Numerical investigation of pressure drop characteristics of gas channels and U and Z type manifolds of a PEM fuel cell stack

Fluid flow manifold plays a significant role in the performance of a fuel cell stack because it affects the pressure drop, reactants distribution uniformity and flow losses, significantly. In this study, the flow distribution and the pressure drop in the gas channels including the inlet and outlet manifolds, with U- and Z-type arrangements, of a 10-cell PEM fuel cell stack are analyzed at anode and cathode sides and the effects of inlet reactant stoichiometry and manifold hydraulic diameter on the pressure drop are investigated. Furthermore, the effect of relative humidity of oxidants on the pressure drop of cathode are investigated. The results indicate that increase of the manifold hydraulic diameter leads to decrease of the pressure drop and a more uniform flow distribution at the cathode side when air is used as oxidant while utilization of humidified oxidant results in increase of pressure drop. It is demonstrated that for the inlet stoichiometry of 2 and U type manifold arrangement when the relative humidity increases from 25% to 75%, the pressure drop increases by 60.12% and 116.14% for oxygen and air, respectively. It is concluded that there is not a significant difference in pressure drop of U- and Z-type arrangements when oxygen is used as oxidant. When air is used as oxidant, the effect of manifold type arrangement is more significant than other cases, and increase of the stoichiometry ratio from 1.25 to 2.5 leads to increase of pressure drop by 527.3%.     

H2S Reactive Absorption from Off‐Gas in a Spray Column: Insights from Experiments and Modeling

H₂S removal from an off‐gas stream was performed in a spray column by H₂S reactive absorption into a NaOH solution. The individual and interactive effects of three independent operating variables on the percentage of absorbed H₂S were investigated: the initial pH of the scrubbing solution, the initial scrubbing solution temperature, and the volumetric liquid‐to‐gas ratio. The optimum operating variables were determined by response surface methodology (RSM) attaining a percentage of absorbed H₂S of 98.7 ± 0.2 %. Additionally, the process performance was modeled by an artificial neural network (ANN) to predict the percentage of absorbed H₂S. The results showed that the experimental data agreed better with the ANN model than with the RSM results.     

Heterogeneous Catalytic Polymerization of Ethylene in Microtubular Reactor Systems

The feasibility of using a microtubular reactor for heterogeneous polymerization of ethylene was investigated experimentally. Chemically inert polymer tubing of 800–2300 μm in inner diameter was fabricated and used as a polymerization reactor. Nonporous silica nanoparticles with a diameter of 400 nm were synthesized and used as support for the high‐activity rac‐ethylene(indenyl)₂ZrCl₂ catalyst with methylaluminoxane as cocatalyst and toluene as diluent. Large‐diameter microtubular reactors were also successfully used to conduct heterogeneous polymerization of ethylene in continuous reaction operations. High initial catalyst activity was obtained and the overall polymerization activity per volume or reactor length was quite high. No particle fragmentation occurred and the polymer particles were covered with small subgrains or nanofibrils with a diameter of 30 nm.     

Engineering geological properties of the pyroclastic cone-shaped rocky houses of Kandovan,Iran

Bulletin of Engineering Geology and the Environment - Kandovan is one of the geo-tourism attractions in the East Azarbaijan Province of Iran, where rural houses were carved within the cone-shaped...     

Modeling of thermodynamic properties of carrot product using ALO,GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer

Engineering with Computers - In this paper, multi-stage continuous belt (MSCB) dryer was used for carrot slices drying. Experiments were performed at three air speeds (1, 1.5, and 2 m/s)...     

A Framework for Ground Water Management Based on Bayesian Network and MCDM Techniques

Groundwater resources are steadily subjected to increasing water demands. The aquifers are considered as the most accessible source of fresh water. In recent years, they have been faced with severe water withdrawal in arid and semi-arid countries like Iran and thus some aquifers was considered as forbidden aquifers that it means the water withdrawal from these aquifers is unauthorized. Given a critical situation, groundwater resources management in the form of tools such as monitoring the level of the aquifers and developing the restoring scenarios is essential. Therefore, for this purpose, a framework has been developed based on prediction of groundwater level using Bayesian Networks (BNs) model. Furthermore, Multi Criteria Decision Making methods (MCDM) techniques proposed and employed for ranking of proposed groundwater management scenarios. This framework was evaluated for restoring the Birjand aquifer in Iran in different hydrological conditions. A probabilistic Dynamic BN was proposed for groundwater level prediction under uncertainties. After analyzing the obtained results, the applicable short term scenarios for groundwater management as well as appropriate economic, social and technical criteria were defined for decision making procedure. Then, using elicitation of decision makers’ opinions on the relative importance and performance of criteria, SAW, TOPSIS and PROMETHEE-II techniques were applied to rank the scenarios and the obtained results were aggregated by Borda method for final ranking of the scenarios. Lastly, the final results demonstrates the capability of the proposed framework for groundwater resources planning and management which can be employed for reducing the risk of aquifer level declining.     

Enhanced optical and hydrophilic properties of Si and Cd co-doped TiO<Subscript>2</Subscript> thin films

In this study, preparation of Si and Cd co doped (5 mol% Si and 5–20 mol% Cd) TiO₂ dip-coated thin films on glass substrates via sol–gel process have been investigated. The samples were characterized by X-ray diffraction (XRD) and Scanning electron microscopy analysis after heat treatments. XRD results suggested that adding dopants has a great effect on crystallinity and particle size of TiO₂. Titania rutile phase formation was inhibited by Si⁴⁺ and promoted by Cd²⁺ doping. But the effect of Cd doped appeared at high concentration. Accordingly, the thin films showed various water contact angles. The water contact angles changed from 69.0° to 9.6° by changing the content of Cd doped.     

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile,the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.