Restrictions of nitric oxide electrocatalytic decomposition over perovskite cathode in presence of oxygen: Oxygen surface exchange and diffusion.

Nitric oxide (NO) abatement from engine exhaust is of great significance to alleviate air pollution and haze. Compared with the traditional selective catalytic reduction (SCR) technology, electrocatalytic decomposition of NO simplifies the reductant supply system and therefore avoids secondary pollution. In this study, typical perovskite La0.6Sr0.4CoxFe1-xO3-δ (LSCF) infiltrated by different dosages of nano ceria Ce0.9Gd0.1O1.9 (GDC) was used as composite cathodes, in order to explore the critical factors to restrain NO conversion in excess of O2. The results show that electron as reactive species transfers among NO, ABO3-type cathode and oxygen vacancy. The maximum of NO removal efficiency can reach 96.27 % in absence of O2 and up to 80.55 % in presence of 1% O2 in case of LSCF infiltrated by moderate dosages LSCF-GDC(2), which is superior to those of LSCF, LSCF-GDC(4) and LSM-GDC(nano) composite cathode. Compared to oxygen storage capacity (OSC) caused by the infiltration of nano ceria, higher surface oxygen exchange coefficient (kδ) and chemical diffusion coefficient (Dchem) lead to the significant decrease in polarization resistance (Rp), and consequently to the enhancement of NO removal in presence of O2. No matter what kind of oxygen deriving from oxygen reduction reaction (ORR) and NO reduction reaction (NORR), GDC infiltration into LSCF improves oxygen transport property and however, the property of cathode in ORR is dominant over in NORR in presence of O2. Moderate GDC loading has the highest oxygen transport kinetics, and oxygen surface exchange is faster than chemical diffusion, due to lower activation energy. Over loading of GDC with greater ohmic resistance (Rs) inversely influences the NO removal.

Adaptive neural network projection analytical fault-tolerant control of underwater salvage robot with event trigger.

Introduction: To solve the problem of control failure caused by system failure of deep-water salvage equipment under severe sea conditions, an event-triggered fault-tolerant control method (PEFC) based on proportional logarithmic projection analysis is proposed innovatively. Methods: First, taking the claw-type underwater salvage robot as the research object, amore universal thruster fault model was established to describe the fault state of equipment failure, interruption, stuck, and poor contact. Second, the controller was designed by the proportional logarithmic projection analytical method. The system input signal was amplified and projected as a virtual input, which replaces the original input to isolate and learn the fault factor online by the analytical algorithm. The terminal sliding mode observer was used to compensate for the external disturbance of the system, and the adaptive neural network was used to fit the dynamic uncertainty of the system. The system input was introduced into the event-triggered mechanism to reduce the output regulation frequency of the fault thruster. Results: Finally, the simulation results showed that the method adopted in this study reduced the power output by 28.95% and the update frequency of power output by 75% compared with the traditional adaptive overdrive fault-tolerant control (AOFC) method and realized accurate pose tracking under external disturbance and system dynamic uncertain disturbance. Discussion: It has been proven that the algorithm used in this research can still reasonably allocate power to reduce the load of a fault thruster and complete the tracking task under fault conditions.

Electrochemical Performance of Ba0.5Sr0.5Co0.8Fe0.2O3-δ in Symmetric Cells With Sm0.2Ce0.8O1.9 Electrolyte for Nitric Oxide Reduction Reaction.

The emission of nitric oxide from the combustion process of fossil fuels causes air pollution problems. In addition to traditional removal methods, nitric oxide can be removed by the electrochemical reduction method. In this study, Ba0.5Sr0.5Co0.8Fe0.2O3-δ powders were synthesized using a solid-state reaction method. Symmetrical cells, with Sm0.2Ce0.8O1.9 as the electrolyte and Ba0.5Sr0.5Co0.8Fe0.2O3-δ as the electrodes, were prepared as the electrochemical reactor for nitric oxide reduction. In the process of electrochemical reduction, nitric oxide reduction occurs at the cathode and oxygen evolution occurs at the anode. To study the nitric oxide reduction performance of the electrode, impedances of the symmetrical cell in different atmospheres were analyzed. For the nitric oxide conversion in symmetric cells, two different modes, dual chamber and single chamber, were applied. Results demonstrated that the denitrification performance of the double chamber was better but the single chamber mode had other advantages in its simple structure. Presliminary stability results of the single chamber symmetric cell show that the electrochemical reduction of nitric oxide in symmetric cells with BSCF performed most reliably.

Non-destructive prediction of protein content in wheat using NIRS.

A steady and accurate model used for quality detection depends on precise data and appropriate analytical methods. In this study, the authors applied partial least square regression (PLSR) to construct a model based on the spectral data measured to predict the protein content in wheat, and proposed a new method, global search method, to select PLSR components. In order to select representative and universal samples for modeling, Monte Carlo cross validation (MCCV) was proposed as a tool to detect outliers, and identified 4 outlier samples. Additionally, improved simulated annealing (ISA) combined with PLSR was employed to select most effective variables from spectral data, the data's dimensionality reduced from 100 to 57, and the standard error of prediction (SEP) decreased from 0.0716 to 0.0565 for prediction set, as well as the correlation coefficients (R2) between the predicted and actual protein content of wheat increased from 0.9989 to 0.9994. In order to reduce the dimensionality of the data further, successive projections algorithm (SPA) was then used, the combination of these two methods was called ISA-SPA. The results indicated that calibration model built using ISA-SPA on 14 effective variables achieved the optimal performance for prediction of protein content in wheat comparing with other developed PLSR models (ISA or SPA) by comprehensively considering the accuracy, robustness, and complexity of models. The coefficient of determination increased to 0.9986 and the SEP decreased to 0.0528, respectively.

Carbonation of gypsum from wet flue gas desulfurization process: experiments and modeling.

In this paper, waste gypsum from wet flue gas desulfurization (WFGD) mixed with NH3·H2O was applied for CO2 absorption in the solid-liquid-gas phase system. The effects of operation temperature, CO2 flow rates, and ammonia-to-gypsum ratio on carbonation process were discussed. Meanwhile, a model for CO2 absorption in the suspension of WFGD gypsum and ammonia was established. The results indicate that higher temperature favors the reaction, and WFGD gypsum conversion can be achieved above 90% even at lower ammonia-to-gypsum ratio, while CO2 conversion reaches 90% and ammonia utilization is up to 83.69%. The model fits well with the experimental results at various CO2 flow rates and predicts the concentration distribution of the main species, including CO2 absorbed, NH2COO-, and HCO3-.

Application of Visible/Near-Infrared Spectroscopy in the Prediction of Azodicarbonamide in Wheat Flour.

Azodicarbonamide is wildly used in flour industry as a flour gluten fortifier in many countries, but it was proved by some researches to be dangerous or unhealthy for people and not suitable to be added in flour. Applying a rapid, convenient, and noninvasive technique in food analytical procedure for the safety inspection has become an urgent need. This paper used Vis/NIR reflectance spectroscopy analysis technology, which is based on the physical property analysis to predict the concentration of azodicarbonamide in flour. Spectral data in range from 400 to 2498 nm were obtained by scanning 101 samples which were prepared using the stepwise dilution method. Furthermore, the combination of leave-one-out cross-validation and Mahalanobis distance method was used to eliminate abnormal spectral data, and correlation coefficient method was used to choose characteristic wavebands. Partial least squares, back propagation neural network, and radial basis function were used to establish prediction model separately. By comparing the prediction results between 3 models, the radial basis function model has the best prediction results whose correlation coefficients (R), root mean square error of prediction (RMSEP), and ratio of performance to deviation (RPD) reached 0.99996, 0.5467, and 116.5858, respectively. PRACTICAL APPLICATION: Azodicarbonamide has been banned or limited in many countries. This paper proposes a method to predict azodicarbonamide concentrate in wheat flour, which will be used for a rapid, convenient, and noninvasive detection device.