scholarly journals Combustion Enhancement of Pulverized Coal with Targeted Oxygen-Enrichment in an Ironmaking Blast Furnace

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 440
Author(s):  
Zhenfeng Zhou ◽  
Ruihao Wang ◽  
Qiujie Yi ◽  
Guang Wang ◽  
Chunyuan Ma
Keyword(s):  
Blast Furnace ◽  
Coal Combustion ◽  
Particle Temperature ◽  
Combustion Process ◽  
Oxygen Enrichment ◽  
Oxygen Utilization ◽  
O2 Concentration ◽  
Computational Fluid Dynamics Cfd ◽  
Enrichment Technology ◽  
Cfd Method

In this study, a targeted oxygen-enrichment technology was proposed to enhance coal combustion in an ironmaking blast furnace. The coal flow and combustion characteristics under targeted oxygen-enrichment were investigated using the computational fluid dynamics (CFD) method. The results showed that oxygen utilization and coal burnout were significantly increased under targeted oxygen-enrichment. The coal burnout at 24% O2 concentration was 86.29%, which was the maximum and indicated an increase of 13.13%. However, the cooling effect of room-temperature oxygen had some adverse effects on coal combustion. Given this, the effect of coal particle temperature on coal combustion was investigated based on targeted oxygen-enrichment. The coal combustion process was further enhanced. The coal burnout at a 600 K particle temperature and 25% oxygen concentration was 91.12% and had an increase of 17.96%, which was the maximum.

scholarly journals Simulation of Thermal Effects on the Flow Field in a Pilot-Scale Kiln

2021 ◽  
Author(s):  
I. A. Sofia Larsson ◽  
Anna-Lena Ljung ◽  
B. Daniel Marjavaara
Keyword(s):  
Flow Field ◽  
Coal Combustion ◽  
Thermal Effects ◽  
Iron Ore ◽  
Combustion Process ◽  
Pilot Scale ◽  
Mixing Properties ◽  
Process Gas ◽  
Mixing Rates ◽  
Computational Fluid Dynamics Cfd

AbstractThe flow field and coal combustion process in a pilot-scale iron ore pelletizing kiln is simulated using a computational fluid dynamics (CFD) model. The objective of the work is to investigate how the thermal effects from the flame affect the flow field. As expected, the combustion process with the resulting temperature rise and volume expansion leads to an increase of the velocity in the kiln. Apart from that, the overall flow field looks similar regardless of whether combustion is present or not. The flow field though affects the combustion process by controlling the mixing rates of fuel and air, governing the flame propagation. This shows the importance of correctly predicting the flow field in this type of kiln, with a large amount of process gas circulating, in order to optimize the combustion process. The results also justify the use of down-scaled, geometrically similar, water models to investigate kiln aerodynamics in general and mixing properties in particular. Even if the heat release from the flame is neglected, valuable conclusions regarding the flow field can still be drawn.

scholarly journals Dependence of the Flue Gas Flow on the Setting of the Separation Baffle in the Flue Gas Tract

2021 ◽  
Vol 11 (7) ◽  
pp. 2961
Author(s):  
Nikola Čajová Kantová ◽  
Alexander Čaja ◽  
Marek Patsch ◽  
Michal Holubčík ◽  
Peter Ďurčanský
Keyword(s):  
Particulate Matter ◽  
Flue Gas ◽  
Gas Flow ◽  
Negative Impact ◽  
Combustion Process ◽  
Cfd Simulations ◽  
Piv Measurements ◽  
Computational Fluid Dynamics Cfd ◽  
Particle Imaging ◽  
Combustion Of Solid Fuels

With the combustion of solid fuels, emissions such as particulate matter are also formed, which have a negative impact on human health. Reducing their amount in the air can be achieved by optimizing the combustion process as well as the flue gas flow. This article aims to optimize the flue gas tract using separation baffles. This design can make it possible to capture particulate matter by using three baffles and prevent it from escaping into the air in the flue gas. The geometric parameters of the first baffle were changed twice more. The dependence of the flue gas flow on the baffles was first observed by computational fluid dynamics (CFD) simulations and subsequently verified by the particle imaging velocimetry (PIV) method. Based on the CFD results, the most effective is setting 1 with the same boundary conditions as those during experimental PIV measurements. Setting 2 can capture 1.8% less particles and setting 3 can capture 0.6% less particles than setting 1. Based on the stoichiometric calculations, it would be possible to capture up to 62.3% of the particles in setting 1. The velocities comparison obtained from CFD and PIV confirmed the supposed character of the turbulent flow with vortexes appearing in the flue gas tract, despite some inaccuracies.

An anti-crystallization design of selective catalytic reduction nozzle holder

2021 ◽  
pp. 095440702097084
Author(s):  
Dewen Liu ◽  
Kai Lu ◽  
Shusen Liu ◽  
Yan Wu ◽  
Shuzhan Bai
Keyword(s):  
Selective Catalytic Reduction ◽  
Catalytic Reduction ◽  
Injection System ◽  
Test Results ◽  
Verification Methods ◽  
Crystallization Risk ◽  
Protective Cover ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Method ◽  
Good Agreement

From the aspect of reducing the risk of crystallization on nozzle surface, a new design of nozzle protective cover was to solve the problem in selective catalytic reduction (SCR) urea injection system. The simulation calculation and experimental verification methods were used to compare different schemes. The results show that reducing the height of nozzle holder can reduce the vortex currents near nozzle surface and effectively reduce the risk of crystallization on the nozzle surface. It is proposed to install a protective cover in the nozzle holder under the scheme of reducing the height of nozzle holder, which can further eliminate the vortex. Simulation and test results demonstrate good agreement under the rated running condition. The scheme of adding a protective cover in the nozzle holder shows the least crystallization risk by computational fluid dynamics (CFD) method. The crystallization cycle test shows that, after the height of nozzle holder is reduced, the risk of crystallization on the nozzle surface is reduced correspondingly. The addition of a protective cover in the nozzle holder solves the problem of crystallization on the nozzle surface, which provides a new method for anti-crystallization design.

Reduction of Fuel Utilization Through Oxygen-Enriched Combustion in a Reheat Furnace Pusher-Type

2021 ◽  
Author(s):  
Francisco J. Martinez Zambrano ◽  
Armin K. Silaen ◽  
Kelly Tian ◽  
Joe Maiolo ◽  
Chenn Zhou
Keyword(s):  
Heat Flux ◽  
Fuel Injection ◽  
Combustion Process ◽  
Oxygen Enrichment ◽  
Rolling Temperature ◽  
Heating Zone ◽  
Reheat Furnace ◽  
Steel Slabs ◽  
The Impact ◽  
Reheating Process

Abstract Steelmaking is an energy-intensive process. Thus, energy efficiency is highly important. Several stages of steelmaking involve combustion processes. One of the most energy-consuming processes in steelmaking is the slab reheating process in a reheat furnace (RF). The energy released by fuel combustion is used to heat steel slabs to their proper hot-rolling temperature. The steel slabs move through the reheat furnace passing the three stages of heating called: Preheating Zone (PZ), Heating Zone (HZ), and Soaking Zone (SZ) to finally leave the discharge door at a rolling temperature of 2375 °F. One way to improve a reheat furnace’s fuel consumption is by implementing oxygen-enriched combustion. This study investigates the implementation of oxygen-enriched combustion in a pusher-type reheat furnace. The increment of oxygen in the combustion process allows for increasing the furnace gas temperature. Consequently, the oxygen enrichment approach allows for the reduction of fuel injection. The principal goal of this investigation is to model the combustion-based on oxygen-enrichment and develop parametric studies of fuel injection rates. The different simulations aim to match the slab heat flux profile of the industrial reheat furnace pusher-type. Computational fluid dynamics are used to generate the slab heat flux distribution. To reach more uniform slab heating, oxygen and fuel ports were alternated. Also, injection angles were modified to optimize slab heating and avoid the impact of hot spots. Thermocouple readings of the industrial reheat furnace are compared to simulation results. The results determined that 40–45% fuel reduction can be achieved.

scholarly journals Validasi Model Turbulensi pada Simulasi Numerik Menggunakan Software Fluent dengan Sayap Onera M6

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Sulistiya Sulistiya ◽  
Alief Sadlie Kasman
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Computational Result ◽  
Computational Simulation ◽  
Turbulence Models ◽  
Wind Tunnels ◽  
Direct Testing ◽  
The World ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Method

AbstractNumerical simulation using Computational Fluid Dynamics (CFD) method is one way of predicting airflow characteristics on the model. This method is widely used because it is relatively inexpensive and faster in getting desired results compared with performing direct testing. The correctness of a computational simulation output is highly dependent on the input and how it was processed. In this paper, simulation is done on Onera M6 Wing, to investigate the effect of a turbulence model’s application on the accuracy of the computational result. The choice of Onera M6 Wing as a simulation’s model is due to its extensive database of testing results from various wind tunnels in the world. Among Turbulence models used are Spalart-Allmaras, K-Epsilon, K-Omega, and SST.Keywords: CFD, fluent, Model, Turbulence, Onera M6, Spalart-Allmaras, K-Epsilon, K-Omega, SST.AbstraksSimulasi numerik dengan menggunakan metode Computational Fluid Dynamics (CFD) merupakan salah satu cara untuk memprediksi karakteristik suatu aliran udara yang terjadi pada model. Metode ini banyak digunakan karena sifatnya yang relatif murah dan cepat untuk mendapatkan hasil dibandingkan dengan melakukan pengujian langsung. Benar tidak hasil sebuah simulasi komputasi sangat tergantung pada inputan yang diberikan serta cara memproses data inputan tersebut. Pada tulisan ini dilakukan simulasi dengan menggunakan sayap onera M6 dengan tujuan untuk mengetahui pengaruh penggunaan model turbulensi terhadap keakuratan hasil komputasi. Pilihan sayap onera M6 sebagai model simulasi dikarenakan model tersebut sudah memiliki database hasil pengujian yang cukup lengkap dan sudah divalidasi dari berbagai terowongan angin di dunia. Model turbulensi yang digunakan diantaranya Spalart-Allmaras, K-Epsilon, K-Omega dan SST.Kata Kunci : CFD, fluent, Model, Turbulensi, Onera M6, Spalart-Allmaras, K-Epsilon, K-Omega, SST.

scholarly journals Flutter speed prediction by using deep learning

2021 ◽  
Vol 13 (11) ◽  
pp. 168781402110622
Author(s):  
Yi-Ren Wang ◽  
Yi-Jyun Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Technology ◽  
Memory Space ◽  
Flutter Speed ◽  
Aeroelastic Flutter ◽  
Speed Prediction ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Method

Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.

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