APPLICATION OF NEURAL NETWORKS FOR THE DEFINITION OF THE OPERATING CONDITIONS OF FLUIDIZED BED POLYMERIZATION REACTORS

In this work, thermal activation of pistachio shell was studied in a fluidized bed reactor. The effects of operating conditions on the pore development within the char particles were studied experimentally. The results showed that activation temperature, residence time, oxidizing gas type and concentration have main effects on the Iodine adsorption capacity of the product. The highest surface area was obtained using steam activation at temperature 850°C for 45 min. The synthesis process was also simulated using artificial neural networks (ANNs) to estimate the Iodine number of the product. The present work applied a Tan-sigmoid transfer function in three layers in the feed forward neural network with back propagation algorithm. The results of the network are in good agreement with the experimental data with a maximum relative deviation of 0.015%.

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Comparative Bio-sorption of Cadmium and Nickel Ions from Aqueous Solution onto Fibers of Date Palm using Fluidized Bed Column

Revista de Chimie ◽

10.37358/rc.19.5.7160 ◽

2019 ◽

Vol 70 (5) ◽

pp. 1507-1512

Author(s):

Baker M. Abod ◽

Ramy Mohamed Jebir Al-Alawy ◽

Firas Hashim Kamar ◽

Gheorghe Nechifor

Keyword(s):

Aqueous Solution ◽

Heavy Metal ◽

Metal Ions ◽

Fluidized Bed ◽

Removal Efficiency ◽

Heavy Metal Ions ◽

Date Palm ◽

Operating Conditions ◽

Initial Concentration ◽

Bed Height

The aim of this study is to use the dry fibers of date palm as low-cost biosorbent for the removal of Cd(II), and Ni(II) ions from aqueous solution by fluidized bed column. The effects of many operating conditions such as superficial velocity, static bed height, and initial concentration on the removal efficiency of metal ions were investigated. FTIR analyses clarified that hydroxyl, amine and carboxyl groups could be very effective for bio-sorption of these heavy metal ions. SEM images showed that dry fibers of date palm have a high porosity and that metal ions can be trapped and sorbed into pores. The results show that a bed height of 6 cm, velocity of 1.1Umf and initial concentration for each heavy metal ions of 50 mg/L are most feasible and give high removal efficiency. The fluidized bed reactor was modeled using ideal plug flow and this model was solved numerically by utilizing the MATLAB software for fitting the measured breakthrough results. The breakthrough curves for metal ions gave the order of bio-sorption capacity as follow: Cd(II)]Ni(II).

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A theoretical and numerical approach for selecting miniaturized antenna topologies on magneto-dielectric substrates

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078715000859 ◽

2015 ◽

Vol 7 (3-4) ◽

pp. 369-377 ◽

Cited By ~ 1

Author(s):

Alex Pacini ◽

Alessandra Costanzo ◽

Diego Masotti

Keyword(s):

Near Field ◽

Dielectric Materials ◽

Numerical Approach ◽

Operating Conditions ◽

Microwave Range ◽

Dielectric Substrates ◽

Full Wave ◽

Miniaturized Antenna ◽

Wavelength Radiation ◽

Definition Of

An increasing interest is arising in developing miniaturized antennas in the microwave range. However, even when the adopted antennas dimensions are small compared with the wavelength, radiation performances have to be preserved to keep the system-operating conditions. For this purpose, magneto-dielectric materials are currently exploited as promising substrates, which allows us to reduce antenna dimensions by exploiting both relative permittivity and permeability. In this paper, we address generic antennas in resonant conditions and we develop a general theoretical approach, not based on simplified equivalent models, to establish topologies most suitable for exploiting high permeability and/or high-permittivity substrates, for miniaturization purposes. A novel definition of the region pertaining to the antenna near-field and of the associated field strength is proposed. It is then showed that radiation efficiency and bandwidth can be preserved only by a selected combinations of antenna topologies and substrate characteristics. Indeed, by the proposed independent approach, we confirm that non-dispersive magneto-dielectric materials with relative permeability greater than unit, can be efficiently adopted only by antennas that are mainly represented by equivalent magnetic sources. Conversely, if equivalent electric sources are involved, the antenna performances are significantly degraded. The theoretical results are validated by full-wave numerical simulations of reference topologies.

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Study on De-Nitrification of Improved Step-Feed Progress

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.703.171 ◽

2014 ◽

Vol 703 ◽

pp. 171-174

Author(s):

Bing Wang ◽

Yi Xiao ◽

Shou Hui Tong ◽

Lan Fang ◽

Da Hai You ◽

...

Keyword(s):

Water Quality ◽

Organic Matter ◽

Fluidized Bed ◽

Removal Efficiency ◽

Nitrogen Removal ◽

Removal Rate ◽

Operating Conditions ◽

Removal Mechanism ◽

Aerobic Zone

Improved step-feed de-nitrification progress combined with biological fluidized bed was introduced in this study. The progress had good performance and capacity of de-nitrification and organic matter. The experiment result showed that the de-nitrification efficiency of the improved biological fluidized bed with step-feed process was higher than the fluidized bed A/O process under the same water quality and the operating conditions. When the influent proportion of each segment was equal, the system showed good nitrogen removal efficiency with the change of influent C/N ratio, HRT and sludge return ratio. The removal rate of TN reached up to 88.2%. It showed that the simultaneous nitrification and de-nitrification phenomenon happened in the aerobic zone. The nitrogen removal mechanism was also studied.

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Improving the Volumetric Efficiency of the Axial Piston Pump

Journal of Mechanical Design ◽

10.1115/1.4007361 ◽

2012 ◽

Vol 134 (11) ◽

Cited By ~ 9

Author(s):

Shu Wang

Keyword(s):

Operating Conditions ◽

Volumetric Efficiency ◽

Valve Plate ◽

Piston Pump ◽

Efficient Design ◽

Axial Piston Pumps ◽

Definition Of ◽

Engineering Practices ◽

First Time ◽

Axial Piston

The volumetric efficiency is one of the most important aspects of system performance in the design of axial piston pumps. From the standpoint of engineering practices, the geometric complexities of the valve plate (VP) and its multiple interactions with pump dynamics pose difficult obstacles for optimization of the design. This research uses the significant concept of pressure carryover to develop the mathematical relationship between the geometry of the valve plate and the volumetric efficiency of the piston pump. For the first time, the resulting expression presents the theoretical considerations of the fluid operating conditions, the efficiency of axial piston pumps, and the valve plate designs. New terminology, such as discrepancy of pressure carryover (DPC) and carryover cross-porting (CoCp), is introduced to explain the fundamental principles. The important results derived from this study can provide clear recommendations for the definition of the geometries required to achieve an efficient design, especially for the valve plate timings. The theoretical results are validated by simulations and experiments conducted by testing multiple valve plates under various operating conditions.

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DeepFogSim: A Toolbox for Execution and Performance Evaluation of the Inference Phase of Conditional Deep Neural Networks with Early Exits Atop Distributed Fog Platforms

Applied Sciences ◽

10.3390/app11010377 ◽

2021 ◽

Vol 11 (1) ◽

pp. 377

Author(s):

Michele Scarpiniti ◽

Enzo Baccarelli ◽

Alireza Momenzadeh ◽

Sima Sarv Ahrabi

Keyword(s):

Neural Networks ◽

Real Time ◽

Future Internet ◽

Operating Conditions ◽

Graphic User Interface ◽

Energy Aware ◽

Internet Applications ◽

The Real ◽

Delay Performance ◽

Hard Constraints

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox.

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Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

Volume 6B: Energy ◽

10.1115/imece2015-51723 ◽

2015 ◽

Cited By ~ 1

Author(s):

M. A. Rafe Biswas ◽

Melvin D. Robinson

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Fuel Cell ◽

Direct Methanol Fuel Cell ◽

Cell Voltage ◽

Operating Conditions ◽

Direct Methanol ◽

Artificial Neural ◽

Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

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