technical complexity. Challenges related to optimal system sizing, energy scheduling, and operational control under fluctuating demand and intermittent renewable resource availability remain critical. Without effective management, these systems may suffer from reduced efficiency, higher costs, or unreliable power supply. This review presents a comprehensive evaluation of optimization techniques applied to fully renewable hydro–solar–biogas hybrid energy systems. It synthesizes existing literature on optimization algorithms, performance assessment metrics, and practical implementation challenges, with the aim of providing useful insights for researchers, system designers, and policymakers.

Proximate and Mineral Analysis of Evaporative-Cooled Tomatoes

O. T. Popoola, H. A. Issa, I. K. Adegun, H. K. Ibrahim — Thu, 30 Apr 2026 00:00:00 +0000

Postharvest losses of tomatoes remain a major challenge in sub-Saharan Africa due to erratic power supply and inadequate storage systems. This study evaluated the effects of aluminium -in-pot evaporative cooling systems on the proximate composition, mineral content, and physiological weight loss of two tomato cultivars (UTC and Plum). The tomatoes were stored in five different Aluminium-in-pot evaporative (A, B, C, D and E) coolers and under ambient conditions (control). The proximate analysis was done using Association of Official Analytical Chemists (AOAC) methods, while mineral contents, lycopene, and vitamin C were determined using an atomic absorption spectrometer and titrimetric methods, respectively. Results showed that weight loss in the evaporative cooling system ranged from 2% to 9% as compared to the weight loss of the control case, which varied from 3.7% to 25%. The shelf life of tomatoes in evaporative cooler E was extended to 15 days, while for the control case, it lasted only 4 to 5 days. Additionally, evaporative cooling significantly reduced weight loss and rate of decay while improving retention of moisture, vitamin C, lycopene, and essential minerals relative to ambient. These findings demonstrate the effectiveness of evaporative cooling as a low-cost, sustainable technology for extending tomato shelf life and preserving nutritional quality, with potential applications in post-harvest storage system design.

Development Of Convolutional Neural Network-Based Pelican Optimization Algorithm for Handwriting Identification System

Wed, 18 Mar 2026 00:00:00 +0000

Handwriting identification remains a significant challenge in the field of information processing and Optical Character Recognition (OCR) due to the diverse nature of human writing styles. Traditional Convolutional Neural Network (CNN) models, although widely used, often suffer from overfitting, hyperparameter sensitivity, and limited adaptability to different handwriting patterns. This research addressed these challenges by optimizing Convolutional Neural Network (CNN) with Pelican Optimization Algorithm (POA) for Handwriting identification System (HRS). A handwriting dataset comprising 3000 forged handwriting samples and 3000 original handwriting samples was obtained from Kaggle,com. The images were resized, normalized, and augmented to enhance model generalization. POA-CNN was developed by using Pelican Optimization Algorithm to fine-tune CNN hyperparameters of learning rate, layer size, activation functions, and batch sizes with Keras support packages. The POA-CNN model was implemented in MATLAB R(2023a).The system’s performance was evaluated across varying decision thresholds with six key metrics employed: False Positive Rate (FPR), Specificity (SPEC), Sensitivity (SEN), Precision (PREC), Accuracy (ACC), and Computational Time (CT). The model was compared with traditional CNN. The optimum threshold was 0.51. The FPR, SPEC, SEN, PREC, ACC and CT for POA-CNN were 3.20%, 96.80%, 96.70%, 96.80%, 96.75%, and 81.95 s, respectively. The corresponding values for CNN were 4.77%, 95.23%, 95.13%, 95.23%, 95.18%, and 91.20 s, respectively. The developed POA-CNN for handwriting identification system demonstrated better performance than the traditional CNN, across all metrics. Overall, the results demonstrate that POA-based hyperparameter optimization significantly improves the accuracy and reliability of CNN-based handwriting identification for effective forgery detection.

Comparative Investigation of the Effect of Pulverized Egg Shell and Potato Peel Powder as Additives on Rheological Properties of Water-Based Drilling Fluid

O. S. Teniola, M. A. Alarape, E. L. Odekanle, M. B. Madehin — Wed, 18 Mar 2026 00:00:00 +0000

The typical additives applied in mud preparation for oil well drilling usually have detrimental effects on the environment and crew safety. However, additives of biodegradable origin have the capacity to eliminate these effects. This study employed a mixture of two food wastes (egg shell and potato peel powders), which were dried and pulverized as alternative drilling fluid additives. A variety of muds prepared with different quantities of the additives (potato peel powder (PPP) and egg shell powder (ESP)) were subjected to rheological and filtration testing. According to the results obtained, ESP lowers the yield point and filtrate loss by an average of 65 % and 2.2 %, respectively, while increasing plastic viscosity and mud density by 50% and 0.75%, respectively, at higher concentrations. Additionally, the additions were able to lower the pH by one unit. In contrast, PPP demonstrated a decrease of about 50% in each of plastic and apparent viscosities, yield point, and pH, while boosting mud density and filtrate reduction at higher doses. When coupled, ESP and PPP indicated a 25 % drop in plastic viscosity and yield point but enhanced mud density, lowered filtrate, and enhanced mudcake formation at lower concentrations. These findings suggest that while ESP and PPP can change a range of fluid properties, their careful combination in drilling mud formulation has great potential to improve all desirable rheological and filtration features when compared to traditional additives like sodium carbonate and xanthan gum.

Development of Cassava Leaf Disease Detection System using Convolutional Neural Network-Based Zebra Optimization Algorithm

Wed, 18 Mar 2026 00:00:00 +0000

Cassava is a widely cultivated root crop valued for its starchy tubers and nutritional importance in tropical regions; however, its productivity is severely affected by diseases and pests, resulting in substantial yield losses for farmers. Although recent advances in deep learning offer effective solutions for automated disease detection, challenges related to model accuracy and hyperparameter tuning remain. This study optimizes a Convolutional Neural Network (CNN) using the Zebra Optimization Algorithm (ZOA) for cassava leaf disease detection. A total of 19,620 cassava leaf images were obtained from Kaggle and categorized into four classes: Cassava Mosaic Disease, Cassava Green Mite, Cassava Bacterial Blight, and healthy leaves. Image preprocessing techniques, including cropping, grayscale conversion, and normalization, were applied to enhance training efficiency. The ZOA was employed to optimize key CNN hyperparameters, specifically the number of neurons and dropout rate. The ZOA-CNN model was implemented using MATLAB R2023a and evaluated using false positive rate, specificity, sensitivity, accuracy, and recognition time. Experimental results show that the ZOA-CNN achieved an FPR of 1.11%, specificity of 98.89%, sensitivity of 95.28%, accuracy of 98.12%, and recognition time of 37.70 s, outperforming the conventional CNN. These results demonstrate that the ZOA-CNN improves detection accuracy, reduces false detections, and enhances computational efficiency, making it suitable for real-world cassava disease monitoring applications.

Fuzzy Synthetic Evaluation of the Level of Service of Agba Dam Water Treatment Plant

O. G. Okeola, T. S. Abdulkadir, A. A. Abdulazeez, A. W. Salami — Wed, 18 Mar 2026 00:00:00 +0000

This study addresses the critical need for a robust evaluation of water treatment plant performance, with a focus on the level of service at the Agba Dam Water Treatment Plant in Nigeria. The objective was to apply fuzzy set theory for objective assessment. The methodology involved collecting 1664 water quality parameter measurements, including pH, conductivity, turbidity, and total dissolved solids. Of these, 354 valid entries served as performance indicators. These data were integrated into a fuzzy synthetic evaluation model assessing service level against prescribed regulatory and expert limits. The key result showed water quality across all sampled parameters was consistently within limits, with mean values: pH = 7.15, conductivity = 176.62 ?S/cm, turbidity = 1.23 NTU, and total dissolved solids = 87.18 mg/L. Consequently, it's concluded that the Agba Dam Water Treatment Plant delivers an excellent level of service, scoring 2.75 on a defined 3-point fuzzy evaluation scale. This scale was explicitly defined using a Likert-type system with linguistic terms 'Poor', 'Good', and 'Excellent', and was validated by expert biochemists and principal scientists from the plant.

Development of an Optimized Deep Learning Technique for Tomato Leaf Diseases Recognision

Wed, 18 Mar 2026 00:00:00 +0000

Tomatoes are among the most widely cultivated and consumed vegetables globally, valued for their rich nutritional content and versatility in culinary applications. However, tomatoes are bedevilled with diseases and pests that wipe out approximately half of farmers’ harvests every year. Recent advancements in deep learning provide promising solutions for automating disease recognition; however, challenges still persist in achieving high accuracy and hyperparameter tuning. Hence, this research optimized Convolutional Neural Network (CNN) with Hippopotamus Optimizer (HO) for Tomato Leaf Diseases Recognition. Four thousand five hundred and thirty-six (4536) images of tomato leaf were downloaded from kaggle.com. The acquired images were grouped into four (4) classes: bacterial spot, early blight, leaf mould, and healthy leaves. The images were preprocessed by cropping to remove unwanted elements, converting to gray-scale for colour complexity reduction, normalizing and filtering to reduce noise. An optimized Convolutional Neural Network (CNN) using Hippopotamus Optimizer (HO), (HO-CNN) was developed. The HO-CNN was employed to select optimal values of number of neurons and dropout rates for CNN hyperparameters; The HO-CNN was implemented using MATLAB R(2023a). The evaluation metrics used were False Positive Rate (FPR), Specificity (Spec), Sensitivity (Sen), Precision (Pre), Accuracy (Acc), and Recognition Time (RT), and the HO-CNN was compared with the traditional CNN. The FPR, Spec, Sen, Pre, Acc, and RT for HO-CNN were 2.59%, 97.41%, 95.11%, 95.67%, 96.55% and 38.16 s, respectively. The corresponding values for CNN were 5.29%, 94.71%, 90.61%, 91.14%, 93.17% and 81.66 s, respectively. A Hippopotamus optimized-Convolutional Neural Network (HO-CNN) improves tomato leaf disease recognition accuracy by about 3.6%, reduces false detections by approximately 51%, and decreases recognition time by nearly 53% compared to the traditional CNN. The HO-CNN developed can be applied for tomato leaf disease recognition in real-world agricultural development.

Optimization of Ethylene – Vinyl Acetate Dosage for Rheological and Physico-Mechanical Properties of Loda Bitumen

R. O. Tewegbola, A. O. Arinkoola, T. O. Salawudeen, O. O. Ogunleye — Wed, 18 Mar 2026 00:00:00 +0000

Heavy reliance on road transportation, especially in developing countries, has necessitated the construction of durable roads using localized materials such as natural bitumen. However, using natural bitumen as a binder in pavement construction gave poor performance and hence, the need for its modification prior to application. This study investigated ethylene–vinyl acetate (EVA) as a modifier and optimized its dosage for improved rheological, physical, and mechanical properties of Loda natural bitumen. A D-optimal mixture design coupled with Response Surface Methodology (RSM) was deployed for evaluating the effect of EVA content (1.5–6 wt%) on penetration, softening point, ductility, viscosity, and flash point. Thirteen experimental runs were analyzed using ANOVA to develop predictive and experimentally validated models. The result showed that EVA modification reduced penetration, increased softening and flash points, enhanced ductility, and reduced the viscosity of the base Loda bitumen. Multi-objective optimization identified an optimal composition of 95.6 wt% bitumen and 4.4 wt% EVA, yielding a penetration of 17.95 mm, softening point of 59.79 °C, ductility of 117.32 cm, flash point of 290.25 °C, and viscosity of 2658.22 MPa·s. These results demonstrate that optimized EVA modification significantly enhances Loda natural bitumen, supporting its use in durable pavements and promoting sustainable utilization of local bitumen resources.

Influence of Fractionation on the Oxidative Stability, Thermal Stability, and Fatty Acid Profile of Shea Olein Fractions

Wed, 18 Mar 2026 00:00:00 +0000

Oxidative stability can directly affect oil quality and shelf life, especially in fat and oil-containing products such as shea olein. Shea butter is becoming increasingly popular in foods, cosmetics and pharmaceutical products, but is generally unstable. The fractionation of shea butter affects the stability of shea olein. Therefore, this study investigated the effect of fractionalization on oxidative and thermal stability with fatty acid composition of shea olein. Shea butter was fractionalized using a cold centrifuge at 5 ? and -5 ? to obtain two fractions of shea olein. The stability of the shea olein fractions was evaluated with peroxide values (PV), p-anisidine value (p-AV), conjugated dienes (CD), and Thiobarbituric Acid Reactive Substances (TBARS). The thermal stability and fatty acid composition were determined using the differential scanning calorimetry method and the Gas Chromatographic method. The percentage yield for crude shea butter, shea olein (DSOA), and super shea olein (DSOB) were 37.5%, 75.59% and 50.94%, respectively. The PV, p-AV, CD and TBARS were in the ranges 0.32-1.59 meq O/kg, 7.28 - 11.51, 0.59 - 5.00 mmol/g and 0.10 to 0.14 mmol/g. The thermal stability reflects an endothermic transition of CSB, DSOA, and DSOB as -17.35 mW, -12.93 mW, and -3.85 mW, while their fatty acid profile revealed two prominent acids, arachidic acid (39.87, 40.04, and 29.26%), oleic acid (47.83%, 48.30%, and 56.23%), respectively. The study demonstrated that the oxidative and thermal stability of shea olein is achievable during fractionation, leading to a more stable oil for food formulations.

Green Synthesis and Characterization of Tin (IV) Oxide Nanoparticles from Fresh and Dried Senna alata Leaf Extract

O. Adegboyega, O. P. Ojo, O. Olabisi, G. A. Alamu, O. Adedokun — Wed, 18 Mar 2026 00:00:00 +0000

Despite the growing interest in plant-mediated nanoparticle synthesis, limited research has examined how the condition of plant material (fresh or dried) has affected the efficiency and quality of synthesized nanoparticles. This study reports the green synthesis of tin (IV) oxide (SnO?) nanoparticles using aqueous extracts from fresh and dried Senna alata leaf as reducing, stabilizing, and capping agents, with 1.0 M Tin (II) chloride dihydrate (SnCl?·2H?O) as the precursor. Structural, morphological, optical, and thermal properties of the synthesized nanoparticles were investigated. XRD analysis confirmed the formation of crystalline tetragonal rutile-phase SnO?, with average crystallite sizes of 3.7 nm for fresh-leaf extract and 8.19 nm for dried-leaf extract. FTIR spectra revealed stronger and more distinct functional groups (O–H, C–H, C–O, and NO??) in nanoparticles derived from fresh extracts. SEM and TEM analyses showed uniformly distributed, spherical nanoparticles with minimal agglomeration and average particle sizes of 9.88 nm (fresh extract) and 9.80 nm (dried extract). EDX analysis confirmed elemental purity with dominant Sn and O signals and complete removal of chlorine residues. Optical studies demonstrated that fresh-leaf-derived nanoparticles exhibited higher absorbance, lower transmittance, and a narrower band gap (3.21 eV) compared to the dried-leaf counterpart (3.58 eV). Thermal conductivity results indicated superior heat-transport performance for nanoparticles synthesized from fresh leaves, particularly at lower temperatures. These findings demonstrated that fresh Senna alata leaf extract provides a potential sustainable and efficient route for producing high-quality SnO? nanoparticles with enhanced optical and thermal properties for advanced technological applications.

Optimization of Green Corrosion Inhibitor Dosage in Acidic Medium: A Case Study of Hunteria umbellata Seed Extracts

O. Adio, A. O. Olanite, A. O. Arinkoola, O. O. Ogunleye — Wed, 18 Mar 2026 00:00:00 +0000

This study investigated the corrosion inhibition performance of Hunteria umbellata seed Extract (HUE) on mild steel in an acidic medium. A Box-Behnken design (BBD)-based optimization was used to analyze the factors affecting inhibition efficiency such as inhibitor concentration, temperature and time. Corrosion studies were carried out using gravimetric weight loss measurement and electrochemical polarization methods. The identification of the constituents of the HUE was done using phytochemical screening and GC-MS analysis. The surface morphology of the coupon was assessed using scanning electron microscopy (SEM). The research revealed that the inhibitor demonstrated good inhibition potential with optimum inhibition efficiency of 89.677% at a concentration of 0.98 g/L, after an immersion time of 10 h at a temperature of 30.22 °C.