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Carbon nanoparticles from Polyethylene Terephthalate (PET) bottles has attracted good attention owing to their exceptional properties such as wide surface area, lower cost, biocompatibility, cytotoxicity, catalytic properties, drug delivery and biosensing. The processed carbon nanoparticles from PET bottles, using hydrothermal method possesses an excellent adsorption quality for the removal of Fe (III) from waste water. The characteristics of the carbon nanoparticles were revealed using Fourier Transform Infrared Spectrometer (FTIR), Energy Dispersive Spectrometer (EDX), X-ray Diffractrometry (XRD), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The adsorption of Fe (III) from the waste water increases with increase in contact time (15min – 2 h) and dosage (0.1g - 0.8g). The characterization and application results showed that carbon nanoparticles from PET bottles synthesized by hydrothermal methods, possesses good surface chemistry that promote effective adsorption of Fe (III) from waste water. This makes PET bottles a viable material for the synthesis of carbon nanoparticles for heavy metals removal from waste water. Keywords: PET bottles, carbon nanoparticles, waste water, hydrothermal synthesis, adsorption.

Fuel price hikes have a significant impact on a country's economic development because they affect travel patterns, freight and service travel, and the cost of operating public transportation, which affects things like road tolls, car maintenance, and mobility costs. This study looks at how fuel price hikes affect Ibadan's public transport operators and how the city's transportation system is affected by fuel prices. A study involving 286 respondents from ten automobile parks in Ibadan found that most respondents (29.7%) reported high mobility expenses, with customer satisfaction second (26.6%). Fuel price increases can lead to higher freight or service mobility costs, as fuel prices increase the cost of public transportation. A regression analysis showed a significant correlation between fuel price increases and public transportation operations in Ibadan.According to the report, rising gasoline prices have a direct impact on inflation and discourage people from taking public transit. According to the findings, in order to lower inflation, the government should create an intervention program to discourage fuel hoarding among fuel marketers. Keywords: Fuel Hike, Transportation, Costs, Subsidy Freight

The Orogodo River, situated in Agbor, Delta State, Nigeria, is a crucial water resource that sustains numerous economic and recreational activities. However, the river faces significant pollution risks from various human-related sources, such as industrial and domestic waste, which can introduce polycyclic aromatic hydrocarbons (PAHs) into the water. PAHs are toxic and carcinogenic substances that pose serious risks to both human health and the environment. This study focused on assessing the levels of PAHs in the Orogodo River’s water. Water samples were gathered from five distinct locations along the river and analyzed for PAHs using gas chromatography-mass spectrometry (GC-MS). The findings revealed that PAH concentrations in the river surpassed the maximum permissible limits established by regulatory authorities. The highest levels of PAHs were detected in samples taken near industrial and urban zones, highlighting these areas as major contributors to PAH contamination. Among the PAH compounds identified, acenaphthylene was the most prevalent, while benzo(a)pyrene was the least detected.The results of this study carry important implications for both human health and environmental sustainability. The elevated levels of PAHs detected in the Orogodo River present a potential risk to individuals who rely on the river for domestic and recreational activities. As such, immediate action is required to address and reduce PAH pollution in the river. This includes adopting effective waste management practices and ensuring strict enforcement of environmental regulations. The findings of this study offer critical insights for policymakers, environmental managers, and other stakeholders, enabling them to formulate strategies aimed at mitigating PAH contamination in the Orogodo River. These efforts are essential to safeguarding the health and well-being of communities that depend on this vital water resource. Keywords: Polycyclic aromatic hydrocarbons; Environmental sustainability; Contamination; Orogodo river.

The persistent problem of industrial effluents causing heavy metal pollution in Nigeria's ground and surface waters has been a longstanding concern. To create inexpensive adsorbents that can remove heavy metals from wastewater, this study looks into using activated bamboo's biochar as a precursor. The objective of this study is to investigate the impact of variables (contact time, adsorbent dosage) on the adsorption efficiency of activated biochar in heavy metal removal from industrial wastewater. Utilising FTIR, SEM, and BET, the adsorbent was characterised. Atomic Absorption Spectrophotometry was used to analyse metallic elements. The adsorbate showed high levels of Fe3+, exceeding National Environmental Standard and Regulations Enforcement Agency (NESREA)) and World Health Organization (WHO) standards. Adsorbent dosage and contact time were two factors that were tested using batch adsorption. Maximum removal of Fe3+ was 87.721% at a 2g adsorbent dosage per 50 ml wastewater (40g/L wastewater). Fe3+ attained a maximum removal at 40 minutes. Pseudo-first order and Pseudo-second order kinetic models were analyzed and Fe3+ adsorption kinetics best fit into the pseudo-second-order kinetic model with a high R2 value of 0.9963 indicating a chemisorption mechanism. This study demonstrates the high efficiency of activated bamboo biochar for Fe³⁺ removal from industrial wastewater, highlighting its potential as an eco-friendly and cost effective alternative to conventional methods. Keywords: Adsorption, kinetics, activated biochar, bamboo, wastewater.

This paper presents the design and simulation of a Fuzzy Logic-based washing machine system tailored for visually impaired users. Visually impaired individuals often face challenges operating conventional washing machines due to the lack of accessible interfaces. To address this, a dual mode Fuzzy Logic control system was developed and simulated, offering both automatic and custom washing options. The system integrates two Fuzzy Logic controllers for enhanced flexibility and control. Simulation and system logic were implemented using MATLAB’s Fuzzy Logic Toolbox for modeling and Python for programmatic control. Key accessibility features include comprehensive audio feedback, flashing indicator buttons, tactile markings, and a simple, high-contrast physical layout. The system adheres to the guidelines set by the Research Institute for Disabled Customers (RIDC), aiming to make automated laundry services inclusive. Preliminary simulations indicate effective customization of water intake, detergent use, spin speed, and drying time based on user or sensor input, suggesting high usability for visually impaired users. Keywords: Washing Machine, Visually Impaired, Fuzzy Logic, MATLAB, Python

Source rock's quality is essential by providing information on their kerogen type and organic matter quality, type of organic matter and characteristics, thermal maturity of the organic matter, and hydrocarbon type. In order to ascertain the bulk composition of oils, organic matter inputs, source depositional environment, and thermal maturation in comparison, a geochemical investigation was conducted on seven crude oil samples (Q, R, S, T, U, V and W) from OML X. Saturates, Aromatics, Resin, and Asphaltene (SARA) bulk compositions were separated from the crude oil samples using ASTMD4124-09 test standards. ASTMD5853 was used to measure the pour point parameter for the flow assurance application, while ASTMD664 was used to measure the total acid number (TAN). The Wavelength dispersive X-ray fluorescence spectrometer (WDXRF) was used to measure the sulfur concentrations in accordance with ASTMD2622 guidelines. Using saturates extracts in selected ion monitoring (SIM), Gas chromatography- mass spectrometry (GC-MS) and Gas Chromatography- Flame Ionization Detection (GC-FID) were used to evaluate the biomarkers in accordance with ASTMD7169. The API values were between 10.90 and 16.10. The range of sulphur concentration in the crude oils showed that Q crude recorded the highest concentration, while S crude was found to be the least in concentration. The Saturates/Aromatic ratio for the crude ranges from 1.15 to 4.38, With W crude having the highest concentration and U crude having the lowest concentration. All seven oil wells produced paraffinic, undegraded (non-biodegraded) crude oil. The findings demonstrated that all of the crude oil samples were heavy crude and they came from source rocks that include type I and type II kerogen from a marine agal source which had been deposited in an anoxic environment. The seven crude oil samples that were chosen from the oil wells were all mature, with some displaying a higher level of maturity than the others, according to the biomarker metrics CPI, Pr/Ph ratio, Pr/n-C17, Ph/n-C18, TAR, etc. utilized for maturity indices. Since the crudes are paraffinic, non biodegraded and their features unchanged, we can reconstitute the nature of the source rock using the crude oil. Keywords: Mining, oil fields, SARA and biodegradation

Covering chemical processes, catalysis, and materials in petroleum refining

Research on environmental impacts and sustainability in the petroleum industry

Focused on natural gas processing, transportation, and utilization technologies

Research in petroleum geology, seismic analysis, and basin modeling

Research in petroleum engineering, drilling technologies, and reservoir management

Artificial Intelligence techniques are gaining interest for traffic management due to their potential to provide real-time insights, predictive analytics, proactive decision-making, and optimization of traffic flow. These technologies have improved real-time traffic monitoring, congestion management, traffic signal control, public transportation systems, urban planning, and infrastructure development. The study utilized primary data from 160 pedestrians/drivers and 19 traffic agencies, using a stratified sampling technique and employing descriptive and inferential statistics.The study found a strong positive correlation between Artificial Intelligence-based traffic management strategies and improved traffic management, with 69.7% of the variability explained by these strategies. The study concluded that Artificial Intelligence-powered solutions enhance traffic management efficiency, reduce travel times, improve road safety, and mitigate environmental impacts. It recommended the government invest in developing and deploying Artificial Intelligence infrastructure for traffic management in Lagos Metropolis. Keywords: Artificial Intelligence, Road Traffic Management, traffic monitoring, congestion management

Internet’s popularity alongside proliferation of smartphones as mode of data exchange for businesses as a great strategy for information sharing amongst users on a private company network. It has consequently also, attracted adversaries with proliferation of attacks to exploit unsuspecting users of resources for personal gain. Adversaries utilize socially-engineering attacks, to breach and gain unauthorized access to a compromised user device via subterfuge mode that can also deny such users of access to resources on a network. With denial-of-service carefully crafted to wreak havoc on network infrastructures, it has since become expedient to explore deep learning mode to predict such cases performance. We explore a scheme to effectively distinguish between genuine and malicious packets; And benchmarks our results using XGB, Random Forest, and Decision Tree. Result shows that our model yields F1 0.9945 that outperforms XGB, RF and DT with F1 of (0.9925, 0.9881 and 0.9805). Its Accuracy of 0.9984 outperforms XGB, RF, and DT with (0.9981, 0.9964 and 0.9815) respectively). Our model correctly classified 13,418 cases with 99.84% Accuracy with 283 cases incorrectly classified. Thus, model effectively differentiates genuine from malicious packets. Keywords: Intrusion. DoS, Transfer Learning, Korhonen Neural Net, Genetic Algorithm

This study primarily focuses on investigating a nanostructured material synthesized through the electrochemical deposition of zirconium sulfide (ZrS) doped with silver (Ag). The study focuses on understanding the influence of silver doping on the structural, optical, and electrical characteristics of ZrS nanostructures to enhance their potential for optoelectronic applications. The nanostructures were fabricated using a controlled electrochemical deposition technique, followed by structural characterization through X-ray diffraction (XRD) and scanning electron microscopy (SEM). The structural, morphological, and optical properties of the films were analyzed using: Bruker D8-Advance for structural properties, MIRA3 TESCAN for surface morphology, UV visible spectrophotometry (756S) for optical properties and measuring wavelength from 300 to 1100 nm. The 2θ values of (23.59, 34.91, 48.42, and 62.62) o are associated with diffraction planes (101), (103), (111), and (112). The slight shifts in peak positions seen in ZrS with Ag doping suggest changes in interatomic distances or lattice strain. The peak aligns with the crystal plane (101) of ZrS'. A stable and well-defined crystalline structure has been observed. Before Ag doping, the ZrS phase is observed with characteristic diffraction peaks corresponding to a specific crystalline structure (likely a hexagonal or orthorhombic phase). Adding Ag atoms disrupts the typical lattice arrangement in ZrS, leading to changes in phase stability and the formation of new phases at certain temperatures. Temperature affects grain size in both ZrS and Ag-doped ZrS. Atomic movement was restricted at 35°C, leading to the formation of smaller grains, while at 45°C, increased mobility led to grain growth. Increased temperatures boost the mobility of charge carriers, improving conductivity and possibly absorbance properties of the material. Due to its intrinsic electronic transitions, ZrS shows a unique absorbance profile at lower temperatures (35°C). At 45°C, shifts in absorbance peaks or changes in intensity occur because of variations in vibrational modes and energy levels. The bandgap energy of Ag-doped ZrS decreases as the temperature increases, with values of 1.98 eV, 1.57 eV, and 1.53 eV at 35°C, 40°C, and 45°C, respectively. The ZrS nanostructures exhibit a well-defined morphology, possibly nanosheets or nanorods. SEM images showed a uniform nanostructure in Ag-doped ZrS films, with increased grain size and roughness at higher doping levels. EDS confirmed Ag incorporation, and elemental mapping revealed its homogeneous distribution. The optical absorption spectra showed a shift in the absorption edge with increasing Ag doping, while Tauc’s plot revealed a corresponding reduction in band gap energy (Eg). This decrease in Eg suggests enhanced optoelectronic properties, making Ag-ZrS promising for photovoltaic and photodetector applications. Additionally, the increased absorption in the visible range indicates improved light harvesting capability. Keywords: bandgap energy; zirconium, silver, metals, semiconductors, temperature

A comprehensive journal covering all aspects of petroleum sciences and engineering.

Benzene Toluene, Ethylbenzene and Xylene (BTEX) often enter the aquatic ecosystem due to accidental oil spills, leakage and improper oil-related waste disposals. Toxicity studies in the aquatic ecosystem have shown that BTEX have adverse effects on aquatic biota and humans. Therefore, this study was conducted to examine the concentrations, potential risks and sources of BTEX in sediments of the Okpare creek in the Niger Delta of Nigeria. The ƩBTEX concentrations in the sediment ranged from 1.14 to 4.96 mg/kg. On average, toluene was predominant in the sediment. The upstream section of the Okpare creek had higher average concentrations of ∑BTEX than the mid- and downstream sections. The concentration of BTEX in the sediment were above the Nigerian Upstream Petroleum Regulatory Commission (NUPRC) target values. The values of the hazard index due to human exposure to the BTEX in the sediments were < 1 which indicated the absence of non-carcinogenic risk from BTEX exposure in the sediments. Moreover, the total cancer risk values were less than 1 × 10-6 also suggesting that there is the absence of carcinogenic risk to humans exposed to the BTEX in the sediments from the Okpare creek. The source identification indicated that the sources of BTEX in the creek were stationary sources like paints, solvents, gasoline and diesel spillage. Keywords: BTEX; ecosystem; non-carcinogenic; Niger Delta

Abstract Today, there is a global demand for efficient energy consumption utilizing energy-fit management solutions that are poised to deliver sustainable living practices with reduced consumption of energy therein. These in their own right- have also continued to raise germane concern tailored at environmental, health and consumption regulations – to yield global concern and priorities targeted at replacing traditional solutions with (alternate) intelligent, optimized models and strategies. Our study presents SEMAEco-IoT – designed with the microcontroller ESP and sensor to observe environmental conditions and energy consumption by home appliances. It utilizes machine learning algorithm to analyze total energy consumed by each appliance and delivers optimal consumption that reduces energy waste. The system tested across multiple parameters – yields effectiveness, reliability, and efficiency. Our use of ESP8266 and ThingSpeak is able to handle expansive inputs without significant delays or data losses. Results affirm its capability to maintain stable performance even with more device connected. Keywords: Intrusion. DoS, Transfer Learning, Korhonen Neural Net, Genetic Algorithm

Carpark management system employs a technology driven solution to manage how cars are parked at specific locations. Traditional car park management systems are faced with many challenges including difficulty in managing large volumes of data, potential security breaches, outdated payment methods and inefficient use of parking spaces. This paper presents a smart car park management system that integrates Radio Frequency identification (RFID) technology, Atmega328 microcontroller and infrared (IR) sensors to efficiently manage parking facilities. The system used RFID tags to identify vehicles, IR sensors to detect parking space occupancy and Atmega328 to process data and control the system’s operations. A custom C++ program was developed using Arduino Integrated Development Environment (IDE) to interface both with the RFID and the IR sensor. While the written code in the RFID generates unique codes assigned to every car registered with the facility, the written code in the IR sensor detects parking space occupancy. The system communicates through a mobile app called blynk IoT and digitally displays the information on the screen for users to check for available spots before arriving at the facility, reducing the time spent searching for parking spaces and alleviating traffic congestion. This system was evaluated through a pilot implementation on a section of the Petroleum Training Institute, Effurun. The system provides real-time parking space availability and enhanced security. The combination of RFID, Atmega328 and IR sensors enables accurate vehicle identification, efficient parking management and improved user experience. The system offers a reliable, efficient and secure solution for managing parking facilities, reducing congestion and improving customer satisfaction. Keywords: RFID, IR sensors, C++, blynk IoT, Arduino IDE

This study focuses on the practice of waste management in the construction industry in Nigeria, using a case study of Cakasa Nigeria Company during the construction of a tank farm from 2019 2022. The study analyzed waste data from consignment notes and found that metal waste (4370kg) was the most generated waste material on site, followed by concrete and bricks (3973 kg), timber (2850kg), masonry and plasterboard (2595 kg), and paper waste (1621 kg). Oil rags (16 kg) were the least generated waste. The factors influencing material waste production during construction were identified as design changes, theft and vandalism, poor site conditions, poor waste minimization strategy, poor procurement management, poor materials handling on site, and poor implementation of waste management plan. Measures to minimize construction material waste included proper site supervision and management techniques, adequate material storage, staff training and awareness, and proper procurement management. Keywords: Construction, waste management, metal waste, procurement plan, environmental sustainability.