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This research aimed to evaluate the comparative biogas yields of waste (peels) of selected fibrous materials from the West African region: cassava, plantain, a mixture of cassava, plantain and yam. Three models: The Boyle model, the Modified Boyle’s model, and the Buswell and Müller’s model were used to determine the theoretical maximum biomethane potential (TBMP), while the Hohenheim Biogas Yield test (D-HBT) was used to undertake a batch test of anaerobic digestion. The samples were co-digested with digested sewage sludge (DSS) for 39 days, with an operating temperature of 37 ± 0.5 °C. The study draws comparisons between the TBMPs and the experimental results, the experimental results of the different substrates, and the experimental results and figures reported in the literature. From the experimental results, plantain peels had the highest biogas yield (468 ± 72 mL/g oTS), followed by a mixture of yam, cassava and plantain peels (362 ± 31 mL/g oTS) and cassava peels obtained the least biogas yield (218 ± 19 mL/g oTS). TBMPS of 204.04, 209.03 and 217.45 CH4 mL/g oTS were obtained for plantain peels, a mixture of yam, cassava and plantain peels and cassava peels, respectively, evaluated using Boyle’s model. For all the samples, the TBMPS (205.56, 209.03 and 218.45 CH4 mL/g oTS, respectively) obtained using the Buswell and Mueller model were slightly higher than those obtained by both the Boyle and the modified Boyle’s model (163.23, 167.22 and 174.76 CH4 mL/g oTS, respectively). While the study result is sufficient to imply that generating biogas from fibrous waste materials in its mixture form is a valuable approach, it is not sufficient to conclude that the use of these waste materials in its naturally occurring mixture form has a technical added advantage in co-digestion over their individual potential. However, future studies could explore this possibility with different fractions of the mixture with a view to optimising generation. The study finds that theoretically modelling the biogas potential of fibrous materials is a good method for biogas evaluation despite having overestimation tendencies, as this challenge could be corrected by applying factors that result in these tendencies, biodegradability indices. The data can, therefore, find use in fibrous waste treatment and waste-to-energy technologies, especially in Africa. This application will not be negatively affected by whether single waste streams are used or their mixture.

Developing clean and renewable energy instead of the ones related to hydrocarbon resources has been known as one of the different ways to guarantee reduced greenhouse gas emissions. Geothermal systems and native hydrogen exploration could represent an opportunity to diversify the global energy matrix and lower carbon-related emissions. All of these natural energy sources require a well to be drilled for its access and/or extractions, similar to the petroleum industry. The main focuses of this technical–scientific contribution and research are (i) to evaluate the global energy matrix; (ii) to show the context over the years and future perspectives on geothermal systems and natural hydrogen exploration; and (iii) to present and analyze the importance of developing technologies on drilling process optimization aiming at accessing these natural energy resources. In 2022, the global energy matrix was composed mainly of nonrenewable sources such as oil, natural gas, and coal, where the combustion of fossil fuels produced approximately 37.15 billion tons of CO2 in the same year. In 2023, USD 1740 billion was invested globally in renewable energy to reduce CO2 emissions and combat greenhouse gas emissions. In this context, currently, about 353 geothermal power units are in operation worldwide with a capacity of 16,335 MW. In addition, globally, there are 35 geothermal power units under pre-construction (project phase), 93 already being constructed, and recently, 45 announced. Concerning hydrogen, the industry announced 680 large-scale project proposals, valued at USD 240 billion in direct investment by 2030. In Brazil, the energy company Petroleo Brasileiro SA (Petrobras, Rio de Janeiro, Brazil) will invest in the coming years nearly USD 4 million in research involving natural hydrogen generation, and since the exploration and access to natural energy resources (oil and gas, natural hydrogen, and geothermal systems, among others) are achieved through the drilling of wells, this document presents a technical–scientific contextualization of social interest.

This study evaluates the energy consumption and embodied carbon emissions of various heat pump systems for an office building in Chicago, IL, U.S., over a 50-year lifespan, including the operation, manufacturing, and construction phases. The analyzed systems include air source heat pumps (ASHP) in Air to Air and Air to Water configurations, and ground source heat pumps (GSHP) in Soil to Air and Soil to Water configurations. A traditional HVAC system serves as the baseline for comparison. Advanced simulation tools, including Rhino, Grasshopper, TRACE 700, and One Click LCA, were used to identify the optimal HVAC system for sustainable building operations. Unlike prior studies focusing on GSHP versus traditional HVAC systems, this research directly compares GSHP and ASHP configurations, addressing a significant gap in the sustainable HVAC system design literature. The GSHP (Soil to Water) system demonstrated the lowest energy intensity at 100.8 kWh/m2·yr, a 41.8% improvement over the baseline, and the lowest total embodied carbon emissions at 3,882,164 kg CO2e. In contrast, the ASHP (Air to Air) system, while reducing energy consumption relative to the baseline, exhibited the highest embodied carbon emissions among the heat pump configurations due to its higher operational energy demands. The study highlights the significance of the operating phase in embodied carbon contributions. These findings emphasize the importance of a holistic design approach that considers both operational and embodied impacts to achieve sustainable building designs.

This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

Proton exchange membrane fuel cells (PEMFCs) are pivotal to advancing sustainable hydrogen energy systems. However, their performance decreases under low-humidity conditions (relative humidity, RH 50%) due to inadequate membrane hydration. This study addresses this challenge by utilizing a sputtering process to deposit titanium dioxide (TiO2) onto microporous layers (MPLs), enhancing their hydrophilicity and water management capabilities. TiO2 intrinsic hydrophilic properties and oxygen vacancies improve water adsorption and distribution, leading to more stable PEMFC performance under reduced humidity. Electrochemical evaluations revealed that while initial resistance slightly increased, long-term stability improved significantly. The TiO2-coated MPL exhibited a lower performance degradation rate, with a 12.33% reduction in current density compared to 25.3% for the pristine MPL after 10 h of operation. These findings demonstrate that TiO2 deposition effectively mitigates performance losses under low-humidity conditions, reducing the reliance on external humidification systems. This work contributes to the development of more efficient and sustainable fuel cell technologies for applications such as hydrogen-powered vehicles and distributed energy systems.

Shadow flicker caused by wind turbine blades passing through sunlight can significantly affect nearby residential buildings, raising environmental and regulatory concerns in wind farm development. The accurate assessment of shadow flicker exposure is critical for compliance and minimizing community impacts. We present a novel method for accurately determining the exposure of shadow flicker from wind turbines on residential buildings, addressing a key regulatory concern in wind farm planning. Current simulation techniques rely on discrete sampling of solar positions, resulting in potential inaccuracies tied to sampling resolution. Our proposed approach models shadow flicker as a continuous function and applies numerical minimization and numerical root finding to compute the duration of exposure. Our evaluation proves that this method achieves a superior balance between precision and computational efficiency, significantly improving existing techniques.

This research presents a novel technique that refines the performance of a frequency event detection algorithm with four adjustable parameters based on signal processing and statistical methods. The algorithm parameters were optimized using two well-established optimization techniques: Grey Wolf Optimization and Particle Swarm Optimization. Unlike conventional approaches that apply equally weighted metrics within the objective function, this work implements variable weighted metrics that prioritize specificity, thereby strengthening detection accuracy by minimizing false-positive events. Realistic small- and large-scale frequency datasets were processed and analyzed, incorporating various events, quasi-events, and non-events obtained from a phasor measurement unit in the Western Interconnection. An analytical comparison with an algorithm that uses equally weighted metrics was performed to assess the proposed method’s effectiveness. The results demonstrate that the application of variable weighted metrics enables the detection algorithm to identify frequency non-events, thereby significantly reducing false positives reliably.

Geothermal energy has emerged as a cornerstone in renewable energy, delivering reliable, low-emission baseload electricity and heating solutions. This review bridges the current knowledge gap by addressing challenges and opportunities for engineers and scientists, especially those transitioning from other professions. It examines deep and shallow geothermal systems and explores the advanced technologies and skills required across various climates and environments. Transferable expertise in drilling, completion, subsurface evaluation, and hydrological assessment is required for geothermal development but must be adapted to meet the demands of high-temperature, high-pressure environments; abrasive rocks; and complex downhole conditions. Emerging technologies like Enhanced Geothermal Systems (EGSs) and closed-loop systems enable sustainable energy extraction from impermeable and dry formations. Shallow systems utilize near-surface thermal gradients, hydrology, and soil conditions for efficient heat pump operations. Sustainable practices, including reinjection, machine learning-driven fracture modeling, and the use of corrosion-resistant alloys, enhance well integrity and long-term performance. Case studies like Utah FORGE and the Geysers in California, US, demonstrate hydraulic stimulation, machine learning, and reservoir management, while Cornell University has advanced integrated hybrid geothermal systems. Government incentives, such as tax credits under the Inflation Reduction Act, and academic initiatives, such as adopting geothermal energy at Cornell and Colorado Mesa Universities, are accelerating geothermal integration. These advancements, combined with transferable expertise, position geothermal energy as a major contributor to the global transition to renewable energy.