Optimization problems are a fundamental component of engineering and scientific research, particularly in energy systems, where growing industrial demands necessitate the development of robust and efficient optimization algorithms. The Combined Heat and Power Economic Dispatch (CHPED) problem seeks to minimize the operating costs of power and heat generation units while satisfying complex operational constraints, including valve-point effects, transmission losses, generation capacity limits, and heat-power coupling in cogeneration units. This study proposes a hybrid optimization framework that integrates the Firefly Algorithm (FA) with Sequential Quadratic Programming (SQP), termed HFASQP. The hybrid HFASQP approach leverages the global search ability of FA and the local refinement strength of SQP to address the non-convex, highly constrained nature of CHPED problems. The proposed method is validated on a set of benchmarks CHPED systems, including traditional test cases with 5, 7, and 48 units, as well as two newly introduced large-scale systems with 96 and 192 units to evaluate scalability. The proposed method achieved a cost reduction of up to 1.7% and 1.9% compared to FA, SQP and outperformed several state-of-the-art algorithms in terms of solution quality and computational efficiency. In addition, the performance of HFASQP is comparatively evaluated against other recently developed metaheuristic algorithms, including the Kangaroo Optimization Algorithm (KOA) and the Heap-Based Optimizer (HBO), to provide a more comprehensive assessment. To further evaluate robustness and generalizability, the HFASQP algorithm is tested on 23 standard benchmark functions from the optimization literature. The results confirm its consistent accuracy and competitiveness across all test cases, demonstrating its effectiveness beyond CHPED applications and highlighting its potential for broader engineering problems. • Developing a stability-enhanced FA–SQP hybrid for reliable optimization on highly nonconvex CHPED landscapes. • Using an adaptive trigger to invoke SQP selectively, reducing computational load while improving accuracy. • Achieving stronger constraint-feasibility than existing hybrid metaheuristics under nonlinear and valve-point effects. • Ensuring robust performance on non-smooth, multi-modal CHPED models through targeted global–local coordination. • Demonstrating scalable behavior via complexity assessment and benchmarks across multi-size CHPED systems.

Chinese hamster ovary (CHO) cells serve as the predominant host for the production of recombinant protein biopharmaceuticals. Maintaining an optimal culture environment is critical to achieving high productivity, ensuring protein quality, and minimising production costs. Amino acids are a key component of cell culture media which are essential for promoting cell growth and protein expression. Amino acids are the fundamental building blocks of proteins and serve critical roles in cellular energy generation and biosynthesis. The demand for amino acids varies throughout batch or fed-batch culture, which necessitates careful media and process design to ensure adequate supplies. However, if oversupplied, certain amino acids and associated byproducts can negatively impact cell growth and protein production. This review consolidates our understanding of the complex role of amino acid metabolism in CHO cells in the context of industrial production systems and growth medium formulations. It identifies and discusses key considerations in the demand and supply of amino acids, including fed-batch dynamics, amino acid toxicity, solubility and stability, medium osmolality, amino acid transporters and the relative availability of different amino acids. Based on the synthesis of current knowledge, emerging strategies to balance amino acid supply with demand are discussed, and research priorities identified to advance future efforts to optimise CHO cell culture process and media formulation.

Impact of environmental factors on renewable energy generation in China: A scenario analysis using predictive modeling for solar energy systems

New insights into heterotrophic nitrification-aerobic denitrification during efficient pyridine degradation by Rhodococcus pyridinivorans WN2.

Renewable energy generation and load forecasting: Technologies, applications in hydrogen energy storage systems and future prospects

Nuclear power technology provides efficient energy generation and reduced carbon emissions, but it also produces radioactive waste, including gaseous radionuclides such as technetium-99 (Tc-99) during the pyroprocessing of spent nuclear fuel. Calcium oxide (CaO) is a promising material for capturing gaseous Tc; however, its adsorption performance declines substantially at intermediate temperatures (500–800 °C). The objective of this study was to improve the adsorption capacity of CaO adsorbents within this temperature range by including isopropyl alcohol (IPA) as a pore-forming agent during synthesis. Mercury intrusion porosimetry showed that increasing IPA content promoted macropore formation, thereby improving rhenium (Re, a Tc surrogate) adsorption performance at 500–700 °C. A quantitative correlation was established between the dominant macropore size and Re adsorption capacity, demonstrating that pore-structure-controlled internal transport accessibility governs capture performance. The optimized CaO adsorbent (CP-1.00) demonstrated a maximum Re adsorption capacity of 16.95 mol-Re/kg-ads and over 99% capture efficiency at 500 °C. These findings demonstrate that tailoring the pore structure via IPA addition significantly improves the capture of gaseous radionuclides throughout an expanded temperature range, providing greater flexibility for high-temperature off-gas treatment in spent nuclear fuel processing systems.

Are natural resources a blessing or a curse for the development of nuclear energy in times of energy insecurity

Informer-enhanced digital twin framework for forced draft fans status forecasting in coal-fired power units

MPC-based load control with dynamic temperature safety constraints for molten salt energy storage coupled with thermal power units

In recent decades, the world has faced successive energy crises that have highlighted the fragility of generation and distribution systems across both developing and advanced economies. Given this scenario, coupled with accelerated urbanisation, a review of urban energy consumption, generation and management systems is indispensable. The energy indicators defined in the Brazilian standard NBR ISO 37120, which addresses service performance and quality of life in cities, were analysed, focusing on the energy sustainability of municipalities in the Brazilian state of Goiás. Data from 2019 to 2023 were systematised, and critical and comparative analyses were performed to identify trends, regional inequalities and opportunities for improvement in municipal energy management. To enable visualisation and interpretation of results, an interactive tool was developed and implemented in Power BI, allowing public managers to understand the local energy landscape and support strategic decision making. This study contributes to strengthening energy planning capacities and to formulating more effective and sustainable public policies aligned with the UN Sustainable Development Goals.

The growing depletion of fossil fuel reserves and the associated environmental concerns have intensified the search for sustainable and renewable energy alternatives. Bioethanol is a promising renewable biofuel that can be produced through microbial fermentation of organic waste. The present study investigated the feasibility of ethanol production from household organic waste using Saccharomyces cerevisiae. Fruit peels, vegetable peels, sugarcane bagasse and leftover rice were collected, processed into slurries and subjected to acid and enzymatic pretreatment to enhance the availability of fermentable sugars. The pretreated substrates were inoculated with S. cerevisiae and incubated under submerged fermentation conditions for 21–30 days. Ethanol was recovered using fractional distillation with a rotary evaporator at 78–79 °C. Qualitative confirmation of ethanol was carried out using the dichromate and Lucas tests, while quantitative estimation was performed using UV–visible spectrophotometry. The highest ethanol concentration was obtained from leftover rice (H”70%), followed by fruit peels (H”69.7%), bagasse (H”65%) and vegetable peels (H”24%). The findings demonstrate that household organic waste can serve as an economical and environmentally sustainable feedstock for bioethanol production, contributing to waste valorization and renewable energy generation.

In commercial microgrids, for effective energy management and reliable decision-making, it is imperative to include the uncertainties in load demands as well as in the renewable energy generation. To refine the research focus, the proposed work focusses on solar PV-load forecasting in the smart grid environments. The rise in demand fluctuations necessitates an improved 10% accuracy in the forecasting models. But whenever the forecasting horizon length crosses time steps of 12, the existing models becomes unstable in predicting, with a degradation in accuracy and scalability by 8-15%. Limited research works are available to analyze the effect of rolling-horizon in managing the uncertainties. In this scenario, the proposed work employing transformer-based PV-load forecasting framework, can achieve an improved probabilistic forecasting accuracy of greater than 12%. Horizon-aware learning mechanisms are incorporated into the proposed model to accurately estimate the uncertainty. Model rolling-horizon based experiments using MATLAB environment simulation is performed on the proposed model for validation purposes. All forecasts and probability analyses illustrated in the present document are produced through simulation results by the authors using consistent simulation conditions. Three different operational conditions are considered for the evaluation with the performance metrics being the continuous ranked probability score (CRPS) and pinball loss. Improved quality of probabilistic forecasting is visible with a reduction in CRPS value by 12.6% and effective prediction interval capture is seen with internal coverage of 9.4%. The computational cost and requirements have been lowered by 18%. The architecture can scale up to about 14 different forecasting horizons, with consistent stability under different PV and load conditions. The numerical results confirm consistent improvements in the performance gains of the proposed forecasting model. Further this transformer-model based approach outperforms both gates recurrent unit baselines and long short-term memory models. There is a 12% gain improvement in average case scenarios and about 6% gain improvement in worst case scenarios, making the model suitable for applications that demand latency time of less than 1.5s. Thus, the detailed analysis demonstrates performance gains in terms of different evaluation metrics. Thus, the overall results exhibit superior performance as against other existing modelling techniques. When scaling up the transformer-based modelling concept beyond horizon steps of 14, there is a degradation in the forecasting performance, which can be analyzed in future scope.

One of the most important concerns now governing international attention is energy generation. In this work, Ce(NDC)MOF@Bentonite nanocomposite was synthesized and used as a novel and effective catalyst for the green synthesis of hydrogen. The synthesized composite was characterized using X-ray diffraction (XRD), thermogravimetric analysis (TGA), N2-adsorption, Fourier- transform infrared (FT-IR), and scanning electron microscope (SEM) techniques. On the view of the characterized data, a pure and distinct crystalline phase with a highly specific surface area (SBET) of 58.5 m2/g was formed. Moreover, the morphology of the nanocomposite was monitored through scanning (SEM). SEM image which revealed a highly porous surface texture with some large particles with highly spherical morphology. The catalytic performance of the fabricated catalysts was propped via the hydrolysis of sodium borohydride. The effect of NaBH4 concentration, weight of the catalyst, and the reaction temperature on NaBH4 hydrolysis was investigated. The hydrogen generation rate HGR of 116.02 mL min-1g- 1 at 30°C was achieved using 16mg of the catalyst and 0.05M NaBH4. Kinetic and thermodynamic functions including activation energy, enthalpy, entropy, and free energy changed were also estimated. This work suggests that such novel and highly active catalysts hold strong potential as advanced materials for hydrogen energy applications.

Targeted pollutant exposure is widely used to acclimate microbial communities for enhanced biodegradation of recalcitrant contaminants, yet the evolutionary mechanisms underlying functional reinforcement remain poorly understood. Here, we acclimated a methanotrophic consortium achieving efficient removal of 3-amino-5-methyl-isoxazole (3A5MI) (>90%, >5 mg/L/d) and elucidated the adaptive evolutionary processes behind it. Analyses of mobile genetic elements (MGEs) and horizontal gene transfer (HGT) revealed that dominant Methylococcaceae members served as genetic exchange hubs in the acclimation bioreactor. Integrated metagenomic and metatranscriptomic analyses showed that prolonged 3A5MI exposure activated their MGEs and promoted extensive HGT of genes related to energy generation, oxidative stress defense, and biosynthesis. This adaptive evolution enabled community-level metabolic rewiring, including optimized carbon metabolism to relieve energy limitation, niche differentiation, and specialized transcription of C-N bond catalytic functions. Furthermore, batch experiments and transformation product analyses confirmed that 3A5MI-induced functional traits (e.g., heterocycle hydroxylation and C-N bond catalysis) facilitated complete sulfamethoxazole (SMX) biodegradation. Overall, this study demonstrates the evolutionary plasticity of methanotrophic consortia under targeted acclimation and highlights MGE-driven genetic exchange and metabolic adaptation as key mechanisms that both underpin functional enhancement and support the development of methanotroph-based strategies for the biodegradation of recalcitrant isoxazole-based pollutants.

Egypt possesses substantial potential for renewable energy generation, prompting heavy national investments to increase the share of wind power in its overall energy portfolio. Consequently, it is crucial to evaluate the long-term vulnerability of future wind energy production to climate change. This study fills a critical gap in regional climate-energy modelling by providing a novel quantification of turbine-specific capacity ratios across four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Through a comparative assessment of 23 CMIP6 Global Climate Models (GCMs), EC-Earth3-Veg, EC-Earth3, and CESM2-WACCM were identified as the most reliable models against historical ERA5-Land data using the Kling-Gupta Efficiency (KGE) metric, followed by Quantile Mapping for bias correction of both historical and future scenarios. Evaluating nine wind turbine models (T1-T9) revealed that T1 and T2 maintained the highest historical capacity ratios, peaking at 68.0-76.5% and 59.5-68.0%, respectively. By 2100, meteorological projections indicate a regional warming trend coupled with a decrease in mean wind speed; notably, the high-emission SSP5-8.5 scenario projects the highest mean temperature (28°C) and lowest mean wind speed (3.8m/s). Despite these declines, future projections for T1 and T2 indicate resilient power generation and localized increases in strategic locations, such as Ras Ghareb and southern Egypt, particularly under the SSP2-4.5 scenario. Ultimately, these findings provide essential data-driven insights for energy planners to optimize turbine selection and site development, ensuring the long-term resilience of Egypt's wind energy infrastructure.

ABSTRACTAccelerating global climate risks increasingly threaten renewable energy infrastructure (REI). However, little evidence on heterogeneous impacts of climate risks on REI across countries, the moderating role of REI resilience, and post‐disaster recovery patterns is available, despite their critical importance for guiding resilient energy transitions and informing disaster risk governance. To address these issues, we employed dynamic panel models in 215 countries and regions from 2004 to 2022. We find that (1) climate risk significantly damages global REI, with disaster frequency and institutional resilience having mitigation effects. (2) The damage follows an inverted U‐shape with increasing disaster frequency and an “N” shape with increasing disaster duration. As renewable energy generation share increases, the damage intensifies and progresses through four increasingly severe stages. (3) Economic resilience exhibits a “Creative destruction” effect in developed nations and a “Build back better” recovery in poor countries. (4) Although social resilience worsens climate disaster damage globally, high disaster frequency and institutional resilience can facilitate a “Recovery to trend” in socially advanced nations. (5) REI in South America is the most affected, followed by Asia and Africa, whereas Europe is the least impacted. Wind energy is the most vulnerable, followed by bioenergy, solar, and hydropower.

Large power fluctuations in a brief amount of time, or ramp events, are an increasing concern for grid operators due to the rise in renewable energy generation and the unreliable hour-ahead predictions. To balance these ramp events, grid operators need to be aware of their anticipated occurrence intervals and range. Prior studies used binary ramp event categorization, whereas other studies employed non-causative classification techniques. Existing clustering methods, Z-score and k-means, have strengths but distinct limitations. To address these, this paper introduces the ZK-means hybrid approach, integrating Z-score normalization with k-means partitioning, forming a centroid-based clustering algorithm to enhance adaptability, noise resistance, and interpretability in ramp classification. Its need arises from the growing demand for accurate and efficient ramp analysis to support reliable grid operation and forecasting. Two comparison phases for the ZK-means approach were conducted: First, it was evaluated against its constituent methods to assess the benefits of their combination; second, it was compared to the density-based spatial clustering of applications with noise (DBSCAN) algorithm to verify its robustness and general applicability. Although DBSCAN can capture local variations in data, it produced inconsistent cluster numbers and required frequent parameter tuning across the ten years. In contrast, ZK-means achieved more stable clustering patterns and lower within-cluster variance, demonstrating superior reliability for long-term ramp event characterization. The new categorization method is applied to a real case study, and the results reveal that the new hybrid method offers significant improvements in the quality, robustness, and interpretability of the clustering process and its resulting cluster characteristics, as it combines the stability of normalization with the scalability of k-means, offering a robust and practical solution for large-scale, high-dimensional clustering. While this new method does entail a slight increase in time-speed, computational complexity, and energy consumption compared to its constituent methods, it remains faster than DBSCAN and the enhanced insights it provides offer critical advantages for effective grid management.

Lightweight, flexible, and capable of high specific power, organic photovoltaic (OPV) cells represent a promising solution for energy generation in deep space. However, their practical operation under such extreme conditions remains in its infancy. Here we reveal that low temperatures reshape the intrinsic energetics and charge dynamics of OPV cells. As temperature decreases, the density of states narrows and the quasi-Fermi level splitting increases, enhancing the open-circuit voltage. Yet, the reduced driving force constrains exciton dissociation and charge transport, highlighting the need for next-generation active layers with enhanced driving forces. Meanwhile, cathode interlayer materials that facilitate charge extraction through interfacial dipole effects demonstrate superior performance at cryogenic temperatures. Flexible OPV cells based on polyimide substrates exhibit remarkable mechanical resilience under such conditions. These findings provide guiding principles for the design of efficient, durable, and adaptable photovoltaic systems for future deep space exploration.

This work analyzes 2019 hourly data from the New York Independent System Operator (NYISO) energy generators to forecast pollutant emissions for 2030. Leveraging methods and insights from the Advanced Research Projects Agency-Energy (ARPA-E) Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) project's optimization tools and the Environmental Protection Agency (EPA) historical data, we produce a method to determine future emissions scenarios within the NYISO region to be used as part of operational studies. Integrating historical data with forecasting software, this study evaluates the impact of renewable energy variability on pollutant emissions, informing policy decisions for a sustainable energy future.

Simulation-driven investigations are presented on highly efficient monolithic tandem solar cells with climate-efficient nano-scaled perovskite and crystalline silicon for green energy generation. Tandem solar cells comprise a lead-free CsGeI3 perovskite top cell and a silicon bottom sub-cell. A Cu2O hole transport material layer and a ZnO electron transport material layer was used. Optimizations were performed by varying doping, defect concentration, thickness, and band gap to obtain valuable insights into material properties. These perovskite-silicon tandem solar cells, with a wide band gap and optimized parameters, yielded power conversion efficiencies above the Shockley-Queisser limit for single-junction cells. The structure of perovskite-silicon tandem solar cells is Glass/FTO/ZnO/CsGeI3/Cu2O/RL/Si(p+)/Si(p)/Si(n)/Au. After optimization, the results show a power conversion efficiency of 37.23%, a Jsc of 23.95mA/cm2, a Voc of 1.895V, and an FF of 82%. This research shows that through this hybrid perovskite-silicon technology, one is assured of increased energy output while decreasing carbon footprints and increasing renewable energy use.