The A- and B-site arrangement in ABO3-type perovskite oxides enables them to be tailored for excellent catalytic performance, while also exhibiting both O2 - and electronic conductivity, making them suitable as electrode materials for solid oxide fuel cells (SOFCs). This study introduces a novel double-conductor perovskite anode, La0.7Sr0.3Al0.7Ti0.3O3- δ, designed to drive the oxidative coupling of methane (OCM) for the coproduction of valuable light olefins such as ethylene and propylene, while simultaneously enabling efficient power generation. The material is characterized for its structural stability and mixed conductivity using XRD, high-resolution HRTEM, and EIS. Based on these properties, the material is assembled into a cell as the anode to evaluate its electrochemical performance. The symmetrical cell employing La0.7Sr0.3Al0.7Ti0.3O3- δ as the electrode demonstrates an area-specific impedance of 0.06 Ω cm2 at 850°C under a CH4 atmosphere, indicating superior catalytic activity and electrical conductivity. When operated at 800°C with CH4 as the fuel, the cell achieves a maximum power density of 188.1 mW cm-2, accompanied by a hydrocarbon selectivity of 54.2%, markedly exceeding the 32.8% obtained in fixed-bed reactor measurements. These results demonstrate that the La0.7Sr0.3Al0.7Ti0.3O3- δ anode promotes partial fuel oxidation through oxygen-ion conduction while maintaining the electrochemical cycle via electronic conduction.

This study investigates the feasibility of transitioning from biogas to biomethane as a fuel source for power generation in wastewater treatment plants. Using real operational data from the As-Samra Wastewater Treatment Plant in Jordan, the biogas upgrading process was modeled using water scrubbing and methanation techniques. Simulations were performed with Aspen Plus®, TRNSYS, and MATLAB® to analyze the biogas upgrading process, generator performance, and emissions. The results revealed that optimizing water flow rates and feed pressure during scrubbing enhanced methane purity to over 80% at the scrubbing stage, prior to the methanation step. After catalytic methanation, the final biomethane purity exceeded 90%. However, increasing the water flow temperature negatively affected the methane concentration in the produced biomethane, necessitating greater hydrogen addition during the methanation stage. Key operational parameters, such as reactor temperature and pressure, were also analyzed, achieving maximum methane recovery of 2500 kg/h at 15 bar and 350 °C. Replacing biogas with biomethane in generators increased power output from 5 MW to 7.9 MW due to biomethane’s higher calorific value, which led to a 50% increase in the heat release rate. Emissions analysis showed a 55% reduction in CO 2 mole fraction (from 9.5% to 4.2%), reflecting the lower CO 2 content in the upgraded fuel compared to raw biogas. • Transitioning from biogas to biomethane enhances power generation efficiency. • Water scrubbing and methanation improve methane percentage and recovery rates. • Biomethane use increases generator output from 5 MW to 7.9 MW. • Biomethane reduces the mole fraction of biogenic CO 2 per unit of energy by 55%. • Optimizing reactor conditions maximizes methane recovery and system performance.

Thermodynamic analysis of a copper-chlorine cycle integrated with carbon capture and methanol synthesis for industrial waste heat recovery and power generation

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.

• A Python model for semi-islanded green ammonia production was developed. • The LCOA ranges from 669.30 to 867.94 €/tNH 3 . • Power generation represents the largest share of total system costs. • The LCOA decreases by up to 15.15 % over the next two decades. • Dynamic operation achieves up to a 6 % reduction compared with continuous operation. The study presents a technical and economic assessment of green ammonia production in several counties in Ireland. The system is based on renewable energy sources, namely photovoltaic and offshore wind. Three locations were chosen based on their renewable potential and the availability of export ports to EU (European Union) markets. A high-temporal-resolution model for green ammonia production has been developed for the first time in Ireland. The WSA (Wind Solar Ammonia) model was developed specifically for this research. It uses MILP (Mixed Integer Linear Programming) and optimisation techniques to simulate scenarios at the lowest possible cost. The Python-based model incorporates all relevant energy subsystems and use functions from specialised libraries. The WSA model includes large-scale hydrogen production with proton exchange membrane electrolysers, air separation to produce nitrogen, Haber-Bosch ammonia synthesis, desalination unit. Storages buffers were implemented for green hydrogen, green ammonia, purified sea water, and nitrogen. Both continuous and dynamic operation were simulated, continuous operation reflects industrial reliability, stable equipment performance, and maximised lifetime, while dynamic operation captures renewable intermittency, curtailment reduction, and system flexibility. Cork is identified as the least-cost location, with the dynamic operation system achieving the lowest LCOA (Levelised Cost Of Ammonia) at 791.07 €/t in 2030 and 731.45 €/t in 2040, outperforming the continuous operation system, which records 834.27 €/t in 2030 and 741.62 €/t in 2040. The system achieve a carbon saving up to 94.63 % compared to the ammonia fossil fuel-based comparator.

Effects of green roofs on the thermal behavior and power generation efficiency of photovoltaic in hot-summer city

Boosting U(VI) extraction and existing organic degradation as well as electricity production from wastewater and seawater by a solar-driven multifunctional photocatalytic fuel cell decorated with ZnS/CF cathode.

Modulating tenorite-based bimetallic electrocatalysts for highly selective ammonia oxidation to N2.

A systematic review on the application of lifecycle-based approaches in assessing geothermal power generation

Forecasting power generation and battery charge in residential solar systems with front and rear side photovoltaic panels

Thermodynamic and techno-economic assessment of an integrated waste-to-levulinic acid and power generation system based on plasma gasification

Porous substrate approach for improved salinity power generation in scalable osmotic energy conversion

Ferric ion improves power generation in microbial fuel cells under optimized conductivity and external resistance

Parametric studies for constant power generation of gas turbine systems using hydrogen/methane blend combustion

The present research paper presents a new hybrid deep-learning model which aims to enhance the precision of multi-step, short-term wind power generation forecasting. The proposed hybrid model integrates two distinct deep learning models i.e. Bi-LSTM (bidirectional long-short-term memory) network and 1D-CNN (one-dimensional convolutional neural network). The fine-grained features from the original input record are automatically extracted by a convolutional layer in a 1D-CNN. The Bi-LSTM layers are used to retain crucial information, allowing it to remember and process data over a longer time. To further improve predicting accuracy, two distinct methods i.e. discrete wavelet transformation (DWT) and adaptive random search optimization (ARSO) have been applied to denoise the time-series input data of wind power and optimize model’s hyperparameters, respectively. The proposed prediction model is tested on SCADA datasets at the Yalova Wind farm, covering the period from January 2018 to December 2018, which considers three input features: wind speed, wind direction, theoretical power curve, and whereas wind power output is treated as the target variable. The effectiveness of the model's performance is examined using 10-minute data from the Yalova Wind Farm, focusing on forecasting horizons of 30, 40, and 60 minutes. The proposed model is also compared with benchmarking models such as Bi-LSTM, 1D-CNN, LSTM, gated recurrent unit (GRU), and hybrid 1D-CNN+Bi-LSTM, is to analyze the model's robustness. The output evaluation metrics, i.e. mean squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ), reveal that the proposed hybrid model is found superior in terms of overall forecasting precision across all three forecasting horizons. For the 30-minute forecasting horizon, the proposed model outperforms other competing models with superior average values for all the error metrics i.e. MSE = 0.0109, R 2 = 0.9206, RMSE =0.1044, and MAE = 0.0656. Moreover, it is capable to achieve mean values of output error metrics i.e. MSE = 0.0135, R 2 = 0.9016, RMSE = 0.1163, and MAE = 0.0710 for 40-minute forecasting horizon, and MSE of 0.0187, R 2 of 0.8751, RMSE of 0.1368 and MAE of 0.0812 for 60-minute forecast horizon. ✓ The proposed hybrid model is compared with several other benchmarking forecasting models, such as GRU, 1D-CNN, Bi-LSTM, LSTM, and hybrid 1D-CNN+Bi-LSTM, to test its effectiveness. ✓ Its performance is further evaluated and verified for three forecasting horizons of 30-Min, 40-Min, and 60-Min, which offers deeper insights into the model's effectiveness for multistep forecasting applications. ✓ The output forecasting precision of the proposed hybrid deep-learning model is examined on four distinct performance metrics i.e., MSE, RMSE, R 2 , and MAE, demonstrating its model's practical relevance with their significant improvements. ✓ An extensive statistical analysis with Boxplots and Diebold-Mariano test results w.r.t. to other competing models are presented to validate the robustness of the proposed hybrid model.

Mixed-frequency external grey information model for forecasting new energy power generation under the global 2 °C trajectory

Towards a low-carbon power grid: a prospective life cycle assessment of aqueous Zn//MnO2 battery energy storage with integrated power generation in China

Toward sustainable power generation: Techno-Economic optimization of a solar-driven ORC power plant with integrated desalination and hydrogen backup

Study on the performance of salinity gradient power generation of sodium alginate/tannic acid-trivalent iron ion cross-linked modified evaporator based on nickel foam

Endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) is increasingly used; however, clinical application remains unstandardized. We assessed real-world practice among international users. A cross-sectional 70-item survey was conducted. Results are presented descriptively (numbers, percentages). Overall, 91 of 175 invited physicians from Europe (74.4%), North America (13.3%), and Asia (12.2%) completed the survey. EUS-RFA was performed by 94.1% of respondents for insulinoma, with heterogeneous responses for other indications. Most physicians (96.3%) performed EUS-RFA under deep sedation or general anesthesia; marked variability was reported on antibiotic prophylaxis (57.5%), aggressive hydration (52.5%), generator power settings, ablation strategy, or probe selection. Lesions involving or located ≤ 1 mm from the main pancreatic duct were considered high risk by 97.5% and 85.0%, respectively, yet no standardized preventive strategy was identified. Post-procedural management and follow-up were heterogeneous, with a high proportion of responses for definitions of technical success (88.8%), clinical success in insulinoma (92.3%), and disease recurrence (97.5%), with high variability for definitions of partial ablation and post-RFA pancreatitis. Despite the global expansion of EUS-RFA, clinical practice remains highly heterogeneous and geographically skewed. The lack of standardized methodology and terminology poses significant barriers to generating high-quality evidence.