Effect of mass transfer on the deactivation model and GPLE parameters of Co/γ-Al2O3 catalysts in the Fischer Tropsch synthesis

The activity of catalysts with various sizes was compared in a fixed-bed Fischer–Tropsch reactor under similar operating conditions by determining the deactivation model. Catalyst size had no impact on the type of deactivation model. The smaller catalyst showed a smaller deactivation constant of catalyst (k_d) and a lower deactivation rate in the initial stage. The decline in the activities of the catalyst with a mesh size of 40 was lower than the other catalysts, suggesting its higher long-term stability (a_ss). Larger catalyst sizes led to the fouling of carbon and heavy hydrocarbons, decreasing the specific surface of the catalyst, thus increasing the pore diffusion resistance and further decrementing the catalyst activities.     

Investigation of liquid water heterogeneities in large area proton exchange membrane fuel cells using a Darcy two-phase flow model in a multiphysics code

Proper management of the liquid water and heat produced in proton exchange membrane (PEM) fuel cells remains crucial to increase both its performance and durability. In this study, a two-phase flow and multicomponent model, called two-fluid model, is developed in the commercial COMSOL Multiphysics® software to investigate the liquid water heterogeneities in large area PEM fuel cells, considering the real flow fields in the bipolar plate. A macroscopic pseudo-3D multi-layers approach has been chosen and generalized Darcy's relation is used both in the membrane-electrode assembly (MEA) and in the channel. The model considers two-phase flow and gas convection and diffusion coupled with electrochemistry and water transport through the membrane. The numerical results are compared to one-fluid model results and liquid water measurements obtained by neutron imaging for several operating conditions. Finally, according to the good agreement between the two-fluid and experimentation results, the numerical water distribution is examined in each component of the cell, exhibiting very heterogeneous water thickness over the cell surface.     

Recent insights in synthesis and energy storage applications of porous carbon derived from biomass waste: A review

In the last few decades, global warming, environmental pollution, and an energy shortage of fossil fuel may cause a severe economic crisis and health threats. Storage, conversion, and application of regenerable and dispersive energy would be a promising solution to release this crisis. The development of porous carbon materials from regenerated biomass are competent methods to store energy with high performance and limited environmental damages. In this regard, bio-carbon with abundant surface functional groups and an easily tunable three-dimensional porous structure may be a potential candidate as a sustainable and green carbon material. Up to now, although some literature has screened the biomass source, reaction temperature, and activator dosage during thermochemical synthesis, a comprehensive evaluation and a detailed discussion of the relationship between raw materials, preparation methods, and the structural and chemical properties of carbon materials are still lacking. Hence, in this review, we first assess the recent advancements in carbonization and activation process of biomass with different compositions and the activity performance in various energy storage applications including supercapacitors, lithium-ion batteries, and hydrogen storage, highlighting the mechanisms and open questions in current energy society. After that, the connections between preparation methods and porous carbon properties including specific surface area, pore volume, and surface chemistry are reviewed in detail. Importantly, we discuss the relationship between the pore structure of prepared porous carbon with surface functional groups, and the energy storage performance in various energy storage fields for different biomass sources and thermal conversion methods. Finally, the conclusion and prospective are concluded to give an outlook for the development of biomass carbon materials, and energy storage applications technologies. This review demonstrates significant potentials for energy applications of biomass materials, and it is expected to inspire new discoveries to promote practical applications of biomass materials in more energy storage and conversion fields.     

Experimental research on combined effect of obstacle and local spraying water fog on hydrogen/air premixed explosion

In order to reveal the mechanism of water fog explosion suppression and research the combined effect of water fog and obstacle on hydrogen/air deflagration, multiple sets of experiments were set up. The results show that the instability of thermal diffusion under lean combustion conditions is the main influencing factor of hydrogen/air flame surface instability, and the existence of water fog will aggravate the hydrogen/air flame surface instability. When obstacle is not considered, 8 μm, 15 μm, 30 μm water fog can significantly reduce the flame velocity and explosion overpressure of hydrogen/air, 45 μm fine water fog plays the opposite role. When considering the relative position of the water fog release position and the obstacle, the 8 μm, 15 μm, 30 μm water fog has almost no suppression effect when released near the obstacle, but a significant suppression effect occur, when using the 45 μm water fog. In the field of theoretical research, the research results not only provide an experimental basis for the fine water fog to reduce the consequences of hydrogen explosion accidents, and the optimal diameter range used by the water fog, but also provide experimental reference for the numerical simulation of hydrogen/air explosion suppression in semi-open space, and promote the development of hydrogen explosion suppression theory. In terms of engineering applications, this study can provide a theoretical basis for the layout of fire fighting equipment in the engine room of nuclear power plants or hydrogen-powered ships.     

Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference

Sweet pickled mango named Ma-Muang Bao Chae-Im is a traditional preserved mango from Hat Yai, Thailand. This study investigated (I) volatile and non-volatile compound profiles of commercial Ma-Muang Bao Chae-Im and (II) their relationship to consumer preference. Untargeted metabolomics profiling was performed by gas chromatography-mass quadrupole-time of flight analysis. There were 117 volatile and 44 non-volatile compounds annotated in six commercial brands of Ma-Muang Bao Chae-Im. Furthermore, 46 volatile and 19 non-volatile compounds’ discriminant markers were found by Partial least square discriminant analysis. Among those markers, sorbic and benzoic acid were observed in several brands; moreover, the combination of both compounds altered the volatile profile, especially the ester group. Partial least square regression revealed that overall consumer liking is correlated to 1-heptanol; 1-octanol; acetoin; acetic acid, 2-phenylethyl ester; D-manitol; terpenes and terpenoids, while firmness to sucrose and L-(-)-sorbofuranose. On the other hand, most ester compounds were not related to consumer preference.     

Polymer electrets and their applications

Polymer electrets have revealed great potential application in electromechanical devices because of the low weight, large quasi-piezoelectric sensitivity, and excellent flexibility. For an electret, a permanent and macroscopic electric field exists on the surface, principally led by a macroscopic electrostatic charge on the surface or a net orientation of polar groups inside the object. Here, progress in the development of polymer electrets is reviewed. After a brief retrospect of the research courses and those typical polymer electrets that are classified into fluorine polymer and nonfluorine polymer, we present a survey on the charging methods, including corona, soft X-ray, contact, thermal and monoenergetic particle beams. The latest representative applications (i.e., power harvesting, sensors, field effect transistors, and biomedicine) based on polymer electrets are also summarized. Finally, we complete this review with a discussion on perspectives and challenges in this field.     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.