FUPRE Journal of Scientific and Industrial Research (FJSIR)

Production of Vanillin Alcohol from Sawdust as an Intermediate Product for the Production of Epoxy Resin

2025-08-04T12:55:57+00:00

This study focuses on the production of vanillin alcohol from sawdust, which is a
renewable and abundant lignocellulosic biomass, as an intermediate product for
the synthesis of epoxy resin. The research aims to utilize sawdust (a waste that
presently creates disposal challenge) as a sustainable raw material to reduce
Nigeria’s reliance on imported specialty chemicals, particularly in the oil and gas
industry. The process involved three main stages: extraction of lignin from sawdust
using a Soxhlet extractor, oxidation of lignin to vanillin with alkaline nitrobenzene,
and the reduction of vanillin-to-vanillin alcohol using sodium borohydride, with
an 80-90% yield. The reaction parameters, which include temperature, reaction
time, NaOH concentration, and sawdust particle size, were systematically varied
to optimize process efficiency and eventual yield. The final product was
characterized using melting point determination and ferric chloride chemical test,
confirming the successful synthesis of vanillin alcohol. The study demonstrated
that smaller sawdust particle sizes (1.18 mm) increased lignin yield due to
enhanced surface area availability, resulting in a 3% improvement in vanillin
production. Key challenges, such as equipment limitations and reagent availability,
were addressed to ensure reliable results. This research presents a cost-effective
and environmentally friendly approach to producing vanillin alcohol, supporting
the development of bio-based epoxy resins and reducing dependency on synthetic
chemicals.

Development and Characterization of an Oil-Soluble Viscosifier for an Oil-Based Drilling Fluid Preparation with Sugarcane Bagasse

2025-08-04T12:27:46+00:00

Drilling fluids are essential in drilling operations, transporting cuttings to the
surface, and providing requirements to prevent catastrophic events during drilling
operations. This work is aimed at developing a suitable viscosifier, from and agrowaste
product (sugarcane bagasse), which contains a considerable amount of wax,
which is soluble in the oil phase of the OBM, thereby improving the rheological
properties of the entire mud. This was achieved by Solvent Extraction of processed
sugarcane bagasse, using carbon tetrachloride as a solvent, Isopropyl alcohol and
activated carbon for the treatment of obtained wax. The activated carbon was also
produced from 50% of the air-dried bagasse, and wax was extracted from (60g,
75g, 80g, and 85g) of powdered bagasse sample, having a yield of an average of
11.3% (wt. /wt.) basis. Obtained wax was characterized by Fourier Transform
Infrared Spectroscopy (FT-IR), indicating the presence of alkanes, ketones,
alcohols, aldehydes and carboxylic acids respectively. Oil-based mud having a mud
weight of 10.00ppg was prepared, using Diesel oil as base oil with an oil/water ratio
of 70/20. It was tested with varying concentrations of the developed sugarcane wax
viscosifier (0%, 0.46%, 0.91%, 1.35%, 1.80% and 2.24%), to observe trends in
rheological properties such as Apparent Viscosity, Plastic Viscosity, Yield Point,
Gel Strengths and Marsh Funnel Viscosity, which revealed a positive increasing
trend of rheological properties with an increase in concentration of the viscosifier;
carried out at room temperature. OBM Electrical Stabilities were also obtained
and revealed a decreasing trend with an increase in viscosifier concentration.
Therefore, Sugarcane waxes can serve as substitutes for Viscosifiers in OBMs.

Geochemical and Multi-Index Ecological Risk Assessment of Heavy Metal Contamination in Surface Soils of Auto-Mechanic Workshops in Effurun, Niger Delta, Nigeria

2025-07-29T08:55:35+00:00

This study investigates the pollution levels and ecological risks posed by heavy metals
in surface soils of auto-mechanic workshops in Effurun, Niger Delta, Nigeria. A total
of forty soil samples, composited into eight representative samples (including a
control), were systematically collected from various parts of the auto-mechanic
village, and analyzed using Atomic Absorption Spectrophotometry. Mean
concentrations (mg/kg) of heavy metals across sampled sites followed the order: Fe
(806.85) >Zn (128.46)>Cu (33.68) >Pb (21.49) >Ni (5.55) >Cr (1.10)>Cd (0.29),
significantly exceeding control values and international guidelines. Contamination
assessment using multiple indices revealed: extreme contamination factors for Pb
(CF=860.27), Cr (CF=2076), and Ni (CF=2776.5) at site C6; geo-accumulation indices
indicating extreme pollution (Igeo>5) for multiple metals; and enrichment factors
showing moderate to severe anthropogenic inputs (EF=3.8-22.5). All sites exhibited
Pollution Load Index values exceeding 1 (ranging from 1.32-57.65), indicating soil
quality deterioration. Site C6 emerged as a critical pollution hotspot with extremely
high ecological risk (RI=24,116.15). Statistical analyses identified significant positive
correlations between Cu-Zn (r=0.809), Pb-Ni (r=0.841), and Cu-Ni (r=0.792).
Principal Component Analysis extracted three components explaining 76.1% of total
variance, while Hierarchical Cluster Analysis classified sites into three distinct
contamination levels. These findings demonstrate significant environmental
degradation and necessitate urgent environmental regulation, remediation efforts,
improved waste management, and continuous monitoring to mitigate ecological and
health risks in this sensitive region.

Evaluation and analysis of SUWAN-1-SR Escherichia coli-mediated acetone-butanolethanol fermentation using roasted corn cob species for biobutanol synthesis

2025-07-29T08:55:36+00:00

The Objective of this paper was to describe and assess the SUWAN-1-SR species
of roasted corn cobs for the acetone-butanol-ethanol fermentation process using
Escherichia coli to produce biobutanol. The feedstock percentage content of
cellulose, hemicellulose, and lignin was estimated, with values of 43.65%, 51.50%,
and 4.85%, respectively. Variations in elemental composition were found when the
SUWAN-1-SR Species of maize cobs were characterized; potassium had the
highest weight percentage of 0.75 and a of peak 4143 cp/Ma The Response surface
model optimization showed how time 27.1978 minutes, temperature of 80.69810C
and a maximum concentration of 0.4623mol/dm3 were adjusted based on the
model developed from each response and gave the percentage total sugar yield of
1489.88 g/l for SUWAN-1 SR species of roasted corn cobs, the adjusted R2 value
was 0.9054. HPLC analysis was also performed to determine the concentration of
biobutanol produced from the ABE fermentation process.

Bacteriological and Physicochemical Evaluation of Petroleum Reservoirs (Flow Stations) in Delta State, Nigeria

2025-07-29T08:55:37+00:00

The chemical and physical characteristics of crude oil are greatly influenced by
microbial activity in petroleum reservoirs, which also affects the oil's economic value
and circumstances of exploitation. The physicochemical and bacteriological
properties of crude oil samples taken from four flow stations in Delta State, Nigeria,
were examined in this study. Total heterotrophic bacteria (THB), hydrocarbonutilizing
bacteria (HUB), and sulfate-reducing bacteria (SRB) were analysed using
standard microbiological procedures. Also, using recognized analytical techniques,
physicochemical parameters including pH, total nitrogen, organic carbon, dissolved
oxygen, and sulfate concentration were ascertained. The findings showed that the
crude oils had a pH ranged from 6.20 to 6.70, that of total nitrogen ranged from 0.05
to 0.08 percent, and that the organic carbon ranged from 9.42% to 11.23 percent.
Potential hazards for microbial corrosion and biofouling were indicated by the
considerable variation in total heterotrophic bacterial counts, which ranged from
1.10×107 to 3.1×107 cfu/ml. Hydrocarbon- utilizing bacterial counts ranged from
0.4×104 to 1.2×104 cfu/ml, indicating active microbial engagement in the
biodegradation of crude oil. The samples were classified as sweet crude because of the
notable absence of sulfate-reducing bacteria, which is consistent with the reported
non-detection of sulfur compounds. The majority of the bacterial isolates found were
Pseudomonas aeruginosa and Bacillus subtilis, which are known to contribute to
biofilms formation, breakdown of hydrocarbons, and biocorrosion. In order to reduce
adverse effects on oil production, environmental integrity, and public health, the
study emphasizes the significance of continuous microbiological surveillance and
reservoir management.

Securing Ride-Sharing Platforms with Blockchain: Enhancing Privacy, Fraud Prevention, And Transaction Integrity Using Elliptic Curve Cryptography and Zero- Knowledge Proofs

2025-07-29T08:55:38+00:00

The increasing reliance on centralized ride-sharing platforms has led to significant
security vulnerabilities, including data breaches, fraud, and identity theft. This
study proposes a blockchain-based ride-sharing framework leveraging Elliptic
Curve Cryptography (ECC) and Zero-Knowledge Proofs (ZKPs) to enhance
security, privacy, and transparency. ECC is implemented for secure digital
signatures and authentication, ensuring tamper-proof ride transactions with
reduced computational overhead compared to traditional cryptographic methods.
ZKPs enable anonymous identity verification, allowing users to authenticate
themselves without revealing personal data, thereby mitigating identity fraud and
unauthorized access. A comparative performance evaluation is conducted to assess
the transaction speed, latency, scalability, and security resilience of the proposed
blockchain-based system versus traditional centralized ride-sharing platforms.
The findings reveal that while blockchain transactions experience slightly higher
latency (1.5–2.3s) and lower scalability (900–1,050 TPS) than centralized systems,
they exhibit superior security resilience, successfully blocking over 90% of
cyberattacks. Matplotlib-generated performance charts over seven days of uptime
illustrate blockchain’s advantages in fraud prevention and transaction integrity,
despite its current scalability challenges. The study also provides a detailed
mathematical breakdown of ECC and ZKPs, demonstrating their implementation
in ride-sharing identity verification and secure fare processing. The results suggest
that future ride-sharing architectures should incorporate hybrid blockchain
models to balance scalability and decentralization. The proposed framework
contributes to the advancement of secure, privacy-preserving, and fraud-resistant
decentralized transportation systems, paving the way for real-world deployment
and industry adoption.

Monitoring of Crude Oil Pipeline Using Long-Range Network

2025-08-07T10:59:59+00:00

This study presents a crude oil pipeline monitoring system using Long Range
(LoRa) networks in Internet of Things (IoT)-based technology. Pipeline leakages,
caused by aging pipes, rusting, and vandalism, remain a major challenge in crude
oil distribution. While existing IoT-based solutions use short-range technologies
like Bluetooth, WiFi, and ZigBee for real-time monitoring, they struggle with longdistance
communication in remote areas with poor network coverage. This study
applies LoRa technology to transmit leakage data over long distances to a remote
monitoring point. An experimental testbed was designed to simulate pipeline
scenarios, using pressure readings to detect leaks. Results showed that in 9 seconds,
leak pressure at point 1 (P1) near the supply rose from 0 Psi to 11.74 Psi, while
pressures at P2 (0.325m from P1) and P3 (0.55m from P2) increased to 10.48 Psi
and 9.79 Psi, respectively. This indicates that leak pressures build up more near
the supply source, and increasing the leak opening results in higher pressure loss.
The findings highlight the effectiveness of LoRa-based IoT systems for real-time,
long-range pipeline monitoring and early leak detection.

Design, Fabrication and Optimization of an Automated Groundnut Shelling Machine

2025-07-29T08:55:40+00:00

This research investigated the design, fabrication and optimization of an
automated groundnut shelling machine to overcome challenges associated with
manual shelling, including increased labour costs, longer processing times, and
inconsistent shelling quality. The methodology involves a multi-objective
optimization approach, considering factors such as the radius of the shelling drum,
radius of the concave mesh, surface area in contact with shelling blades, rasp bar
spacing, and the number of shelling blades. The performance evaluation includes
mechanical damage, shelling efficiency, and throughput capacity, measured
through experiments using groundnut seeds. Material selection for the groundnut
sheller was based on a materials index. The result showed that carbon steel is the
most preferable material, followed by low alloy steel, aluminium, wrought iron,
stainless steel, and oak wood. A global optimal condition was found at radius of the
shelling drum (X1) of 0.17, radius of the concave mesh (X2) of 0.0914, surface area
in contact with shelling blades (X3) of 0.424, rasp bar spacing (X4) of 0.013 and
number of shelling blades (X5) of 3. At these conditions, mechanical damage (Y1),
shelling efficiency (Y2), and throughput capacity (Y3) were 12.32%, 80.4942% and
50.22 g/sec respectively. The validation of the mathematical model through
ANOVA confirms the adequacy and significance of the coefficients in the developed
model. The Pareto chart visually represents the standardized effects of different
parameters, providing insights into the relative importance of factor combinations.
The designed and fabricated machine demonstrated promising results and
reduced damage.

Health Risk Assessment and Source Apportionment of PAHs in Rainwater Obtained in an Urban Area in Southern Nigeria

2025-07-29T08:55:42+00:00

This study investigates the presence and composition of potential health risks from
the sources of Polycyclic Aromatic Hydrocarbons (PAHs) in rainwater collected
from urban rooftops in Asaba, Delta State, Nigeria. Ten rainwater samples were
analyzed for their PAH composition using Gas Chromatography Flame Ionization
Detection (GC-FID), HP5890 Series II model. Average Daily Dose (ADD),
benzo(a)pyrene toxic equivalence (B(A)Pteq), and carcinogenic toxic equivalence
(TEQs) were utilized to evaluate the potential health risks associated with the
consumption of the rainwater. Also, molecular diagnostic ratios were employed to
identify the sources of PAHs in the water. The results indicated the presence of
PAHs in all the samples, with average concentrations ranging from 0.21 to 6.4 μg/L.
The evaluated ADD values ranged from 0.006286 to 0.201143 μg/L, while B(A)Pteq
ranged from 0.0013 to 2.5, with a TEQ value of 5.1. The study established that most
PAH levels were below critical thresholds for non-carcinogenic effects. The
potential cancer risk assessment (B(A)Pteq and TEQs) revealed values exceeding
the USEPA's negligible risk threshold for cancer risk (CR), indicating significant
public health concerns. Furthermore, it was observed that lower molecular weight
PAHs accounted for 63.73% of the total composition. Inferences from the
molecular diagnostic ratio plots suggest that the pyrogenic processes, particularly
fuel combustion from industrial and vehicular emissions were dominant sources of
PAHs in the rainwater.

Investigating the structural, optical and electrical properties of electrochemically deposited aluminum-doped manganese sulfide for photovoltaic applications

2025-07-29T08:55:43+00:00

MnS has garnered considerable research attention because of its interesting
magneto-optical properties, layered structure and superior charge transport
facilitated by ionic strength. The micrographs revealed that Al doped MnS resulted
to agglomeration from rod like shapes to uniform spherical nanostructures. The
XRD analysis of MnS reveals a cubic crystal structure, evidenced by distinct peaks.
The pattern of MnS is modified by the addition of aluminum due to Mn ion
replacement resulting to subtle peak position and intensity changes. The energy
bandgap decreased from 2.30 to 2.22 eV as the concentration increased from 0.1 to
0.3 M. The results obtained from the four-point probe showed that as the electrical
resistivity increased from 0.126 to 0.142 ohm/m, the resulting conductivity reduced
from 7.935 to 7.042 S/m. The deposited thin films can be applied for photovoltaic
and solar cells applications.

Development Of an Improved Machine Learning Model for Stock Exchange Prediction System Using Long Short-Term Memory (LSTM)

2025-07-29T08:55:43+00:00

The stock market serves as a fundamental driver and indicator of a nation’s
economy, providing opportunities for investors to trade and invest in shares of
companies and organizations listed on stock exchanges. This process aids in
enhancing the capital base of these companies, thereby yielding profits for market
investors. However, a significant challenge within the stock market is its inherent
volatility and uncertainty. The prices of various companies' stocks may fluctuate
unpredictably, resulting in potential investment losses. This dissertation proposes
a machine learning algorithm designed to predict stock market activities, aiming
to assist investors in making informed investment decisions. Specifically, we
introduce a Recurrent Neural Network model, the Long Short-Term Memory
(LSTM), utilizing the Structured System Analysis and Design Methodology
(SSADM). The model focuses on predicting the stock performance of Banks in
Nigeria, using a dataset sourced from the Nigerian Stock Exchange Group
repository, covering the period from 2000 to 2011. Implemented in Python within
Google Collaboratory, a cloud-based open-source machine learning development
environment, the model achieved an accuracy of 97.8% with a low error margin.
This high accuracy makes the model suitable for stock market prediction, thereby
mitigating investment risks and enhancing investor confidence.

Hybrid Deep Learning Convolution Neural Network Scheme for Enhanced Human Activity Recognition System in Security Surveillance

2025-07-29T08:55:44+00:00

Nigeria continues to experience a high security breach with the attendant loss of lives and
properties worth in billions especially in the North-East as ravaged by insurgency,
kidnapping and other forms of crime that have overwhelmed law enforcement agencies.
Technology have been explored to help curb security threats and improve surveillance. This
study proposes an enhanced human activity recognition system with hybrid deep learning
fuzzy convolutional neural network. We utilize dynamic agile development mode using the
python IDE for training and testing the machine learning models. The CNN-Fuzzy model
was trained with 3,500 dataset extracted from 7 classes of the UCF Crime dataset which was
further split into 70:30 ratio for training and test purposes. The model after 30 epoch with
training and validation accuracy of 0.9954 and 0.9954 yielded a prediction accuracy of
97.14%, Recall of 97.14%, F1-Score of 96.83% and precision of 114.2 outperforming the
existing system making it an efficient tool for security surveillance to mitigate security
breaches and generate early warning signals for security agencies.

Evaluating the Effects of Cu-Water Nanofluid, Space/Temperature Dependent Heat Source/Sink on Magnetohydrodynamics (MHD) Heat and Mass Transfer Stretching sheet with Buoyancy Forces

2025-07-29T08:55:45+00:00

Magnetohydrodynamics (MHD) heat and mass transfer with nanofluid flow has
attracted several authors in science, engineering and several fields of endeavour.
Several Newtonian and non-Newtonian fluids experiences friction in the course of
motion due to the presence of viscous forces. The flow of Cu-water nanofluid
volume fraction with the combine effects of space, temperature dependent heat
source/sink and buoyancy forces is considered in this study. The governing
equation is transformed from partial differential to ordinary differential using the
stream function and the similarity variables and solved numerically using solved
numerical using the mathematical package called the Mid-point Richardson
extrapolation code in MAPLE 2021. Hence, results are shown in tables and graphs
for various increasing values of the governing parameters. It is revealed from the
result of the study that the increase in the second-grade parameter (α) results in
the increase of the fluid velocity, The increasing function of the volume fraction (φ)
brings about the increase in the fluid motion and in the temperature of the fluid at
free stream, the increase in the space and temperature dependent heat source/sink
parameters A and B respectively result to the increase in the of the temperature at
the free stream and decrease in the concentration of the fluid and the increase in
the yields result to increase in the motion, temperature and concentration of the
fluid.

Relevance of Adequate Sewage disposal on Disease Prevalence Amongst Sub-urban Communities in Delta State: a Case of Uvwie

2025-08-04T12:24:41+00:00

The study investigated Relevance of Adequate Sewage disposal on Disease
Prevalence Amongst Sub-urban Communities in Delta State: a Case of Uvwie. The
study used combined expo-factor and survey research designs, collecting medical
case reports from Uvwie and distributing questionnaires to five communities. The
dataset included medical records from the Central Hospital in Ekpan from 2023.
The questionnaires were distributed using systematic sampling to ensure coverage.
A total of 180 questionnaires were distributed evenly across five communities, with
36 per community and each fifth building. The data was analysed with Multiple
Regression. The physicochemical properties of sewage discharged in a specific area
revealed the ability of disease-causing microorganisms to grow rapidly. These may
infect nearby populations. The most common illness was diarrhoea, and the least
common was typhoid. Diarrhoea is common in Effurun, but dysentery is unlikely,
whereas dysentery is prevalent in Uviwe. The study found a significant correlation
(P<0.05) between indiscriminate sewage disposal and disease prevalence in Uvwie
(R-value = 0.799). The study attributed 64% of disease prevalence to
indiscriminate disposal and 36% to climate. The indiscriminate disposal of sewage
contributes to the spread of diseases. Approximately 48.9% of survey respondents
believe sewage causes breathing problems. Furthermore, 45% of respondents
recommend treating sewage before disposal, while 17.8% suggest cleaning gutters.
The study emphasises the importance of effective sewage disposal management
and makes recommendations for improving regulatory agencies, enforcement
mechanisms, and agency-Public Health Department collaboration.

Developing A Web Application for Pandemic Palliative Food Distribution Using Blockchain Technology

2025-08-12T17:23:49+00:00

Blockchain technology can track food supply chains and also help to build trust
between donors and end-users. The COVID-19 pandemic had detrimental impacts
on the world economy, resulting in food shortages. Addressing the averting of food
loss in the process of the supply chain, involving both contributors and
manufacturers, has emerged as a significant defiance for many establishments.
This research created a web application aimed at alleviating food distribution
issues during the pandemic, utilizing blockchain technology. Following an
examination of the current food distribution methods in Nigeria, the study
formulated a model that incorporates the Secure Hash Algorithm (SHA) 256 for
effective food distribution. The result after evaluation of the developed model
performance showed responsiveness as 69%, speed as 70% and availability as
80%, so with that, effective distribution of pandemic palliatives item was made
possible substantially using blockchain technology.