Background Developing cost-reduced Pd-based membranes and understanding their integration into membrane reactors for hydrogen production remain important challenges. Although Pd and Pd-Ag membranes have been widely studied for methanol steam reforming, their high cost limits application. Pd-Ni membranes have attracted attention as a lower- palladium-content option for hydrogen separation due to their reduced noble metal content; however, their performance in membrane reactor configurations has not been reported. This study integrates, for the first time, an organic-inorganic activation-fabricated Pd-Ni composite membrane into a membrane reactor for methanol steam reforming, enabling evaluation of membrane behavior and hydrogen separation performance under a reactive environment containing steam, methanol, and reforming products. Methods A dense tubular Pd-Ni membrane (∼2 μm selective layer) was prepared via organic-inorganic activation combined with electroless plating. Hydrogen permeation was evaluated at 200–350 °C and 10–50 kPa using pure gases and binary mixtures containing steam or methanol. The membrane was implemented in a membrane reactor packed with a Cu/ZnO/Al₂O₃ catalyst under identical operating condition (H₂O/CH₃OH = 1:1; gas hourly space velocity = 2000 h⁻¹) and compared with a conventional reactor. Significant findings The membrane exhibited infinite H₂/N₂ selectivity and a hydrogen flux of 2.55 × 10⁻² mol m⁻² s⁻¹ at 350 °C and 50 kPa. Steam and methanol reversibly inhibited hydrogen permeation, stronger for methanol. Methanol conversion reached 46.5% at 350 °C and 100 kPa—twice that of the conventional reactor—while hydrogen recovery reached 91%, demonstrating the capability of Pd-Ni membranes to enhance hydrogen separation and improve methanol conversion in MSR membrane reactors.
Ultrasonic welding of titanium to steel is important in medical and aerospace applications, but remains challenging due to the significant differences in their physical properties. In this study, ultrasonic welding was used to join 0.3 mm-thick titanium and St12 steel sheets, with aluminum interlayers of 10, 50, and 100 μm. After preparation, the microstructure of the weld interface was analyzed by optical microscopy, while mechanical properties were evaluated through shear-tensile and microhardness tests. Reducing the interlayer thickness from 100 to 10 μm decreased heat dissipation, raised the interface temperature, and consequently increased the grain size, with the steel grain size rising from 7 to 19.5 μm. As a result, the weld line density increased from 62
Blasting is one of the most critical stages in open-pit mining operations, directly affecting mine productivity, extraction costs, and the control of fines generation. This study focuses on optimizing blasting operations in the Rahmanlu quarry to reduce fines production and enhance overall mining efficiency. Initially, three blasts were examined using image analysis techniques to identify an appropriate blasting pattern. Subsequently, three mathematical models SVEDEFO, Kuz-Ram and modified Kuz-Ram were employed to predict fragmentation resulting from blasting. The model predictions were compared with image analysis results from the actual blasts, with the modified Kuz-Ram model showing the closest agreement to real conditions. A calibration equation was then fitted to further improve the alignment between the modified Kuz-Ram model and actual blast results. Next, eighteen different blasting patterns were developed based on empirical methods, and the optimal pattern was selected according to fragmentation size distribution and economic parameters. Implementation of the optimized pattern resulted in approximately a 10
Climate change driven by anthropogenic greenhouse gas emissions has intensified the need for sustainable mitigation strategies. Carbon capture and storage (CCS) in geological formations (depleted reservoirs and aquifers) is a promising approach to reduce atmospheric CO2 and limit global warming. However, under reservoir conditions, CO2 can interact with pore water to form gas hydrates, which are crystalline solids that severely reduce injectivity and alter reservoir performance. This review integrates current understanding of hydrate formation mechanisms, distribution patterns, and mitigation strategies by integrating insights from laboratory experiments, numerical simulations, and data-driven approaches. Microfluidic and core flooding studies show that hydrate nucleation primarily occurs at gas-water interfaces, leading to rapid pore blockage and permeability loss, with growth influenced by rock type, porosity, and initial water saturation. Advanced imaging has revealed morphologies such as films, bridges, and needle-like crystals that affect transport and sediment stability. Reservoir-scale simulations highlight the roles of Joule-Thomson (JT) cooling, capillary effects, and heterogeneity, with injection rate emerging as a key driver of near-wellbore blockage. Machine learning (ML) further improves predictions of hydrate kinetics, permeability reduction, and hydrate-prone zones. Gas impurities such as H2S, CH4, and N2 expand hydrate stability zones and increase operational risks. Mitigation strategies include inhibitors, pressure-temperature management, dehydration, and novel approaches like cold flow transport. Despite progress, uncertainties remain in scaling laboratory results to field conditions and ensuring long-term inhibitor effectiveness. Addressing these gaps requires integrated experiments, advanced simulations, and ML to develop reliable predictive models and optimize CCS operations.
Precise pressure regulation in nonlinear shell-and-tube steam condensers is essential for maintaining thermal efficiency and operational safety in power generation plants; however, conventional proportional-integral (PI) and proportional-integral-derivative (PID) controllers struggle with nonlinear dynamics, leading to overshoot, slower settling, and reduced robustness. In this regard, a novel hyperbolic tangent-based PID (tanh-PID) controller is developed in this study to introduce smooth nonlinear gain modulation, enabling enhanced damping behavior and improved transient shaping. The recently introduced artificial lemming algorithm (ALA) is employed to optimally tune the proposed controller for integral of time-weighted absolute error minimization. Extensive simulation studies are performed using a comprehensive nonlinear condenser model incorporating steam–air interactions and hot-well dynamics. The proposed strategy is benchmarked against four competitive optimization algorithms (coati optimization algorithm, dandelion optimizer, success-history based adaptive differential evolution with linear population size reduction, and adaptive artificial electric field algorithm) and compared with state-of-the-art PI and fractional-order PID (FOPID) controllers reported in the literature. The ALA-tuned tanh-PID achieves the lowest integral of time-weighted absolute error (2.1189), fastest rise time (0.5960 s), minimal settling time (12.4799 s) and overshoot (5.8056
