Electrochemical Removal of Carbon Monoxide in Reformate Hydrogen for Fueling Proton Exchange Membrane Fuel Cells

A twin-cell electrochemical filter is demonstrated to reduce the CO concentration in reformate hydrogen. In this design, the potential and gas flow are switched between the two filter cells so that alternative CO adsorption and oxidation occur in each cell while providing a continuous flow of H2 to a fuel cell. The effects of filter switching time and applied potential on the CO concentration of gas exiting the filter are presented here for a CO concentration of 1000 ppm in nitrogen flowing at 100 cm3/min. The parasitic loss of hydrogen from a corresponding reformate stream was estimated to be 1.5%

Experimental Performance Analysis Of Free And Forced Fully Developed Air Flow Green House Solar Dryer Using Curry Leaves

The world is beginning to move away from its consumption of fossil fuels. Various technologies are being developed to make use of renewable energy sources such as wind, solar, and tidal, etc. Solar energy is the best choice among these sources because of it is readily available, abundant, and capable of producing both electric energy and space heating. Solar energy can be used directly or indirectly to dry agricultural and non-agricultural products to preserve them for long a period without formation of fungi. Drying of herbal leaves is an important process in Siddha and Ayurvedic industries to produce herbal medicines in power form. However, as herbal leaves are dried in the open sun, they are susceptible to environmental factors such as rain, insects, and livestock. These disadvantages of open-air drying shall be overwhelmed by greenhouse solar dryer. Greenhouse solar dryer with natural convection, forced convection with hot air supply are the existing methods, but when supplied with hot air, the rise in temperature leads to nutrient loss in herbal leaves. In order to avoid this loss in nutrients, the current work gives a solution that the temperature of forced convection greenhouse dryer can be reduced and controlled by supplying the ambient air at inlet flow in a fully developed air region, and this method can also leads to reduction in colour loss with possibly same or higher drying rate compare to natural convection greenhouse dryer

Performance Comparison of Tray, Bed and Integrated Drying Chamber in Closed Loop Heat Pump Dryer for Bermuda Grass

Drying plays a crucial role in various industries such as food production, agriculture, Siddha, Ayurveda, and medical fields. To achieve controlled drying conditions, a heat pump dryer is considered an effective method, allowing for precise control of parameters like temperature, humidity, and air velocity. In this study, a heat pump dryer was designed and constructed to investigate the drying characteristics of Bermuda grass (Cynodon dactylon) at different velocities (1.5 m/s, 2.0 m/s, and 2.5 m/s) using three types of drying chambers: fluidized bed dryer, tray dryer, and combined dryer (a combination of bed and tray). The heat pump system utilized R134a as the refrigerant. The performance of the heat pump dryer in the three drying chambers was analyzed using Bermuda grass as the drying product. The Moisture Removal Rate (MRR) was calculated for various combinations of velocity and drying chamber, and it was observed that the combined dryer achieved a higher MRR at all three velocities compared to the tray and fluidized bed dryers

Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks

Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, we propose a novel neuro-evolution algorithm based on ant colony optimization (ACO), called Ant-based Neural Topology Search (ANTS), for directly optimizing RNN topologies. The procedure selects from multiple modern recurrent cell types such as ∆-RNN, GRU, LSTM, MGU and UGRNN cells, as well as recurrent connections which may span multiple layers and/or steps of time. In order to introduce an inductive bias that encourages the formation of sparser synaptic connectivity patterns, we investigate several variations of the core algorithm. We do so primarily by formulating different functions that drive the underlying pheromone simulation process (which mimic L1 and L2 regularization in standard machine learning) as well as by introducing ant agents with specialized roles (inspired by how real ant colonies operate), i.e., explorer ants that construct the initial feed forward structure and social ants which select nodes from the feed forward connections to subsequently craft recurrent memory structures. We also incorporate communal intelligence, where best weights are shared by the ant colony for weight initialization, reducing the number of backpropagation epochs required to locally train candidate RNNs, speeding up the neuro-evolution process. Our results demonstrate that the sparser RNNs evolved by ANTS significantly outperform traditional one and two layer architectures consisting of modern memory cells, as well as the well-known NEAT algorithm. Furthermore, we improve upon prior state-of-the-art results on the time series dataset utilized in our experiments

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

A hybrid approach for feature subset selection using ant colony optimization and artificial neural networks

Feature selection deals with selecting a subset of feature from a data set to predict the output with an acceptable level of accuracy. Feature selection problems have been solved previously by researchers using various meta-heuristic algorithms like branch and bound method, genetic algorithm, simulated annealing etc. This thesis presents a hybrid approach using artificial neural network and ant colony optimization, which would find out the inter-variable relationship amongst a subset of feature, if any, to predict the output accurately --Abstract, page iv

Electrochemical Oxidation of Carbon Monoxide in Reformate Hydrogen for PEM Fuel Cells

Carbon monoxide (CO) poisoning of platinum anode significantly decreases the performance of proton exchange membrane fuel cells (PEMFC). Various techniques studied to remove CO from source or mitigate CO poisoning have met with limited success due to fuel loss or increases infrastructure requirements to meet the operating needs. We developed a twin cell electrochemical filter that is similar in construction of the PEMFC and remove CO with minimal fuel loss. In this CO in fuel gas is adsorbed on a Pt electrode and oxidized by applying a pulse potential. The CO kinetics on Pt electrode was characterized by employing cyclic voltammetry and chronoamperometry techniques. A fixed bed adsorber model was used to characterize the adsorption part of the filtering. The model was validated by demonstrating as a filter cell. The performance was compared with other CO removing techniques

Replication Data for: Measuring Candidate Selection Mechanisms in European Elections: Comparing Formal Party Rules to Candidate Survey Responses

Students of party organization often rely on politicians' perceptions when measuring internal party institutions and organizational characteristics. We compare a commonly used survey measure of political parties' European Parliament candidate selection mechanisms to measures that the authors coded directly from parties' selection rules. We find substantial disconnect between formal institutions and survey respondent perceptions of selection mechanisms, raising questions about measure accuracy and equivalency. While this divergence may be driven either by distinctions between de jure and de facto selection procedures or by respondent error, we find the differences between the two measures are unsystematic. Our findings suggest authors studying party characteristics must decide whether their research question calls for survey or formal institutional measures