The selective separation of lithium and cobalt from leachate solution is a critical challenge in lithium-ion battery (LIB) waste management. This study develops a detailed process simulation and techno-economic assessment (TEA) of a hydrometallurgical recycling process to recover lithium and cobalt from lithium cobalt oxide (LCO) cathode materials using sulfuric acid-based leaching. The process was simulated in Aspen Plus® V14, incorporating stoichiometric reaction blocks with electrolyte-equilibrium chemistry to accurately model metal dissolution, aqueous speciation, separation, and precipitation. The simulation results in 99.89 % cobalt sulfate (CoSO4) purity and 98.93 % lithium carbonate (Li2CO3) purity, respectively. An economic analysis, considering both capital and operating costs, resulted in a positive net present value (NPV) of $36.72 million over a 20-year project duration for a 100 kg/hr feed. This suggests economic potential under the base-case performance scenario. Sensitivity analysis showed that profitability is highly sensitive to both product price realization and recovery efficiency. This study serves as a foundation for future process optimization and comparative evaluation of new recycling technologies.
Food fraud and mislabeling represent persistent threats to food safety and public health, particularly in Sub Saharan Africa where fragmented supply chains, dominant informal markets, and limited regulatory capacity increase vulnerability to adulteration and misrepresentation. The growing complexity of global food systems has intensified the need for reliable authentication and traceability tools as integral components of preventive food safety frameworks. This review critically examines the current landscape of food authentication technologies in Sub Saharan Africa, with emphasis on their analytical performance, practical applicability, and integration into regulatory control systems. A systematic narrative review approach was adopted, guided by structured evidence synthesis principles. Peer reviewed literature published between 2010 and 2026 was analyzed across major scientific databases, focusing on spectroscopic, chromatographic, molecular, and isotopic analytical methods, alongside digital traceability systems and artificial intelligence-driven data analytics. The review employed thematic and comparative synthesis to evaluate technological effectiveness, adoption patterns, and regulatory relevance. The evidence indicates that spectroscopic techniques enable rapid screening, while chromatographic and molecular methods provide high precision confirmatory analysis. Isotope ratio analysis supports origin verification, while digital traceability systems, including blockchain-based ledgers, improve supply chain transparency, while artificial intelligence and machine learning tools function as analytical decision-support systems that enhance pattern recognition and predictive analytics for fraud detection. However, significant limitations persist, including inadequate context specific validation, high costs, limited infrastructure, and weak integration into preventive food safety systems. Technology adoption remains uneven, with concentration in urban laboratories and limited coverage in informal markets. Strengthening food authentication in Sub Saharan Africa requires coordinated investment in low cost technologies, context specific validation frameworks, and regulatory harmonization. Integrating authentication into risk-based food safety systems is essential for improving surveillance, enhancing consumer protection, and aligning regional practices with international standards.
The fluid catalytic cracking (FCC) process plays a critical role in maximizing gasoline yield; however, its operation is highly complex due to nonlinear process interactions in feedstock composition. In the present study, a data-driven, ensemble learning framework is developed to enhance gasoline yield prediction accuracy. A three-stage feature selection strategy integrating the Variance Inflation Factor (VIF), Recursive Feature Elimination with Cross-Validation (RFECV) and Sensitivity Analysis was employed to identify the most informative variables prior to model development. Particle Swarm Optimization (PSO) was used for hyperparameter optimization of the proposed Extremely Randomized Trees-based model (XRTR). Model performance was evaluated against baseline models, and the proposed VIF–RFECV–PSO–XRTR framework demonstrated superior predictive performance for improved gasoline yield. In single train–test evaluation, the model achieved R2 = 92.26%, RMSE = 0.3750, and MAE = 0.1008. Cross-validation further confirmed its robustness, with R2 = 92.20% (±0.55) at k fold = 10.
This review investigates scalability, environmental performance, and socio-economic sustainability of biochar production in sub-Saharan Africa with a focus on traditional/low-tech solutions. Low-tech methods, including open pits, metal drum kilns, and earth mounds, are easily accessible and reasonably priced, but they can have high emissions, uneven biochar quality, and poor thermal efficiency. Standardizing enhanced low-technology designs, putting strong quality assurance systems in place, mobilizing underutilized biomass streams, and using digital monitoring and artificial intelligence to optimize pyrolysis operations are some of the main strategies proposed to achieve scalable and sustainable biochar production in the region. Retort kilns or sophisticated pyrolysis units with gas recirculation are considered medium-to-high technology to stabilize product qualities, lower emissions, and call for a moderate amount of capital and technical know-how. Along with evaluating feedstock possibilities such as invasive species, forestry wastes, and crop residues, the review incorporates techno-economic analysis, climate-smart forestry tools, and circular economy principles. Economically viable biochar systems in Sub-Saharan African contexts require either (i) integration with existing agro-industrial waste streams (e.g., rice mills, sugarcane processing) where collection infrastructure already exists, or (ii) decentralized production using low-cost kilns (< US500 capital cost) that eliminate transport through on-farm processing. To align waste management and forest conservation with goals for negative carbon soil enhancement, the paper recommends policymakers support standardized low-technology designs, create frameworks for ensuring the quality of biochar, increase the use of underutilized biomass streams, and use digital monitoring and artificial intelligence to optimize pyrolysis processes.
Post-traumatic stress disorder (PTSD) is a debilitating neuropsychiatric condition marked by cognitive deficits and neurobiological disturbances, including oxidative stress, neuroinflammation, cholinergic dysfunction, hypothalamic–pituitary–adrenal (HPA) axis dysregulation, and apoptosis. This study investigated the protective effects of silymarin against single prolonged stress (SPS)-induced PTSD-like changes in mice. Mice were exposed to a triple stress model consisting of restraint stress, forced swim test and brief ether-induced loss of consciousness, and left singly for 7 days of isolation. Animals were assigned to control (saline 10 mL/kg, p.o), SPS, SPS + silymarin (25, 50, 100 mg/kg, p.o), and SPS + fluoxetine (10 mg/kg, p.o) groups for 21 days. Behavioral performance was assessed using the novel object recognition, Y-maze, and open field tests. We quantified serum corticosterone concentration. Oxidative stress and immune markers, viz malondialdehyde, nitrite, reduced glutathione, superoxide dismutase (SOD), catalase, glutathione-S-transferase (GST), myeloperoxidase (MPO), and acetylcholinesterase (AChE) were assayed in the prefrontal cortex, hippocampus and striatum. Neuronal cell death and caspase-3 expression were evaluated using hematoxylin and eosin staining and immunohistochemistry. SPS exposure impaired recognition, working memory, and reduced locomotor activity, which were reversed by silymarin. Silymarin reduced SPS-induced increased malondialdehyde and nitrite levels, accompanied by increased glutathione, GST, SOD and catalase levels in the prefrontal cortex, hippocampus and striatum, respectively. Silymarin treatment attenuated MPO and AChE activities in the prefrontal cortex, hippocampus and striatum. Silymarin modulated caspase-3 expression in a brain-region-dependent manner, notably inhibiting it in the dentate gyrus of the hippocampus, increasing it in the prefrontal cortex, and preserving neuronal cytoarchitecture in all regions. These findings suggest that silymarin provides broad neuroprotection in SPS-induced PTSD through apoptosis modulation, enhancing antioxidants and anti-inflammatories, with upregulated cortical cholinergic transmissions and inhibition of corticosterone release.
