Investigation of wood pellets self-heating kinetics and thermal runaway phenomena using lab scale experiments

The phenomena of spontaneous combustion and thermal runaway in wood pellets storage were investigated using lab-scale experiments in the temperature range of 100–200 °C. The critical temperatures were determined for four sizes of reactors. The kinetic parameters of the self-heating were determined using three methods, the Frank-Kamenetskii's method, crossing point method, and numerical curve fitting method. Mean values of activation energy (E) of 78.7 ±0.8 kJ/mol and self-heating rate constant (∆ _rhA) of (4.22 ±2.5) × 10 ⁶ kJ/(kg s) were obtained for four type of wood pellets (made from whitewood) samples from different pellet producers in British Columbia. Finally, a two-dimensional numerical model was developed to predict the temperature development during self-heating and the critical temperature for known sizes of reactors.     

Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one. This process can take up a large amount of resource real estate, and as we have laid out in this paper, by using a Model consisting of various Neural Networks trained on a custom dataset. It can be automated into assisting designers, allowing them to focus on the other more complicated parts of the system they are designing by quickly generating what would rather be straightforward busywork. Over the past 20 years, the boom in how much the internet is used and the sheer volume of pages on it demands a high level of work and time to create them. For the efficiency of the process, we proposed a multi-model-based architecture on image captioning, consisting of Convolutional neural network (CNN) and Long short-term memory (LSTM) models. Our proposed approach trained on our custom-made database can be automated into assisting designers, allowing them to focus on the other more complicated part of the system. We trained our model in several batches over a custom-made dataset consisting of over 6300 files and were finally able to achieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50 hand-drawn images at 87.86%     

Calibration of low-pressure MEMS gas sensor for detection of hydrogen gas

Detection of hydrogen by sensors are significant for improvement and safe usage of hydrogen gas as an energy source. In this paper, the application of the MEMS gas sensor for detection of hydrogen gas is numerically studied to develop the application of this device in different industrial applications. The flow feature and force generation mechanism inside a rectangular enclosure with heat and cold arms as the non-isothermal walls are inclusively discussed. In this study, the pressure of hydrogen is varied from 62 to 1500 pa correspond to Knudsen number from 0.1 to 4.5 to investigate all characteristics of the thermal-driven force inside the MEMS sensor. In order to simulate a rarefied gas inside the micro gas detector, Boltzmann equations are applied to obtain high precision results. To solve these equations, Direct Simulation Monte Carlo (DSMC) approach is used as a robust method for the non-equilibrium flow field. The effects of length, thickness and temperature of arms are comprehensively investigated in different ambient pressures. In addition, the effect of various hydrogen concentrations on the Knudsen force is studied. Our findings show that maximum Knudsen force occurs at P = 387 pressure and intensifies when the length of the arms is increased from 50 μm to 150 μm. In addition, the obtained results demonstrate that the generated force is highly sensitive to hydrogen gas species and this enables device for detection of hydrogen gas.     

A robust scheduling for the multi-mode project scheduling problem with a given deadline under uncertainty of activity duration

The problem of this paper deals with the multi-mode project scheduling problem under uncertainty of activity duration where only the renewable resources are taken into account and a given deadline has to be met at the cost of recruiting additional resources. A heuristic algorithm is employed to solve this problem, and to maintain the robustness of the baseline schedule, the concept of critical chain project management (CCPM) is applied in which a new definition to resource buffer is considered. A simulation methodology is used to determine the size and location of resource buffers in the schedules in which three different buffer sizes and three different uncertainty levels are considered. Results and analysis of the simulation outcomes illustrate that resource buffers are useful and should be simulated by the CCPM schedules, as they help to decrease the total duration of the project during implementation and meet the deadline of the project with more assurance.     

Optical and photocatalytic properties of the Fe-doped TiO2 nanoparticles loaded on the activated carbon

In this work, Fe-doped (1?wt%) TiO₂ loaded on the activated carbon nano-composite was prepared using a sol-gel method. A prepared nano-composite was characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), BET surface area, Fourier transform infrared spectroscopy (FTIR), photoluminescence (PL) spectroscopy and UV–Vis diffuse reflectance spectroscopy (DRS). The photocatalytic activity of the nano-composite was evaluated through degradation of synthetic textile wastewater, reactive red 198, under visible light irradiations. The XRD result indicated that the TiO₂ nano-composite contained only anatase phase. The surface area of the TiO₂ increased from 48?m²/g to 100?m²/g through the fabrication of the nano-composite. The FE-SEM results indicate that the TiO₂ particles with an average particle size of 35–70?nm can be deposited homogeneously on the activated carbon surface. DRS showed that the Fe doping in the TiO₂ -activated carbon nano-composite induced a significant red shift of the absorption edge and then the band gap energy decreased from 3.3 to 2.9?eV. Photocatalytic results indicated that the photocatalytic activity of the Fe doped TiO₂ increased under visible light irradiation in the presence of the activated carbon.     

Highly Conductive Ti3C2Tx MXene Hybrid Fibers for Flexible and Elastic Fiber‐Shaped Supercapacitors

Fiber‐shaped supercapacitors (FSCs) are promising energy storage solutions for powering miniaturized or wearable electronics. However, the scalable fabrication of fiber electrodes with high electrical conductivity and excellent energy storage performance for use in FSCs remains a challenge. Here, an easily scalable one‐step wet‐spinning approach is reported to fabricate highly conductive fibers using hybrid formulations of Ti₃C₂T_x MXene nanosheets and poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate. This approach produces fibers with a record conductivity of ≈1489 S cm^?1, which is about five times higher than other reported Ti₃C₂T_x MXene‐based fibers (up to ≈290 S cm^?1). The hybrid fiber at ≈70 wt% MXene shows a high volumetric capacitance (≈614.5 F cm^?3 at 5 mV s^?1) and an excellent rate performance (≈375.2 F cm^?3 at 1000 mV s^?1). When assembled into a free‐standing FSC, the energy and power densities of the device reach ≈7.13 Wh cm^?3 and ≈8249 mW cm^?3, respectively. The excellent strength and flexibility of the hybrid fibers allow them to be wrapped on a silicone elastomer fiber to achieve an elastic FSC with 96% capacitance retention when cyclically stretched to 100% strain. This work demonstrates the potential of MXene‐based fiber electrodes and their scalable production for fiber‐based energy storage applications.     

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.     

Incremental one-class classifier based on convex–concave hull

Pattern Analysis and Applications - One subject that has been considered less is a binary classification on data streams with concept drifting in which only information of one class (target class)...     

A decoupled source current reconstruction method for noisy and reactive near‐field to far‐field transformation

In this article, a decoupled source current reconstruction method (SRM) for noisy and reactive near‐field (NF) to far‐field (FF) transformation is introduced. It is shown that the traditional SRM for NF/FF transformation shows instability in the regions that the amounts of noise or reactive radiations are noticeable. Therefore, in these regions, equivalent currents should be determined from a Tikhonov SRM equation. However, this equation increases the computational cost of the SRM. To simplify the Tikhonov SRM equation, a Tikhonov radial field retrieval algorithm is also proposed. In this algorithm, the Tikhonov integral equation is decoupled by considering and retrieving the radial components of the electric field. Results of far‐field calculation with both the proposed Tikhonov SRM equation and Tikhonov radial field retrieval algorithm with three different antennas are presented and compared with those of the full‐wave simulation and measurements. The results show more accurate field transformation with the proposed algorithms.     

Deactivation of Pt/wire-mesh and vanadia/monolith catalysts applied in selective catalytic reduction of NO<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript> in flue gas

In this study the deactivation of Pt/wire mesh and vanadia/monolith catalysts by aerosol particles of some inorganic salts (K₂SO₄, KCl and ZnCl₂) with high or low melting points has been investigated. The aerosol particles may either diffuse within the matrix of the catalysts and block the mezzo and micro pores, or deposit on the outer surface of the catalysts and form a porous layer causing a mass transfer resistance that ultimately deactivates the catalysts. It has been observed that in both Pt/wire mesh and vanadia/monolith catalysts the deactivation effect of ZnCl₂ was more pronouced compared to other salts. As an example, after 31 hours of exposure to ZnCl₂, 10% of the catalysts activities was lost. This may be related to the ZnCl₂ lower melting point in comparison with other poisons. These results are in agreement with the previous findings for deactivation of wire-mesh catalysts used for oxidation of volatile organic compounds (VOC) and CO by exposing the catalysts to the aerosols generated from inorganic salts.