Charcoal stoves constitute an essential energy provision for millions residing in sub-Saharan Africa; however, conventional designs exhibit inefficiency and pose health risks, contributing to approximately 3.2 million premature fatalities each year due to household air pollution. This systematic review consolidates advancements in charcoal stove technology, with an emphasis on enhancing thermal efficiency, minimizing emissions, and ensuring user safety. Utilizing a methodologically rigorous approach, a total of 52 peer-reviewed studies (1994–2025) were meticulously examined from databases such as Scopus and ScienceDirect, employing standardized testing protocols (e.g., Water Boiling Test). The findings indicate that innovative designs, including rocket and gasifier stoves, attain thermal efficiencies ranging from 17% to 87%, in contrast to the 11% to 16% efficiencies observed in traditional models, alongside reductions in carbon monoxide emissions by as much as 75% and a decrease in fuel consumption by 70%. Nonetheless, performance outcomes exhibit variability in practical applications, influenced by user behavior and the durability of materials employed. The review emphasizes the imperative for validation through field-based studies and the development of economically accessible designs to promote widespread adoption. These technological innovations hold the potential to provide sustainable cooking solutions, thereby contributing to public health and the achievement of environmental objectives such as Sustainable Development Goal 7.

Artificial Intelligence (AI) is revolutionizing industrial process automation by introducing intelligent decision-making and adaptive control to traditionally deterministic systems. In the petrochemical and oil & gas industries where safety, efficiency, and reliability are paramount, AI technologies such as machine learning, deep learning, and digital twins enhance plant operations through predictive maintenance, process optimization, and asset integrity management. Despite challenges in certification, data quality, and cybersecurity, AI continues to evolve as an indispensable enabler of smart and self-optimizing industrial plants. This research examines the integration of AI within programmable logic controllers (PLCs), distributed control systems (DCS) and supervisory control and data acquisition (SCADA) frameworks, the improvements it brings in efficiency, energy management, and maintenance scheduling as well as examines the real-world implementations from major automation vendors such as Honeywell, Emerson, Yokogawa and Siemens.

Nanotechnology has become the revolutionary movement in terms of promoting the global agenda of sustainable development to make innovations on the frontiers of materials science, environmental engineering, biotechnology and renewable energy. The tunable physicochemical properties, the high surface reactivity, and multifunctionality of nanoparticles are central to the development of sustainable solutions to the complex problems of resource scarcity, energy requirement, environmental degradation and human health. This review is a comprehensive study of multidisciplinary uses of nanoparticles in ensuring sustainability in various fields such as clean energy production, pollution mitigation, precision agriculture, green manufacturing, and biomedical uses. It is devoted to the latest successes of environmentally friendly production routes, in particular, bioinspired and waste-based nanoparticles, in accordance with the principles of green chemistry and the idea of a circular economy. The review also addresses the role of the nanoparticle-enabled technologies towards the United Nations of the Sustainable Development Goals (SDGs) by increasing energy efficiency, environmental stability, and sustainable production. Using materials innovation and sustainability science, this paper provides valuable critical reflections on how nanotechnology can make the transition to a more fair, low-carbon, resource-saving future. The discussion has pointed out the necessity of having a cross-disciplinary approach and regulatory vision to ensure a safe and ethical use of nanomaterials in sustainable systems.

Every year, the oil and gas sector spends billions of naira on transmission pipeline corrosion costs, which rise as pipeline networks age and deteriorate. As a result, pipeline operators must reconsider their approaches to corrosion prevention. Companies are being forced to create precise maintenance models based on failure frequency because of corrosion problems. Future line safety, lowering the frequency of failures, and cost-effective pipeline operation all depend on statistical techniques for modeling pipeline failures and making appropriate maintenance decisions. The present study predicted both the reliability and corrosion rate of crude oil pipelines by combining Monte Carlo simulation with degradation models. Corrosion was modeled using linear and power-law formulations that incorporated discrete random samples generated from inline inspection data. The degradation models were used to assess the mean time for failure (MTFF). The average corrosion rate (CRav) has a lower root mean square error (RMSE) than the largest occurrence projected random number (CRfreq), according to the TML shown against the RMSE of the predicted models. The RMSE for the degradation models ranged from 1.89 % to 17.02 %. This chart shows that the deterioration models correctly predicted the pipeline corrosion rate to be between 83.91% and 98.06%. Also, the Linear Model Law had the lowest recorded value of 1.98% and the most of 16.11%, while the Power Law degradation was the lowest at 1.88% and the most at 17.01%. When compared to the Monte Carlo Simulation value, which is 2.11 at the lowest and 1.01 at the highest, all of the findings fall between 1.89 and 17.02 percent. Consequently, the RMSE of the degradation models varied between 1.89 and 17.02 percent. Additionally, R2 for the Linear Model ranges from 0.925 to 0.990, but it ranges from 0.989 to 0.999 for the Power Model. According to the results, the degradation model has correctly predicted the field corrosion of the pipelines and will be a crucial tool for predicting when the pipelines will break.

The cost of corrosion-related transmission pipeline maintenance, which escalates as pipeline networks age and deteriorates, costs the oil and gas sector billions of naira every year. As a result, pipeline operators should reconsider their approaches to corrosion control. The present study employed the Monte Carlo Simulation model to forecast the rate of corrosion and dependability of pipelines carrying crude oil. The corrosion rate was predicted using a Linear and Power Law Model and discrete random numbers that were simulated from Inline Inspection Data. The study's conclusion demonstrates that the Monte Carlo simulation can forecast the pipelines' corrosion rate with an accuracy of 84.24–97.94%. From Monte Carlo Simulation results, a 2.01 lowest and 15.76 highest were obtained. Every value is within the range of 1.67% to 16.95%. The predicted number of failures is thus provided by the statistical models. Optimal maintenance decisions, risk analysis, and reliability analysis can all benefit from the statistical models' output.

The use of welded medium-carbon steels is increasing within the oil and gas industry, which requires these materials to resist corrosive environments, extreme temperatures and cyclic loading. This investigation analyzes the effects of Post Weld Heat Treatment (PWHT) upon the mechanical performance of manually arc-welded AISI 1035 (UNS G10350) steel. Rectangular test pieces were machined from carbon-steel piping that had been manually arc-welded using electrode type E6013. These samples were preheated to a temperature of 100 °C, then subsequently heat treated at 300 °C, 450 °C and 650 °C for 1 hour prior to cooling in still air. Mechanical performance of each specimen was assessed via tensile testing utilizing a Testometric machine and hardness assessment using Rockwell testing in accordance with ASTM E18. The results indicated an increase in tensile strength with increasing temperature during heat treatment and maximum tensile strength at 650°C; this was due to the formation of precipitated carbides and stabilization of the weld structure through heat treatment. The stress-strain plots illustrated an increase in flow stress that corresponded to a reduction in the elongation to failure for PWHT specimens; a trend indicative of a strength-ductility trade off. Decreases in Rockwell hardness relative to the as-welded condition were exhibited across all heat-treated conditions; this reflected both relief of residual stresses present within the heat affected zone and tempering of martensitic microstructures within this zone. Overall, the results indicate that heat-treatment parameters may be selected to decrease hardness (and therefore associated SSC/HIC risks), while simultaneously increasing strength for load bearing applications within oil and gas fabrication and/or repair operations involving AISI 1035 components. The relationship between PWHT parameters and material properties will aid in establishing practical guidelines for specification of PWHT within oil and gas fabrication and/or repair operations involving AISI 1035 components.