High temperature sulfuric acid decomposition in iodine-sulfur process --thermodynamics,concentrator and reactor,product separation,materials, and energy analysis

A successful demonstration of closed-loop thermochemical Iodine–Sulfur (IS) cycle for hydrogen production remains challenging owing to the lack of detailed data on thermodynamic estimates, recuperator system, reactor designs, product separation, and materials of construction. This review identifies and details the technological challenges in the most energy-consuming sulfuric acid decomposition step with a focus on the thermodynamic behavior of the fluids, acid concentration system, reactor designs, SO₂–O₂ separation, energy consumption, and materials of construction. The state-of-art and directions for future development are also discussed in detail. The results show that the thermodynamic properties can be estimated accurately using an electrolyte NRTL model. The compression-cooling process is found to be a better alternative for separating SO₂–O₂. However, the high energy requirement for cooling remains a tall challenge. Hastelloy C-276 and SiC are suitable materials for the construction of the equipment in the high temperature range while, SiC, Teflon, and glass are suitable in the low temperature range.     

Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method

Automatic tool changer (ATC) is one of the key basic parts in CNC machining centers, and the globoidal indexing cam and the groove cam are the functional units for tool changing. Thus the condition monitoring is important for highly efficient and precision machining. In this paper, a condition monitoring system is constructed for the ATC, especially for the globoidal indexing cam, including vibration signal acquisition, fault feature extraction and localization, and condition assessment. In the constructed system, sparsity-enabled signal decomposition method is introduced to extract transient component and reduce noises in the complex vibration signals, and the transient component is always a key feature for fault localization. Simulation study shows that the sparsity-enabled signal decomposition method is effective in transient feature extraction. The experimental application in condition assessment for the ATC demonstrates that the constructed condition monitoring system has the potential to assess the working condition of the ATC in practical application.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Direct high-purity hydrogen production from ammonia by using a membrane reactor combining V-10mol%Fe hydrogen permeable alloy membrane with Ru/Cs2O/Pr6O11 ammonia decomposition catalyst

The hydrogen production capabilities of the membrane reactor combining V-10 mol%Fe hydrogen permeable alloy membrane with Ru/Cs₂O/Pr₆O₁₁ ammonia decomposition catalyst are studied. The ammonia conversion is improved by 1.7 times compared to the Ru/Cs₂O/Pr₆O₁₁ catalyst alone by removing the produced hydrogen through the V-10mol%Fe alloy membrane during the ammonia decomposition. 79% of the hydrogen atoms contained in the ammonia gas are extracted directly as high-purity hydrogen gas. Both the Ru/Cs₂O/Pr₆O₁₁ catalyst and the V-10 mol% Fe alloy membrane are highly durable, and the initial performance of the hydrogen separation rate lasts for more than 3000 h. The produced hydrogen gas conforms to ISO 14687–2:2019 Grade D for fuel cell vehicles because the ammonia and nitrogen concentrations are less than 0.1 ppm and 100 ppm, respectively.     

Mathematical modelling and simulation of the thermo-catalytic decomposition of methane for economically improved hydrogen production

The demand for low-emission hydrogen is set to grow as the world transitions to a future hydrogen economy. Unlike current methods of hydrogen production, which largely derive from fossil fuels with unabated emissions, the thermo-catalytic methane decomposition (TCMD) process is a promising intermediate solution that generates no direct carbon dioxide emissions and can bridge the transition to green hydrogen whilst utilising existing gas infrastructure. This process is yet to see widespread adoption, however, due to the high catalyst turnover costs resulting from the inevitable deactivation of the catalyst, which plays a decisive role in the feasibility of the process. In this study, a feasible TCMD process was identified and a simplified mathematical model was developed, which provides a dynamic estimation for the hydrogen production rate and catalyst turnover costs over various process conditions. The work consisted of a parametric study as well as an investigation into the different process modes. Based on the numerous simulation results it was possible to find the optimal process parameters that maximise the hydrogen production rate and minimise the catalyst turnover costs, therefore increasing the economic potential of the process and hence its commercial viability.     

Achieving superior hydrogen storage properties via in-situ formed nanostructures: A high-capacity Mg–Ni alloy with La microalloying

Mg-based hydride is a promising hydrogen storage material, but its capacity is hindered by the kinetic properties. In this study, Mg–Mg₂Ni–LaH_x nanocomposite is formed from the H-induced decomposition of Mg₉₈Ni_1·67La_0.33 alloy. The hydrogen capacity of 7.19 wt % is reached at 325 °C under 3 MPa H₂, attributed to the ultrahigh hydrogenation capacity in Stage I. The hydrogen capacity of 5.59 wt % is achieved at 175 °C under 1 MPa H₂. The apparent activation energies for hydrogen absorption and desorption are calculated as 57.99 and 107.26 kJ/mol, which are owing to the modified microstructure with LaH_x and Mg₂Ni nanophases embedding in eutectic, and tubular nanostructure adjacent to eutectic. The LaH_2.49 nanophase can catalyze H₂ molecules to dissociate and H atoms to permeate due to its stronger affinity with H atoms. The interfaces of these nanophases provide preferential nucleation sites and alleviate the “blocking effect” together with tubular nanostructure by providing H atoms diffusion paths after the impingement of MgH₂ colonies. Therefore, the superior hydrogenation properties are achieved because of the rapid absorption process of Stage I. The efficient synthesis of nano-catalysts and corresponding mechanisms for improving hydrogen storage properties have important reference to related researches.     

Skeleton parsing for complex question answering over knowledge bases

Answering complex questions involving multiple relations over knowledge bases is a challenging task. Many previous works rely on dependency parsing. However, errors in dependency parsing would influence their performance, in particular for long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This lightweight formalism and its BERT-based parsing algorithm help to improve the downstream dependency parsing. To show the effectiveness of skeleton, we develop two question answering approaches: skeleton-based semantic parsing (called SSP) and skeleton-based information retrieval (called SIR). In SSP, skeleton helps to improve structured query generation. In SIR, skeleton helps to improve path ranking. Experimental results show that, thanks to skeletons, our approaches achieve state-of-the-art results on three datasets: LC-QuAD 1.0, GraphQuestions, and ComplexWebQuestions 1.1.     

One-step synthesis of Ni/yttrium-doped barium zirconates catalyst for on-site hydrogen production from NH3 decomposition

On-site produced hydrogen from ammonia decomposition can directly fuel solid oxide fuel cells (SOFCs) for power generation. The key issue in ammonia decomposition is to improve the activity and stability of the reaction at low temperatures. In this study, proton-conducting oxides, Ba(Zr,Y) O_3-δ (BZY), were investigated as potential support materials to load Ni metal by a one-step impregnation method. The influence of Ni loading, Ba loading, and synthesis temperature, of Ni/BZY catalysts on the catalytic activity for ammonia decomposition were investigated. The Ni/BZY catalyst with Ba loading of 20 wt%, Ni loading of 30 wt%, and synthesized at 900 °C attained the highest ammonia conversion of 100% at 600 °C. The kinetics analysis revealed that for Ni/BZY catalyst, the hydrogen poisoning effect for ammonia decomposition was significantly suppressed. The reaction order of hydrogen for the optimized Ni/BZY catalyst was estimated as low as ?0.07, which is the lowest to the best of our knowledge, resulting in the improvement in the activity. H₂ temperature programmed reduction and desorption analysis results suggested that a strong interaction between Ni and BZY support as well as the hydrogen storage capability of the proton-conducting support might be responsible for the promotion of ammonia decomposition on Ni/BZY. Based on the experimental data, a mechanism of hydrogen spillover from Ni to BZY support is proposed.     

Catalytic behavior in CH4 decomposition of catalysts derived from red mud: Impact of residual Na2O

Catalyst samples for CH₄ decomposition were prepared from red mud (RM) by an acid-leaching neutralization precipitation approach. Water-washing the resultant precipitates multiple times, followed by drying at 105 °C and calcination at 500 °C, resulted in a threshold of residual Na₂O, equivalent to 96% Na₂O removal. Drying the precipitate at a higher temperature of 200 °C, followed by repeated water washing, provided a deeper Na₂O removal of 99% and made the resultant samples more active for the targeted reaction. Subsequently, four catalyst samples with a simulated red mud composition and NaOH contents from 0 to 0.3 wt% were prepared and the catalytic test results revealed that the Na₂O remaining in the RM-derived catalysts did not only inhibit their activation in CH₄ but also lower their maximal activities for CH₄ decomposition. Finally, two catalysts with the same simulated red mud composition and their Na impregnated respectively on Fe₂O₃ and a mixture support of Al₂O₃-SiO₂-TiO₂ were prepared and tested to explore the effect of Na distribution on the activation behavior of RM-derived catalysts for CH₄ decomposition. The activity testing results showed that it was the Na residual dispersed on iron oxides in the RM-derived samples to significantly inhibit the activation of CH₄ decomposition.