Federal University of Petroleum Resource Effurun

The predictions made by reverse modelling are valid, but they are slightly inferior than those produced by forward modelling because numerous permutations of input variables lead to identical outcomes due to input variable interaction. Artificial neural networks (ANNs) require huge data sets for training to make accurate predictions. The 1000 input-output data sets for training were obtained from experiments and artificially generated random data sets after varying input variables between their respective levels to predict corresponding outputs through derived empirical equations using central composite design (CCD). The back propagation neural network (BPNN) and genetic algorithm–neural network (GA-NN) were established for performing forward and reverse modelling tasks. Forward modelling aims to predict outputs such as performance (brake thermal efficiency: BTE and brake-specific fuel consumption: BSFC), emissions (carbon monoxide: CO, nitrogen oxide: NOx, unburnt hydrocarbon: UHC) characteristics with a set of input variables such as engine load (EL), blend type (BT), injection pressure (IP), and compression ratio (CR). The reverse modelling tasks were performed to predict input variables (EL, BT, IP, and CR) for the desired performance (BSFC and BTE) and emission (NOx, UHC, and CO) characteristics. The parametric study of BPNN and GA-NN methods reduced mean squared error (MSE) to 0.00086 and 0.000738 for forward modelling and 0.00487 and 0.00212 for reverse modelling tasks. The prediction performances were evaluated with 20 random experimental cases for all outputs, resulting in 3.198% for CCD, 2.578% for BPNN, and 2.056% for GA-NN for forward modelling, as well as 3.83% for BPNN and 3.03% for GA-NN for reverse modelling. GA-NN produced better predictions for both forward and reverse directions due to its global search capability. Predicting outputs for unknown parametric conditions with forward models and predicting input variables for desired outputs with reverse models helps novice users automate the process without needing practical experiments.

The use of alternative fuels in internal combustion engines has significantly increased due to their promising characteristics. Many researchers use computational analysis, a reliable technique that provides accurate results comparable to experimental findings, to analyse engine performance, combustion, and emission characteristics. The cost of converting feedstock into biodiesel ranges from 70% to 95%. Garcinia Gummi-Gutta (GGG) is a commercially valuable spice tree that helps reduce biodiesel production costs, generating Rs. 82,666 annually for farmers. The oil yield from GGG seeds was 18% for boiling, 23% for mechanical expeller, and 42% for solvent extraction methods, with physical properties meeting ASTM biodiesel standards. The crude oil from GGG seeds had a free fatty acid content of 33.56%, necessitating esterification followed by transesterification. GC-MS and FTIR analyses confirmed biodiesel conversion and the design of experiments (DOE) explored process kinetics, resulting in 96.2% biodiesel yield under optimized transesterification conditions using teacher-learner-based optimization (TLBO). The addition of silver oxide nanoparticles increased biodiesel yield to 97.8%, maintaining 90.1% after six reuse cycles. The estimated cost of biodiesel conversion is $0.8393. Three biofuel type blends (B10, B20, and B30) were prepared for use, with computed overall uncertainty in experiments of ±3.15%. The experiments were conducted with four varying variables: engine load (EL), BT, injection pressure (IP), and compression ratio (CR). The researchers evaluated the performance (brake thermal efficiency: BTE, and brake-specific fuel consumption: BSFC) as well as the emissions (carbon monoxide: CO, nitrogen oxide: NOx, unburnt hydrocarbon: UHC) characteristics. The models developed for emission and performance characteristics showed a high coefficient of determination, indicating their statistical adequacy for prediction and optimization.

Unconventional production techniques significantly affect reaction parameters and sustainable biodiesel production. Achieving a sustainable and resilient energy future involves feedstock selection, oil extraction, and biodiesel production. A comprehensive analysis of the several phases is necessary to lower the overall economics of biodiesel production. Mechanical pressing, solvent extraction, and boiling are common oil extraction industrial methods, each with notable benefits and drawbacks. Key criteria for selecting extraction methods include seed type, oil yield, product quality, economic viability, environmental impact, scale of production, energy efficiency, speed, by-product use, and raw material availability. The triglycerides in extracted oil and fats are transformed into biodiesel using transesterification, hydroprocessing, pyrolysis, catalytic and reactive distillation, micro-emulsion, supercritical fluid method, etc. The advantages and limitations of each method were discussed. Holistic assessment on feedstock availability and suitability, reaction kinetics, conversion efficiency, catalyst selection, glycerol separation, scale-up potential, environmental impact, and biodiesel purity ensures easy selection of cost-effective biodiesel production methods that offer high yield with minimized energy consumption and waste. Transesterification is a widely practised biodiesel production technique in literature for high-quality biodiesel production from diverse feedstock. Transesterification factors such as methanol-to-oil molar ratio, catalyst loading, reaction time and temperature, and stirring speed influence the biodiesel conversion efficiency, quality, and cost. The methods practised for experimentation and analysis are discussed, and their advantages and limitations are highlighted. Design of experiments (DOE) helps reduce overall experiments and draw meaningful conclusions with a systematic study of parametric (both individual and interaction factors) analysis. Artificial intelligence (AI) tools are applied to model and analyse input-output (transesterification parameters-biodiesel yield, engine parameters-performance, and emission characteristics) relationships and optimize them by leveraging computers based on human intelligence. The biodiesel research experimentation, modelling, analysis, and optimization framework were established using DOE and AI tools.

Researchers worldwide are investigating alternative energy sources to meet the increasing energy demand due to urbanization and population growth. Renewable energy sources (RESs) like hydro, wind, solar, geothermal, and biomass offer sustainable and environmentally friendly solutions to replace fossil fuels. However, challenges related to cost, regulations, technology, and climate impacts (such as floods, droughts, and windstorms) make large-scale energy production difficult to commercialize. Biomass energy, which produces electricity from organic resources like waste or plant remnants, currently provides 70% of RESs 14% share of global energy demand. By-products like biofuel are projected to meet 14% of global transportation energy by 2050, up from 4% in 2020, highlighting the growing importance of biofuels in the energy mix. Biofuels offer numerous advantages over fossil fuels, such as including powering internal combustion engines without modification, reducing greenhouse gas emissions and pollutants like Nitrogen Oxides (NOx), Carbon Monoxide (CO), and Sulphur Oxides (SOx), being biodegradable and environmentally friendly, boosting farmers’ incomes, improving social welfare, and reducing reliance on imported oil. Feedstock typically makes up 70–95% of total biodiesel production costs. The transition from Generation 1 to Generation 4 feedstocks has brought both advantages and limitations, prompting researchers to focus on more economically viable options for biodiesel production. Challenges associated with Generation 3 and 4 feedstocks for biodiesel production include inefficient oil extraction technologies and high production costs. The rising cost of food-based products and the food vs. fuel debate have discouraged the use of Generation 1 feedstocks. Generation 2 feedstock, including non-food crops and non-edible seeds with over 30% oil content, are well-suited for cost-effective biodiesel production. Utilizing region-specific, low-cost feedstocks with higher oil content can further decrease production costs, making second-generation feedstocks a sustainable, technical, and economical option for biodiesel production.

Medicinal plants have been utilised to treat illnesses and maintain good health. Traditionally, Allophylus spicatus Radlk (Sapindaceae) has been used to treat wounds, diarrhoea, cough, colds, dysentery, and various heart conditions. It has strong ethnopharmacological applications. Gas Chromatography-Mass Spectrometry (GC–MS) was used to analyse the essential oils (EOs), which were isolated via hydro-distillation. The 2, 2-diphenyl-1-picrylhydrazyl (DPPH) method was used to assess the antioxidant activities. The essential oils isolated from the leaves, twigs, and roots contained 30, 32, and 21 compounds overall, accounting for 89.5%, 95.8%, and 82.5% of the total compositions, respectively. β-elemene was the major constituent of volatile phytochemicals found in leaves, twigs, and roots, with percentage compositions of 15.2%, 20.5%, and 14.8%, respectively. Compared to α-tocopherol (0.546 mg/mL), a well-known antioxidant utilised as one of the standards at concentrations between 1.0 and 0.0625 mg/mL, the EOs demonstrated greater free radical scavenging activities (IC50 = 0.4746 mg/mL, 0.4172 mg/mL, and 0.4156 mg/mL). EOs from leaves, twigs and roots of A. spicatus and antioxidant activities is studied for the first time.

The application of agrochemicals such as organophosphate pesticides (OPPs) has several benefits in agriculture but also poses great risks to the environment and human well-being. Thus, this study was conducted to determine the concentrations, distribution pattern, relationships, potential risks and sources of OPPs in agricultural soils and vegetables from Delta Central District (DCD) of Nigeria to provide useful information for pollution history, establishment of pollution control measures and risk management. Fourteen OPPs were determined in the soil and vegetables using a gas chromatograph-mass selective detector (GC-MSD). The ∑14 OPPs concentrations varied from 5.29 to 419 ng g⁻¹ for soil and 0.69 to 130 ng g⁻¹ for vegetables. On average, pirimiphos methyl (23.8 ng g⁻¹) and diazinone (4.74 ng g⁻¹) were the dominant OPPs in soils and vegetables respectively. The cumulative ecological risk assessed using the toxicity-exposure-ratio (TER) and risk quotient (RQ) approaches revealed that there was a high risk of OPPs to soil organisms. The increasing order of OPPs toxicity to the soil organisms was chlorpyriphos < fenitrothion < diazinone < pirimiphos methyl while the cumulative human health risk suggested there was adverse non-carcinogenic risk for children but not for adults exposed to OPPs in these agricultural soils and vegetables.

Transformer leakage, industrial air deposition, open burning of electrical and electronic equipment, and biomass combustion using incinerators are the main sources of PCBs in Nigerian soils. PCBs’ strong adsorptive qualities allow them to stick to soil particles for long periods of time. Because of their near proximity, PCBs can be ingested, inhaled, or come into touch with skin. PCB exposure has been linked to a wide range of deleterious health effects, including neurotoxicity, mutagenicity, and carcinogenicity. This study in South-West and South-Southern Nigeria examined the levels of polychlorinated biphenyls (PCBs) and potential health concerns in urban and rural soils. Soil samples were collected and analysed using Gas Chromatography-Mass Spectrometry (GC-MS) at six locations in Nigeria’s south and southwest. The study then evaluated the risk associated with human interaction with the soil using health indices such as the Hazard Quotient (HQ), Hazard Index (HI), and Incremental Life Cancer Risk (ILCR) from human unconscious ingestion, inhalation, and skin contact with contaminated soil. Each soil sample contained 17 PCBs, including one dioxin-like congener. The PCB concentrations in the soil samples ranged from 5.87 to 46.12 ng/g, following this pattern: Apata > Apete > Bomadi > Ozoro > Ayeka > Itokin. The toxic equivalent (TEQ) value of PCB-189, the only dioxin-like PCB discovered in the soil sample has a TEQ of 4.2 × 10−6, exceeded the World Health Organization’s permissible limit for dioxin-like PCBs (10−4 -10−6). Ingestion, skin contact, and inhalation risks ranged from 1.95 × 10⁻⁶ to 8.73 × 10⁻⁶, 5.55 × 10⁻⁸ to 8.58 × 10⁻⁷, and 2.28 × 10⁻¹⁷ to 3.93 × 10⁻¹⁴, respectively. Except for ingestion, these values were below the incremental lifetime cancer risk threshold (10⁻⁶), indicating that PCBs pose minimal health risks.

Industrial wastewater treatment is crucial for environmental protection and public health. This study aimed to investigate the efficiency of the coagulation-flocculation-aided adsorption (C/F-A) system utilizing aluminum salt (AS) coagulant and characterized acid-activated kaolin clay adsorbent (KC) for the removal of pollutants from vegetable oil processing industrial wastewater (VOPIW). The objectives were to optimize the operational parameters of the C/F-A system, evaluate the adsorption capacity of KC, analyze the removal mechanisms, and assess the feasibility of scale-up for industrial applications. Batch experiments were conducted at 25 °C and pH 6–8 to determine optimal conditions for turbidity and total suspended solids (TSS) removal. The Smoluchoski kinetic model and various isotherms (Redlich-Peterson, Elovich, and Dubinin-Radushkevich) were employed for mechanistic analysis. Optimal conditions of 0.2 g/L dosage, pH 6, and 12 min settling time resulted in 96% turbidity and 97% TSS removals. Significant reductions were achieved for various pollutants, including Cu (84%), Fe (80%), Mn (85%), Pb (71%), and Al (98%). The sorption capacities of KC for various pollutants were determined, with the highest recorded for Cu at 35.47 mg/g C. Scale-up analysis was conducted to meet WHO effluent discharge requirements resulting in organic loading corresponding to TDS (2.94 × 10⁹ mg/day), DO (5.1 × 10⁸ mg/day), BOD (4.33 × 10⁸ mg/day), and COD (3.99 × 10⁸ mg/day). The mechanistic parameters confirmed an optimum sweep-flocculation constant, 6.2 × 10⁻³ L/g·min, and half-life, 101 min⁻¹. The study highlighted the effectiveness of the C/F-A system using KC for removing contaminants from VOPIW, suggesting its potential as a cost-effective and sustainable method for industrial wastewater treatment, thereby aiding environmental protection. Graphical Abstract

Phytochemical screening of plant extracts is a promising approach that therapeutically examined bioactive compounds in various plant species. The present study was carried out to screen and identify the bioactive phytocompounds present in the rhizome of Crinum jagus. Phytochemical screening revealed the presence of alkaloids, steroids, tannins, flavonoids, saponins, terpenoids and glycosides. Gas chromatography mass spectrometry (GC-MS) results present a diverse array of compounds. Each compound was identified by its molecular formula, molecular weight, retention time, and peak area percentage, a total of ten (10) major compounds: Mesitylene, Naphthalene, 1-methyl, Dibutyl phthalate, Linoleic acid ethyl ester, Bis(2-ethylhexyl) phthalate, 4-(4-Methoxyphenyl)-1-butanol, 1,3-Benzenedicarboxylic acid, 2-(4-Methoxyphenyl) ethanol, Squalene and Cyclononasiloxane octadecamethyl and ten (10) most bioactive compounds: Alpha. -Terpineol, Piracetam, Dichloroxylenol, 2,4-Di-tert-butylphenol, 1,1'-Biphenyl, 3,3',4,4'-tetramethyl, Cholest-5-ene, 3-methoxy-, (3. beta.), Bis(2-ethylhexyl) phthalate, Stigmasterol, Gamma. -Sitosterol and Ergosta-4,22-dien-3-one were identified from n-hexane and ethyl acetate fractions respectively. Phytochemical screening revealed the presence of phyto-compounds that have demonstrated some biological activities. These compounds are recognized for their diverse biological activities, including antimicrobial and anti-inflammatory properties. Hence, their consistent of detection across different solvents suggests that C. jagus could be a valuable source for pharmacological research due to its diverse phytochemical composition.

The aim of the study is to examined fertilizers obtained from poultry manure (PM), poultry manure biochar (PMB), inorganic fertilize (IF), poultry manure + poultry manure biochar (PM + PMB), poultry manure + inorganic fertilizer (PM + IF), poultry manure biochar + inorganic fertilizer (PMB + IF), poultry manure + poultry biochar + inorganic fertilizer (PM + PMB + 6 IF) on reduction of petroleum hydrocarbon pollutant and nutrients retention of petroleum hydrocarbon polluted soil. Unpolluted sandy loam soil used for study was collected from farm land in Mosogar, Delta State, Nigeria using standard procedure. The soil was sparked with weathered crude oil of 3 different concentrations, 2%, 4%, 6%. The polluted soil was treated with fertilizers, the physico-chemical properties of unpolluted soil (US) and fertilizers was analyzed for pH, nutrients and petroleum hydrocarbon (PHC). The same analysis was also performed on polluted soil. The results of the study revealed that PM and PMB contained remarkable value of nutrients, the results also revealed that interaction of fertilizers on petroleum hydrocarbon polluted boost the pH and nutrients value at the first 21 days of incubation while after 21 days the pH and nutrients value of study polluted soil began to decrease. In addition, PM + PMB fertilizer retained highest value of nutrients after 63 and 84 days incubation period, in addition PM + PMB also displayed over 94% reduction of petroleum hydrocarbon pollutant. Therefore, this study has proved that PM + PMB is a fertilizer of choice to address petroleum hydrocarbon polluted.

The study examined the extraction of bio-oil from pumpkin seed and compared the optimization of the production of Fatty acid ethyl ester (FAEE) via the transesterification process using Response Surface Methodology (RSM), and Artificial Neural Network (ANN). This research uniquely highlights the utilization of pumpkin seed oil, a non-edible and sustainable feedstock, and combines RSM and ANN methodologies to enhance the precision of biodiesel optimization. The transesterification experiment was conducted at 60 min reaction time under varying temperature ranges, catalyst weight, stirrer speed, and ethanol-oil molar ratio. The RSM optimized conditions for maximum production were determined to be a 1.3% catalyst concentration, 6:1 ethanol-to-oil molar ratio, 50 � C temperature, and 550 rpm stirrer speed, resulting in a 90% biodiesel yield. The statistical evaluation metrics confirmed the neural network predictions were compromised to 80% yield due to limitations such as insufficient data size and the inherent complexity of the ANN model. The biofuel produced satisfied ASTM specifications peculiar to sustainable environmental applications. The mechanistic parameters revealed variations in thermodynamic stability and feasibility across reaction orders (zero, pseudo-first, and pseudo-second), emphasizing the temperature-dependent effects of the transesterification kinetic pathway on yield, design, and efficiency. HIGHLIGHTS � Combined RSM and ANN was used to optimize biodiesel production, achieving a 90% yield � Pumpkin seed oil was utilized as a non-edible sustainable biodiesel source � Pseudo-second-order reaction is the most efficient for KOH-based biodiesel conversion � 35.63 m 3 plug flow reactor was designed for continuous biodiesel production � Produced biodiesel met ASTM standards, for sustainable energy applications ARTICLE HISTORY

Magnesium Nanoparticles (MgNPs), are biocompatible and have shown promise in various biomedical applications, including antimicrobial and antimalarial treatments. Synthesis of magnesium nanoparticles from crude extract and isolated compound of crinum jagus rhizome and their antimalarial activity were reported. Magnesium nanoparticles mediated by crude extract and isolated compound were characterized by UV-visible spectroscopy, SEM and TEM analyses. The UV-visible absorption results of the magnesium nanoparticles synthesized from the crude extract showed absorption that varies slightly across the wavelength range of 343 nm to 353 nm, with a peak absorption value of 1.52934 at 345 nm. UV-visible absorption data for the magnesium nanoparticles synthesized from the isolated compound (lupeol) shows significant absorption in the range of 343 nm to 353 nm. The absorption values are relatively high, with a peak at 345 nm where the absorbance is 0.88005. MgNPs synthesized from the crude extract exhibited the best antimalarial activity (IC50 = 0.1310), significantly outperforming both the lupeol-based MgNPs (IC50 = 0.9103) and chloroquine (IC50 = 0.2762). The enhanced activity of the crude extract-based MgNPs may be attributed to the synergistic effects of multiple bioactive compounds present in the crude extract. The antimalarial activity observed in this study highlights the potential of combining traditional plant-based medicine with nanotechnology. The significantly lower IC50 values (0.1310) for the crude extract MgNPs compared to chloroquine (0.2762) demonstrate the promising future of this approach in overcoming drug resistance and improving the efficacy of antimalarial treatments.

The engine performance (brake thermal efficiency: BTE and brake-specific fuel consumption: BSFC) and emission characteristics (carbon monoxide: CO, nitrogen oxide: NOx, unburnt hydrocarbon: UHC) were reliant on engine parameters such as engine load (EL), blend type (BT), injection pressure (IP), and compression ratio (CR). Optimizing these parameters helps improve engine efficiency and reduce pollutants released into the atmosphere. Optimization is often complex due to conflicting requirements such as maximizing BTE and minimizing BSFC, CO, NOx, and UHC. Empirical equations with a better coefficient of determination were used as an objective function for optimization. No universal algorithm has been defined yet to produce the best-optimized conditions satisfying all applications. Six meta-heuristic search algorithms, including the JAYA Algorithm, Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Teaching–Learning-Based Optimization (TLBO), Election Optimization Algorithm (EBOA), and Driving Training Optimization (DTBO), were used to conduct the optimization task. A parametric study was undertaken to tune algorithm-specific and common parameters before optimization. The algorithm performances were evaluated for global desirability value and computation time. Six cases were considered with equal weight fraction for five responses (1/5 = 0.2) corresponding to case 1, cases 2–6 assigned with maximum weight fraction (0.6) for individual output with the least weight for the rest (0.1) towards BTE, BSFC, CO, UHC, and NOx. DTBO and EBOA converged to a global fitness function value of 0.94, with DTBO being more computationally efficient than EBOA. The conditions determined by DTBO and EBOA correspond to case 2 (maximum weight fraction (i.e. 0.6) for BTE, with a minimum weight fraction of 0.1 maintained for BSFC, CO, UHC, and NOx) and are recommended as optimal ones that satisfy all outputs resulting in an average absolute percent deviation in prediction equal to 4.68% experimentally. Ag2O nanoparticles in biodiesel, fuelled by diesel engines, were found to increase by 2.77% for BTE and decrease by 5.41% for BSFC, 4.72% for CO, 2.53% for NOx, and 4.20% for UHC, respectively.

Garcinia indica (GI) feedstock poses high oil content (45.2%) and after transesterification resulted with 94.8% yield. The GI crude oil, biodiesel, and their blends were tested for fuel characteristics and run in a diesel engine. Response surface methodology based on central composite design experimental matrices was used to model and examine the input variables (engine load, injection timing, injection pressure, and blend type) on engine performance (brake thermal efficiency (BTE), brake specific fuel consumption (BSFC)) and emission characteristics (nitrogen oxide (NOx), unburnt hydrocarbon (UHC), carbon monoxide (CO)). All factors showed significant effects (except injection pressure and injection time for NOx) on all responses. The empirical equations predicted 27 experimental cases with 4.75% accuracy. Desirability function approach was applied to transform all output functions (maximize BTE and minimize BSFC, CO, NOx, and UHC) with different weight fractions (WF) to single composite desirability function for maximization. Teaching learning-based optimization (TLBO) determined optimal condition corresponding to case 4 (maximum WF to CO, minimum WF to BTE, BSFC, UHC, NOx) resulting in highest desirability function value (0.9432) with a percent deviation of 7.09%. The developed models assist novice users in predicting unknown parametric conditions and improving engine performance and emission characteristics without practical experiments. ARTICLE HISTORY

The spatio-temporal distribution, source apportionment, and risks of polycyclic aromatic hydrocarbon (PAHs) were investigated in the Sombreiro River Estuary, Niger Delta, Nigeria. Water, sediment and oysters were obtained from the estuary and analyzed for 16 priority PAHs using gas chromatograph coupled with mass selective detector after extraction. The levels of the ∑16-PAHs in the water, sediment and oyster ranged from 25-10079 µg/L, 495-12811 µg/kg and 489-10823 µg/kg respectively for all locations and seasons. The results showed significant spatio-temporal variations in PAHs concentrations in all the matrices. The ecological risk assessment revealed high risk posed by the PAHs level to the estuarine ecosystem. The health risk indicated unacceptable carcinogenic risk to human via ingestion of oysters. Besides, for water and sediments, both ingestion and dermal pathways indicated unacceptable cancer risk. Source apportionment suggested that the PAHs in the estuary were from both pyrogenic and petrogenic sources. .

Existing ecofriendly apprehensions about climate change have directed scientists to discover plant-based vegetable oils for use as fuels, such as straight vegetable oils and their biodiesels, because of their renewability, nontoxic nature, biodegradability, and environmental friendliness. This experimental study intended to reveal the tribological aspects of 90 °C preheated Jatropha curcas straight vegetable oil (PHSVO90) used in a 7.35 kW, 1000 rpm constant speed indirect injection (IDI) diesel engine and likened to conventional diesel operation by conducting an elongated term durability examination for 512 h as per IS:10000 standards. Tests were performed under encoded loading cycles in two segments: one with PHSVO90 and the second one with conventional diesel operation (CDO). Following the completion of these tests, all essential engine components were disassembled to assess physical deterioration and the accumulation of carbon deposits. Due to their different chemical compositions, PHSVO90 and diesel showed noticeable differences in their properties. PHSVO90 had a higher amount of carbon deposits, measuring 6.3 g. It also had a total acid number (TAN) of 9.11 mg KOH/g, a density of 986 kg/m³, and a viscosity of 381 cSt. In addition, an inductive plasma-based atomic emission spectroscope detected a greater amount of metal wear debris from spectrum metals with PHSVO90. The elements Fe, Cr, Al, Cu, Mg, and Pb exhibited concentrations 1.49×, 1.37×, 1.19×, 1.37×, 1.2×, and 1.18× higher, respectively, than those recorded during typical diesel operation. Wear of PHSVO90 fuel engine components like the cylinder head, cylinder bore/liner, piston, Gudgeon pin, small end bush of the connecting rod, big end bearing, crankshaft bearing, connecting rod bearing, and inlet and exhaust valves, along with guides, was detected to be marginally advanced compared to CDO. As per identified results, an appropriate maintenance protocol was developed for the PHSVO90 application. It is observed that, without any extensive modifications, PHSVO90 oil has proven to be a good substitute for IDI engines.

The rising prevalence of elements in surface water is becoming a major global environmental problem due to their resistance to change and capacity to accumulate in organisms. Humans are causing significant heavy metal contamination in the aquatic environment, which is killing aquatic creatures. The purpose of this study is to determine the elemental composition of surface water in Ogun State. Water samples were collected from a river that flows alongside Liberty Estate in Ewupe, Ado-Odo Ota Local Government Area, and a stream that runs alongside Pacific Estate in Iganmode. The element composition of water samples was determined using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). Several components were found to exceed the limitations imposed by the World Health Organisation (WHO) and the Environmental Protection Agency (EPA). In River I, sodium (Na) had the highest apparent concentration, whereas titanium (Ti) had the lowest average concentration. In River II, silicon (Si) had the highest apparent concentration, whereas chromium (Cr) had the lowest average concentration. Titanium (Ti) had the lowest mean concentration value, whereas sodium (Na) appeared to have the highest concentration in stream I. Stream II has the highest concentration of sodium (Na), while Uranium (U) has the lowest average concentration value. Nonetheless, the incremental lifetime cancer risk (ILCR) values for adults and children who drink river and stream water are similar, ranging from 10–9 to 10–8. This implies a high carcinogenic risk (CR) linked with drinking water from both sources. Children who had access to both stream and river water demonstrated higher risk levels than adults. Routine studies of the state's streams and rivers should be conducted to avoid the subtle impact that may emanate from the water bodies.

Scientific data on the occurrence, distribution, risk and sources of endocrine disruption chemicals such as organochlorine pesticides (OCPs) in herbal medicines (HMs) is rare in Nigeria. Thus, this study was conducted to assess the levels, risks and sources of OCPs in HMs from Bayelsa State, Nigeria to ascertain their safety. Fifty HMs were obtained and analyzed for OCPs using a gas chromatograph combined with an electron capture detector (GC-ECD). The OCPs were detected in all the samples analyzed. The levels of ∑20 OCPs in the HMs ranged from 12.0 to 128 ng L−1 for liquid HMs, 21.2–112 ng g−1 for powder HMs and 26.0–72.7 ng g−1 for capsule HMs. The levels of OCPs obtained in these HMs were below their respective maximum residue limits (MRLs). Aldrin, γ-chlordane and β-BHC were the dominant OCPs in the liquid, powder and capsule HMs respectively. The values of both cumulative non-carcinogenic and carcinogenic risks for humans were < 1 and 1 × 10–6 respectively suggesting that there are no potential health risks via the ingestion of the HMs. The source identification revealed that OCPs in the HMs originated from historical and recent use confirming that the residues of these endocrine-disrupting OCPs are in continuous applications despite being banned. Although, the results generally indicated no current health risk implication to public consumption of the herbal medicines regarding OCP levels, however, we suggest a future risk assessment of susceptible groups, considering their concurrent exposure to all contaminants that have endocrine disrupting effects.

The worldwide exploration of the ethanolysis protocol (EP) has decreased despite the multifaceted benefits of ethanol, such as lower toxicity, higher oxygen content, higher renewability, and fewer emission tail compared to methanol, and the enhanced fuel properties with improved engine characteristics of multiple-oily feedstocks (MOFs) compared to single-oily feedstocks. The study first proposed a strategy for the optimisation of ethylic biodiesel synthesis from MOFs: neem, animal fat, and jatropha oil (NFJO) on a batch reactor. The project's goals were to ensure environmental benignity and encourage the use of totally biobased products. This was made possible by the introduction of novel population based algorithms such as Driving Training-Based Optimization (DTBO) and Election-Based Optimization (EBOA), which were compared with the widely used Grey Wolf Optimizer (GWO) combined with Response Surface Methodology (RSM). The yield of NFJO ethyl ester (NFJOEE) was predicted using the RSM technique, and the ideal transesterification conditions were determined using the DTBO, EBOA, and GWO algorithms. Reaction time showed a strong linear relationship with ethylic biodiesel yield, while ethanol-to-NFJO molar ratio, catalyst dosage, and reaction temperature showed nonlinear effects. Reaction time was the most significant contributor to NFJOEE yield.The important fundamental characteristics of the fuel categories were investigated using the ASTM test procedures. The maximum NFJOEE yield (86.3%) was obtained at an ethanol/NFJO molar ratio of 5.99, KOH content of 0.915 wt.%, ethylic duration of 67.43 min, and reaction temperature of 61.55 °C. EBOA outperforms DTBO and GWO regarding iteration and computation time, converging towards a global fitness value equal to 7 for 4 s, 20 for 5 s and 985 for 34 s. The key fuel properties conformed to the standards outlined by ASTMD6751 and EN 14,214 specifications. The NFJOEE fuel processing cost is 0.9328 USD, and is comparatively lesser than that of conventional diesel. The new postulated population based algorithm models can be a prospective approach for enhancing biodiesel production from numerous MOFs and ensuring a balanced ecosystem and fulfilling enviromental benignity when adopted.