Design of flash ironmaking reactors with computational fluid dynamics modeling

A novel ironmaking technology is being developed in a research group at the University of Utah headed by Professor H.Y. Sohn where large-scale bench reactor (LSBR) experimental investigations are conducted before launching the pilot-scale phase and eventual commercialization. The goal of the technology is to produce iron directly from magnetite concentrate, with lower carbon dioxide emissions and reduced energy consumption compared to the blast furnace technology. Product iron will be the feed for the steelmaking process, ultimately replacing the most widely used technology, the blast furnace. Computational fluid dynamics (CFD) modeling was performed to simulate the large-scale bench reactor and the industrial reactor for the novel technology using a program named ANSYS Fluent®17.1. CFD was performed to describe and analyze the performance of two different types of reactors for the flash ironmaking technology. User defined functions (UDF) was constructed to incorporate the chemical reduction of iron concentrate with reducing gas, previously determined in a drop-tube reactor at our research lab, into the continuity, momentum, and species transport equations. The first reactor is the large-scale bench reactor where a model was created using CFD to simulate the actual bench reactor operated in the University of Utah. The results of the simulation were validated by the experimental runs of the LSBR in the operating temperature range of 1150 - 1600 °C. The simulation results of the reduction degrees of concentrate particles and the composition of the off-gas from the reactor experimental iv data were found to have a satisfactory agreement. The developed model of the large-scale bench reactor was used to study the effect of the oxygen/natural gas ratio, the total input gas flow rate, and the concentrate powder feeding locations on the reduction degrees of the concentrate particles. The optimum operating conditions of the large-scale bench reactor required to achieve high reduction degrees of iron oxide concentrate were suggested. The second reactor is an industrial reactor to produce 0.3-1 million tons/yr of metallic iron using the flash ironmaking technology. Possible industrial reactors have been designed where higher than 90% metallization has been achieved.

DESIGN OF FLASH IRONMAKING REACTORS WITH COMPUTATIONAL FLUID DYNAMICS MODELING by Amr Refay Abdelghany Mohamed A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Metallurgical Engineering The University of Utah August 2018 Copyright © Amr Refay Abdelghany Mohamed 2018 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL The dissertation of Amr Refay Abdelghany Mohamed has been approved by the following supervisory committee members: , Chair Hong Yong Sohn 05/01/2018 Date Approved Michael L. Free , Member Zhigang Zak Fang , Member Philip J Smith , Member 05/01/2018 Date Approved 05/01/2018 Date Approved 05/01/2018 Date Approved York Reed Smith and by , Member Date Approved Manoranjan Misra the Department/College/School of 05/01/2018 , Chair/Dean of Metallurgical Engineering and by David B. Kieda, Dean of The Graduate School. ABSTRACT A novel ironmaking technology is being developed in a research group at the University of Utah headed by Professor H.Y. Sohn where large-scale bench reactor (LSBR) experimental investigations are conducted before launching the pilot-scale phase and eventual commercialization. The goal of the technology is to produce iron directly from magnetite concentrate, with lower carbon dioxide emissions and reduced energy consumption compared to the blast furnace technology. Product iron will be the feed for the steelmaking process, ultimately replacing the most widely used technology, the blast furnace. Computational fluid dynamics (CFD) modeling was performed to simulate the large-scale bench reactor and the industrial reactor for the novel technology using a program named ANSYS Fluent®17.1. CFD was performed to describe and analyze the performance of two different types of reactors for the flash ironmaking technology. User defined functions (UDF) was constructed to incorporate the chemical reduction of iron concentrate with reducing gas, previously determined in a drop-tube reactor at our research lab, into the continuity, momentum, and species transport equations. The first reactor is the large-scale bench reactor where a model was created using CFD to simulate the actual bench reactor operated in the University of Utah. The results of the simulation were validated by the experimental runs of the LSBR in the operating temperature range of 1150 – 1600 °C. The simulation results of the reduction degrees of concentrate particles and the composition of the off-gas from the reactor experimental data were found to have a satisfactory agreement. The developed model of the large-scale bench reactor was used to study the effect of the oxygen/natural gas ratio, the total input gas flow rate, and the concentrate powder feeding locations on the reduction degrees of the concentrate particles. The optimum operating conditions of the large-scale bench reactor required to achieve high reduction degrees of iron oxide concentrate were suggested. The second reactor is an industrial reactor to produce 0.3-1 million tons/yr of metallic iron using the flash ironmaking technology. Possible industrial reactors have been designed where higher than 90% metallization has been achieved. iv To Ayat, Afaf, and Islam TABLE OF CONTENTS ABSTRACT ....................................................................................................................... iii LIST OF TABLES ............................................................................................................. ix LIST OF FIGURES ............................................................................................................ x LIST OF ABBREVIATIONS ........................................................................................... xii ACKNOWLEDGMENTS ............................................................................................... xiii Chapters 1. INTRODUCTION .......................................................................................................... 1 1.1. Ironmaking .......................................................................................................... 1 1.1.1. Blast furnace (BF) ................................................................................ 1 1.1.2. Direct reduced iron (DRI) .................................................................... 2 1.1.3. Flash ironmaking technology ............................................................... 2 1.2. Statement of the Problem and Aim of the Research ........................................... 4 1.3. Research Questions ............................................................................................. 5 1.3.1. Development of a CFD model of the flash ironmaking process .......... 5 1.3.2. Validation of the CFD model with the results of LSBR runs .............. 6 1.3.3. Calculation by the CFD model of process parameters that are difficult to measure and/or obtain by simple methods .................................................... 6 1.3.4. The optimum flow rates of natural gas and oxygen to achieve 95% metallization of the product from the LSBR..................................................... 6 1.3.5. Design and dimensions of an industrial flash ironmaking reactor with a capacity of 0.3 –1.0 million tons/yr of the metallic iron ................................ 8 1.3.6. Operating conditions of the industrial flash ironmaking reactor that achieve > 90% metallization based on the CFD model .................................... 8 1.4. References ........................................................................................................... 8 2. NOVEL FLASH IRONMAKING TECHNOLOGY BASED ON IRON CONCENTRATE AND PARTIAL COMBUSTION OF NATURAL GAS: PART I: LARGE BENCH REACTOR SYSTEM AND CFD SIMULATION .............................. 13 2.1. Abstract ............................................................................................................. 13 2.2. Introduction ....................................................................................................... 14 2.3. Materials and Methods ...................................................................................... 16 2.4. Model Equations ............................................................................................... 17 2.4.1. Model assumptions ............................................................................... 17 2.4.2. Gas-phase governing equations ............................................................ 18 2.4.3. Incorporation of natural gas combustion .............................................. 18 2.4.4. Reduction kinetics of iron concentrate particles ................................... 19 2.4.5. Numerical details .................................................................................. 21 2.5. Results and Discussions .................................................................................... 22 2.6. Concluding Remarks ......................................................................................... 24 2.7. Acknowledgments............................................................................................. 24 2.8. Nomenclature .................................................................................................... 25 2.9. Appendix ........................................................................................................... 27 2.10. References ....................................................................................................... 31 3. NOVEL FLASH IRONMAKING TECHNOLOGY BASED ON IRON CONCENTRATE AND PARTIAL COMBUSTION OF NATURAL GAS: PART II: OPTIMIZATION OF OPERATING CONDITIONS WITH CFD .................................. 42 3.1. Abstract ............................................................................................................. 42 3.2. Introduction ....................................................................................................... 43 3.3. Materials and Methods ...................................................................................... 44 3.4. Model Equations ............................................................................................... 45 3.5. Results and Discussions .................................................................................... 45 3.5.1. Effect of the inlet oxygen to natural gas ratio with the same total gas flow rate .......................................................................................................... 46 3.5.2. Effect of total gas flow rate with constant oxygen/natural gas ratio..... 46 3.5.3. Comparing the simulation model with equilibrium .............................. 47 3.5.4. Reducing gases contours ....................................................................... 47 3.5.5. Particle flow pattern and profile of metallization degree...................... 47 3.5.6. Heat loss to the surroundings ................................................................ 49 3.6. Conclusions ....................................................................................................... 50 3.7. Acknowledgments............................................................................................. 50 3.8. Nomenclature .................................................................................................... 51 3.9. Appendices ........................................................................................................ 52 3.9.1. Appendix I ............................................................................................ 52 3.9.2. Appendix II ........................................................................................... 53 3.10. References ....................................................................................................... 54 4. DESIGN OF INDUSTRIAL IRONMAKING REACTORS USING COMPUTATIONAL FLUID DYNAMICS MODELING ............................................... 64 4.1. Introduction ....................................................................................................... 64 4.2. Model ................................................................................................................ 64 4.3. Dimensions and Operating Conditions ............................................................. 65 4.4. Meshing............................................................................................................. 65 4.5. Results and Discussion ..................................................................................... 66 4.5.1. Mass weighted average gas composition and product metallization at the outlet.......................................................................................................... 66 4.5.2. Contours of gas velocity, temperature, and product gas content ....... 66 vii 4.5.3. The concentrate particles .................................................................... 68 4.5.4. Heat loss ............................................................................................. 69 4.6. Conclusions ....................................................................................................... 69 viii LIST OF TABLES Tables 2.1. The experimental conditions for the LSBR ............................................................... 34 2.2. Gas phase governing equations .................................................................................. 35 2.3. Arrhenius constant, temperature exponent, and activation energy of CH4-O2 partial combustion reactions ........................................................................................................ 35 2.4. Experimental vs. calculated reduction degrees .......................................................... 35 3.1. Thicknesses and thermal properties of refractory, insulation, and carbon steel layers in the LSBR....................................................................................................................... 55 3.2. The simulation runs of the LSBR .............................................................................. 55 3.3. CFD simulated results ................................................................................................ 56 3.4. Comparison of gas mole% at outlet of simulation vs. HSC ...................................... 57 3.5. Heat generated from the combustion of natural gas, heat loss through the walls of the reactor, and percentage heat loss. ..................................................................................... 58 4.1. Operating conditions of the two industrial reactors ................................................... 71 4.2. The dimensions of the industrial reactors .................................................................. 71 4.3. Mass weighted average gas composition, EDF, and metallization% of concentrate product for the two reactors. ............................................................................................. 72 4.4. Heat generated from the combustion of natural gas, heat loss from the walls, and percentage heat loss. ......................................................................................................... 72 LIST OF FIGURES Figures 1.1. The LSBR vessel........................................................................................................ 12 1.2. Schematic representation of the LSBR ...................................................................... 12 2.1. The LSBR vessel........................................................................................................ 36 2.2. Schematic representation of the LSBR ...................................................................... 36 2.3. The burner used in the LSBR..................................................................................... 37 2.4. The meshing of the LSBR.......................................................................................... 37 2.5. Temperature distributions in K: (a) Run 1 (b) Run 2 (c) Run 3 (d) Run 4 (e) Run 5 (f) Run 6 ............................................................................................................................ 38 2.6. Particle stream distribution with time in s: (a) Run 1 (b) Run 2 (c) Run 3 (d) Run 4 (e) Run 5 (f) Run 6 ............................................................................................................ 39 2.7. Comparison of the measured off-gas contents of H2, CO, CO2, and H2O with the computed values: (a) Run 1 (b) Run 2 (c) Run 3 (d) Run 4 (e) Run 5 (f) Run 6 ............. 40 2.8. The SEM picture of product from Run 4 ................................................................... 41 3.1. Schematic representation of the LSBR (a) Top plan (b) Side plane ...................... 59 3.2. Schematic of the particular burner used in the LSBR................................................ 59 3.3. The LSBR temperature distribution in K of the 726 SLPM total gas flow rate for: (a) Run 1 (b) Run 4 (c) Run 7 ................................................................................................ 60 3.4. The temperature distribution in the LSBR of the oxygen/methane ratio of 0.8 for: (a) Run 4 (b) Run 5 (c) Run 6 ........................................................................................... 60 3.5. H2 contours in mole fraction: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 .................... 61 3.6. CO contours in mole fraction: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 ................... 61 3.7. Particle stream distribution with time in s for: (a) Run 1 (b) Run 4 .......................... 62 3.8. Number density distribution in particles per cm3 volume: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 ........................................................................................................... 62 3.9. Profile of mass averaged iron mass fraction along the LSBR: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 ........................................................................................................... 63 4.1. Schematic representation of the industrial reactor ..................................................... 73 4.2. Schematic representation of the burner...................................................................... 73 4.3. Meshing of the top section for a quarter of Reactor 2 ............................................... 73 4.4. Directional arrows representation of gas velocity magnitude in m/s where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 ...................... 74 4.5. The contours of temperature in K in the gas phase where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 .......................................... 75 4.6. The contours of H2 gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 .......................................... 75 4.7. The contours of CO gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 .......................................... 76 4.8. The contours of H2O gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 .......................................... 76 4.9. The contours of CO2 gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 .......................................... 77 4.10. The number density in particles/cm3 where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 ........................................................................ 77 4.11. The mass weighted average mass fraction of metallic iron in particles: (a) Reactor 1, (b) Reactor 2............................................................................................... 78 4.12. The mass weighted average particle temperature in K: (a) Reactor 1, (b) Reactor 2 ..................................................................................................................... 78 xi LIST OF ABBREVIATIONS BF Blast furnace CFD Computational fluid dynamics DRI Direct reduced iron DTR Drop tube reactor EAF Electric arc furnace EDF Excess driving force GHG Greenhouse gas LSBR Large-scale bench reactor OBC Oxygen blown convertor RD Reduction degree UDF User defined function UFR Utah flash reactor ACKNOWLEDGMENTS I want to give my thanks and gratitude to my supervisor “Prof. Sohn”; his generous help and broad knowledge was a blessing for me as one of his students and he will be forever a part of my life as the model of a distinguished researcher, professor, and tennis player. I want to thank my friend and college “De-Qiu Fan” for his help and fruitful discussions through the last three years as we shared the same office. I want to give my thanks to my wife “Ayat Ahmed” for being my soul-mate and my shelter in the difficult days; all the words in the universe won’t fulfill the feelings I have for her. I want to thank my mother “Afaf Soliman” for her love and support through my whole life and even before my birth; although she passed away two years ago, she is still living with me and my prayers for her will continue until I die. I want to thank my friend “Islam Ghanem” who passed away in my first week in Utah. Islam has lived his life as a helper to everyone and he was my first motivation for all my involvement on campus including three blood drives and 100+ blood donations to save lives of people, as Islam always believed “…. whoever saves one soul - it is as if he had saved mankind entirely” Quran 5:32. I want to thank my dissertation committee for their time and for their help through my research work. Finally, I want to thank all my colleagues in Prof. Sohn’s research group, especially Mohamed Elzohiery, for their help. CHAPTER 1 INTRODUCTION The following is the introduction chapter for the dissertation work. 1.1. Ironmaking Iron is the fourth most common element in the earth’s crust. Magnetite (Fe3O4) and hematite (Fe2O3) are the most common minerals used for the production of metallic iron. The crude iron produced from these minerals is further processed to produce steel. The processes used for ironmaking can be summarized as follows. 1.1.1. Blast furnace (BF) The blast furnace process is the major producer of iron in the whole world. The charge to BF consists of pellets and sinters of iron ore (0.5 - 1.5 inch. diameter), coke, and limestone.1 Coke is typically made by heating coal to remove volatile organics and tar. Coke contains mainly carbon and ash components and is also much stronger than coal. The product pig iron from the BF is fed with/without recycled steel scrap to the oxygen blown convertor (OBC), which processes 74.2% of the world production in crude iron.2 2 1.1.2. Direct reduced iron (DRI) The DRI is produced by several processes in which the iron ore is converted to metallic iron in the solid state. The reduction is carried out below 1200 °C to avoid the absorption of carbon by the metallic iron product. The product is fed with/without recycled steel scrap to the electric arc furnace (EAF), which processed 67% of steel in the US in 20163 and 25.2% of the world production in crude iron in 2015.2 1.1.3. Flash ironmaking technology The novel flash ironmaking technology conceived by Sohn4 is based on the suspension of iron oxide concentrate by reducing gas mixtures in the temperature range 1150 - 1600°C. The novel technology bypasses the energy-intensive processes of sintering and pelletizing in the blast furnace technology. The coke-making step that causes environmental problems and carbon dioxide emissions, the primary source of the greenhouse gas (GHG) emission ultimately leading to global warming, will no longer be needed when the novel flash ironmaking technology substitutes the blast furnace technology. Sohn and coworkers5-17 have performed many experimental and bench-scale investigations, kinetics determination, flowsheet development, and economic analyses to prove the feasibility and economic net worth of the novel technology. Sohn and OlivasMartinez14 illustrated by material and energy balance calculations that the novel flash ironmaking technology will reduce energy consumption by about 6 GJ per ton of product metallic iron compared with the blast furnace technology if pure hydrogen was used. They also showed that the use of natural gas in the novel flash ironmaking technology 3 would save about 3 GJ per ton of product metallic iron compared with the blast furnace technology energy requirement. Choi et al.18 performed kinetic feasibility tests of the proposed process in a drop tube reactor (DTR). They showed that when using pure hydrogen as a reducing agent in various amounts of excess supply, a reduction degree of 99% could be obtained within 1-7 seconds in the temperature range of 1200 - 1500 °C. Elzohiery et al.19- 20 determined the kinetics of hydrogen reduction of solid magnetite concentrate particles in the temperature range of 1150 – 1600 °C and achieved a reduction degree of more than 90% in 3-8 seconds in a DTR. Fan et al.21 used the Computational Fluid Dynamics (CFD) approach for developing improved rate equations by taking into consideration the variation of particles temperature and velocity in the DTR where magnetite concentrate has been reduced by hydrogen gas only. Other sets of experiments were performed by Elzohiery et al.22 and Wang and Sohn23 in which a mixture of hydrogen and carbon monoxide was used to reduce the magnetite concentrate in different temperature ranges. Those experiments were performed to prove the ability of the syngas mixture (H2+CO) to achieve reduction degrees higher than 90% within the few seconds of residence time typically available in a flash reactor. Elzohiery et al.24 obtained an expression to represent the reduction of magnetite in a hydrogen and carbon monoxide mixture as a function of the individual magnetite reduction by hydrogen and carbon monoxide. Fan et al.25- 26 developed a three-dimensional CFD model using ANSYS Fluent® to study the reduction of magnetite concentrate particles in a lab-scale reactor called the Utah flash reactor (UFR) in the presence of hydrogen and water vapor mixture. The mixture was produced from the partial combustion of hydrogen with oxygen, which 4 makes the reactor reach 1175±25 °C with the help of electrical heating elements. The UFR achieved a reduction degree as high as 91% under the operating conditions tested. They compared temperature profiles and reduction degrees obtained from CFD with the experimental results27 and obtained a satisfactory agreement. Perez-Fontes and Sohn28 proposed a three-dimensional CFD model for an industrial pilot reactor to produce 25,000-50,000 tons of iron per year using Sohn’s novel technology. 1.2. Statement of the Problem and Aim of the Research In this dissertation work, two different types of reactors for the novel flash ironmaking technology were simulated. The first reactor was the large-scale bench reactor (LSBR) shown in Figure 1.1 and schematically shown in Figure 1.2 with dimensions of 0.8 m inner diameter, 2.1 m length, and lined with three layers of refractory, insulation, and carbon steel. The LSBR was installed on the campus of the University of Utah for the bench-scale study of the novel flash ironmaking technology. The large bench-scale study aimed for the research and testing of the effect of system variables like feeding rates, oxygen to natural gas ratio, and temperature. The solid feed to the LSBR was magnetite concentrate with a mass flow rate of 1-7 kg/h. The burner of the LSBR was designed to make a swirling flow in the reactor. Swirl was introduced to increase the residence time of the magnetite concentrate particles, thus increasing the reduction degree. The unique design of the burner shortened the flame zone and ensured a larger uniform temperature zone in the reactor. 5 The second simulated reactor was an industrial reactor, which will be used for the industrial production of metallic iron by the novel flash ironmaking technology. The industrial reactor was designed to produce 0.3 - 1.0 million tons of metallic iron per year with > 90% metallization to be fed to the electric arc furnace to produce steel. The industrial production is the ultimate goal of the novel flash ironmaking technology. 1.3. Research Questions The objectives of this research were as follows. 1.3.1. Development of a CFD model of the flash ironmaking process In our dissertation research, we used the commercial software ANSYS Fluent® as our CFD platform. For each of the transport phenomena (momentum transfer, heat transfer, and mass transfer), there were several options for equations to be used that vary greatly according to the particular use, accuracy, and computational time requirements. User defined functions (UDF) were constructed to incorporate the chemical reduction of iron concentrate, previously determined in a drop-tube reactor at our research lab, with reducing gas into the continuity, momentum, and species transport equations. In addition, we used the appropriate equations and boundary conditions for the model that simulated our reactor satisfactorily, yet with reasonably low computational time requirements. 6 1.3.2. Validation of the CFD model with the results of LSBR runs ANSYS Fluent® has been used in commercial applications and design of burners and reactors. The model used in the simulation program was validated by comparison of the results with experimental data in terms of the outlet gas composition and the product reduction degree. Satisfactory validation achieved for the model when compared to the experimental runs thus decreased the number of costly experiments using the LSBR. Reliable simulations cut the time required for the experimental investigation. 1.3.3. Calculation by the CFD model of process parameters that are difficult to measure and/or obtain by simple methods We calculated the following using the CFD model: 1. The percentage of heat loss through the walls with respect to the heat generated by the combustion of fuel. 2. The particle flow pattern and distribution inside the reactor. 3. The gas flow pattern inside the reactor. 4. Contours of the gas temperature and H2, CO. H2O, and CO2 concentrations. 1.3.4. The optimum flow rates of natural gas and oxygen to achieve 95% metallization of the product from the LSBR The flow rates of natural gas and oxygen in the LSBR affected the operating temperature of the reactor. The oxygen/natural gas ratio changed the mole percentages of 7 hydrogen, carbon monoxide, carbon dioxide, and water vapor, and thus changed the value of the EDF defined below. The EDF at certain operating temperatures was the driving force for the reduction of magnetite concentrate to produce metallic iron. The kinetics of magnetite concentrate reduction was developed after extensive experimental investigations in the DTR in Sohn’s research lab.29 Metallization degree was the percentage of the iron present as metallic iron after reduction to the total iron, as shown in Eq. (1.3). A product of 90 - 95% metallization from the novel technology will be the feed for the electric arc furnace (EAF). ( 𝑛𝐻 2 𝑜𝑓𝑓𝑔𝑎𝑠 𝑛𝐻 𝑜 2 𝑜𝑓𝑓𝑔𝑎𝑠 𝐸𝑥𝑐𝑒𝑠𝑠 𝐷𝑟𝑖𝑣𝑖𝑛𝑔 𝐹𝑜𝑟𝑐𝑒 = ( )−( 𝑛𝐻 2 𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 𝑛𝐻 𝑜 2 𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 𝑛𝐻 2 𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 𝑛𝐻 𝑜 2 𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 1 ∗𝑛 𝑛𝐻 𝐾 𝐻2 𝑜𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 2 𝑜𝑓𝑓𝑔𝑎𝑠 ( )−( ) 𝑛𝐻 𝑜 𝑛𝐻 𝑜 2 𝑜𝑓𝑓𝑔𝑎𝑠 2 𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 1 ∗𝑛 𝐾 𝐻2 𝑜𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 ( 𝑛𝐻 𝑜 2 𝑜𝑓𝑓𝑔𝑎𝑠𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚 𝑛𝐻2 = 𝐾 ∗ (𝑛 = ) 𝑜𝑓𝑓𝑔𝑎𝑠 𝐻2 𝑜𝑜𝑓𝑓𝑔𝑎𝑠 ) ) )−1 (1.1) where K is the equilibrium constant of the reduction of wustite by hydrogen as follows: Fe0.947O + H2 = 0.947Fe + H2O (Overall equation for reduction of magnetite by hydrogen) 𝐾= 𝑝𝐻2 𝑂𝑒𝑞𝑢. 𝑝𝐻2 (1.2) 𝑒𝑞𝑢. where 𝑝𝐻2 𝑂𝑒𝑞𝑢. and 𝑝𝐻2 𝑒𝑞𝑢. represent the partial pressures of H2O and H2 at equilibrium, respectively. Metallization % = 𝑊𝐹𝑒𝑀 𝑊𝐹𝑒𝑇 *100 (1.3) where 𝑊𝐹𝑒𝑇 and 𝑊𝐹𝑒𝑀 represents the total weight of iron and metallic iron, respectively, 8 in the reduced sample. 1.3.5. Design and dimensions of an industrial flash ironmaking reactor with a capacity of 0.3 –1.0 million tons/yr of the metallic iron The industrial flash ironmaking reactor is the most important part of the potential industrial facility and needs to have a production rate comparable to other industrial technologies in the steel industry. We determined that producing 0.3 - 1.0 million tons/yr of the metallic iron was compatible with industrial-scale production. Sizes for the lower and higher ends were selected for the industrial reactor. Sizing mainly involves the dimensions of the inner reactor region where the reacting particle-gas flow takes place. 1.3.6. Operating conditions of the industrial flash ironmaking reactor that achieve > 90% metallization based on the CFD model Operating conditions needed to be determined for producing > 90% metallization of magnetite concentrate in the industrial reactor. Operating conditions are the flow rates and the feeding angles of natural gas and oxygen through the burner. The CFD model of the industrial reactor was used to calculate the items listed in Section 3.3 above. 1.4. References 1. Hosford, W.F. Iron and steel 2012, Cambridge University Press, NY, USA. 2. World Steel Association, Steel Statistical Yearbook 2016 2016. 9 3. World Steel Association, World Steel in Figures 2017 2017. 4. Sohn, H.Y. Suspension Ironmaking Technology with Greatly Reduced Energy Requirement and CO2 Emissions. Steel Times International 2007. 31(3): p. 68-72. 5. Sohn, H.Y.; Choi, M.E. An AISI-Utah Project on Novel Green Ironmaking Technology with Greatly Reduced CO2 Emission and Energy Consumption. The 5th International Congress on the Science and Technology of Ironmaking (ICSTI’09) 2009: Shanghai, China.: p. 1283-1287. 6. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Process Simulation and Economic Feasibility Analysis for a Hydrogen-Based Novel Suspension Ironmaking Technology. Steel Research International 2011. 82(8): p. 951-963. 7. Wang, H.; Sohn, H.Y. Hydrogen Reduction Kinetics of Magnetite Concentrate Particles Relevant to a Novel Flash Ironmaking Process. Metallurgical and Materials Transactions B 2012. 44(1): p. 133-145. 8. Choi, M.E.; Sohn, H.Y. Development of Green Suspension Ironmaking Technology Based on Hydrogen Reduction of Iron Oxide Concentrate: Rate Measurements. Ironmaking & Steelmaking 2013. 37(2): p. 81-88. 9. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Flowsheet Development, Process Simulation and Economic Feasibility Analysis for Novel Suspension Ironmaking Technology Based on Natural Gas: Part 1 – Flowsheet and Simulation for Ironmaking with Reformerless Natural Gas. Ironmaking & Steelmaking 2013. 39(6): p. 398-408. 10. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Flowsheet Development, Process Simulation and Economic Feasibility Analysis for Novel Suspension Ironmaking Technology Based on Natural Gas: Part 2 – Flowsheet and Simulation for Ironmaking Combined with Steam Methane Reforming. Ironmaking & Steelmaking 2013. 40(1): p. 32-43. 11. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Flowsheet Development, Process Simulation and Economic Feasibility Analysis for Novel Suspension Ironmaking Technology Based on Natural Gas: Part 3 – Economic Feasibility Analysis. Ironmaking & Steelmaking 2013. 40(1): p. 44-49. 12. Yuan, Z.; Sohn, H.Y.; Olivas-Martinez, M. Re-oxidation Kinetics of FlashReduced Iron Particles in H2-H2O(g) Atmosphere Relevant to a Novel Flash Ironmaking Process. Metallurgical and Materials Transactions B 2013. 44(6): p. 1520-1530. 10 13. Sohn, H. Y. From Sulfide Flash Smelting to a Novel Flash Ironmaking Technology. Keynote paper in Celebrating the Megascale: EPD Symposium on Pyrometallurgy in Honor of David G. C. Robertson 2014, 143rd TMS Annual Meeting, San Diego, California, ed. by P. J. Mackey et al., TMS/Wiley, Hoboken, NJ, p. 69-76. 14. Sohn, H.Y.; Olivas-Martinez, M. Methods for Calculating Energy Requirements for Processes in Which a Reactant is also a Fuel: Need for Standardization. JOM 2014. 66(9): p. 1557-1564. 15. Sohn, H.Y.; Mohassab, Y. Development of a Novel Flash Ironmaking Technology with Greatly Reduced Energy Consumption and CO2 Emissions. Journal of Sustainable Metallurgy 2016. 2(3): p. 216-227. 16. Sohn, H.Y.; Mohassab, Y.; Elzohiery, M.; Fan, D.-Q.; Abdelghany, A. Status of the Development of Flash Ironmaking Technology. Applications of Process Engineering Principles in Materials Processing, Energy and Environmental Technologies 2017, 146th TMS Annual Meeting, San Diego, California, ed. by S. Wang et al., The Minerals, Metals & Materials Series, The Minerals, Metals & Materials Society, p. 15-23. 17. Sohn, H.Y.; Moo, E.C.; Zhang, Y.; Ramos, J.E. Suspension Reduction Technology for Ironmaking with Low CO2 Emission and Energy Requirement. Iron & steel technology 2009. 6(8): p. 158-165. 18. Choi, M.; Sohn, H.Y. Development of Green Suspension Ironmaking Technology Based on Hydrogen Reduction of Iron Oxide Concentrate: Rate Measurements. Ironmaking & Steelmaking 2010. 37(2): p. 81-88. 19. Elzohiery, M.; Sohn, H.Y.; Mohassab, Y. Kinetics of Hydrogen Reduction of Magnetite Concentrate Particles in Solid State Relevant to Flash Ironmaking. Steel Research International 2017. 88(2): p. 1-14. 20. Elzohiery, M.; Mohassab, Y.; Abdelghany, A.; Zhang, S.; Chen, F.; Sohn, H.Y. Reduction Kinetics of Magnetite Concentrate Particles with Hydrogen at 1150 – 1600 °C Relevant to a Novel Flash Ironmaking Process. EPD Congress 2016, 145th TMS Annual Meeting, Nashville, Tennessee, ed. by A. Allanore et al., TMS (The Minerals, Metals & Materials Society), p. 41-49. 21. Fan, D.; Mohassab, Y.; Elzohiery, M.; Sohn, H.Y. Analysis of the Hydrogen Reduction Rate of Magnetite Concentrate Particles in a Drop Tube Reactor through CFD Modeling. Metallurgical and Materials Transactions B 2016. 47(3): p. 1669-1680. 11 22. Elzohiery, M.; Mohassab, Y.; Pal, J.; Zhang, S.; Sohn, H.Y. Reduction Kinetics of Magnetite Concentrate Particles with H2 + CO at 1200 to 1600 °C Relevant to a Novel Flash Ironmaking Process. 7th International Symposium on HighTemperature Metallurgical Processing 2016, 145th TMS Annual Meeting, Nashville, Tennessee, ed. by: J.Y. Hwang et al., TMS (The Minerals, Metals & Materials Society), p. 35-41. 23. Wang, H.; Sohn, H.Y. Reduction of Magnetite Concentrate Particles by H2+CO at 1673 K. ISIJ International 2015. 55(3): p. 706-708. 24. Elzohiery, M.; Fan, D.Q.; Mohassab, Y.; Sohn, H.Y. Flash Ironmaking from Magnetite Concentrate in a Laboratory Reactor: Experimental and CFD Work. 8th International Symposium on High-Temperature Metallurgical Processing 2017, 146th TMS Annual Meeting, San Diego, California, ed. by JY. Hwang et al., the Minerals, Metals & Materials Series, p. 3-10. 25. Fan, D.-Q.; Sohn, H.Y.; Mohassab, Y.; Elzohiery, M. Computational Fluid Dynamics Simulation of the Hydrogen Reduction of Magnetite Concentrate in a Laboratory Flash Reactor. Metallurgical and Materials Transactions B 2016. 47(6): p. 3489-3500. 26. Fan, D.; Mohassab, Y.; Sohn, H.Y. Computational Fluid Dynamics Simulations of a Laboratory Flash Reactor Relevant to a Novel Ironmaking Process. CFD Modeling and Simulation in Materials Processing 2016, 145th TMS Annual Meeting, Nashville, Tennessee, ed. by: L. Nastac et al., TMS (The Minerals, Metals & Materials Society), p. 11-18. 27. Mohassab, Y.; Elzohiery, M.; Sohn, H.Y. Flash Reduction of Magnetite and Hematite Concentrates with Hydrogen in a Lab-Scale Reactor for a Novel Ironmaking Process. 7th International Symposium on High-Temperature Metallurgical Processing 2016, 145th TMS Annual Meeting, Nashville, Tennessee, ed. by: J.Y. Hwang et al., TMS (The Minerals, Metals & Materials Society), p. 3-10. 28. Perez-Fontes, S.E.; Sohn, H. Y.; Olivas-Martinez, M. A Computational Fluid Dynamic Model for a Novel Flash Ironmaking Process. in Celebrating the Megascale: EPD Symposium on Pyrometallurgy in Honor of David G. C. Robertson 2014, 143rd TMS Annual Meeting, San Diego, California, ed. by P. J. Mackey et al., TMS/Wiley, p. 385-392. 29. Fan, D.-Q.; Sohn, H.Y.; Elzohiery, M. unpublished work 2018. 12 Figure 1.1. The LSBR vessel Figure 1.2. Schematic representation of the LSBR CHAPTER 2 NOVEL FLASH IRONMAKING TECHNOLOGY BASED ON IRON CONCENTRATE AND PARTIAL COMBUSTION OF NATURAL GAS: PART I: LARGE BENCH REACTOR SYSTEM AND CFD SIMULATION Amr Abdelghany, De-Qiu Fan, Mohamed Elzohiery, and H. Y. Sohn Department of Metallurgical Engineering, University of Utah 135 S 1460 E, RM 412, Salt Lake City, Utah, USA, 84112 Phone: [phone] Email: [e-mail] Keywords: Flash ironmaking, bench reactor, magnetite concentrate, CFD simulation, natural gas This is Chapter 2 of the dissertation and it is a manuscript for a journal article that we will submit for publishing. 2.1. Abstract A large-scale bench reactor (LSBR) has been operated at the University of Utah to develop a novel flash ironmaking process. The reactor vessel is a refractory-lined furnace 0.8 m in diameter and 2.1 m in length, with a capacity of processing 1-7 kg/h of magnetite concentrate. Natural gas is partially oxidized with oxygen in a uniquely 14 designed burner, producing a short flame that provides heat (1423 – 1873 K) and a reducing gas mixture (hydrogen and carbon monoxide). The reducing gas reacts with magnetite concentrate particles to produce metallic iron. A computational fluid dynamics (CFD) model was developed to simulate the LSBR runs, in which the kinetics of magnetite concentrate reduction, separately determined in a drop-tube reactor, was incorporated. The reduction degrees and composition of the off-gas obtained from the CFD model show reasonable agreements with experimental results. 2.2. Introduction A novel ironmaking technology is being developed at the University of Utah to produce iron directly from iron oxide concentrate using reducing gases, which are produced by the partial combustion of natural gas with oxygen. The novel technology challenges the widely used blast furnace technology by producing iron with lower energy consumption and carbon dioxide emissions. The latter is the primary source of the greenhouse gas (GHG) emissions leading to global warming. Lower energy consumption is achieved in the novel technology largely by by-passing the sintering and pelletizing steps of magnetite concentrate and coke-making. The novel flash ironmaking technology conceived by Sohn1 is based on the gaseous reduction of iron oxide concentrate in suspension in the temperature range 1423 ˗ 1873 K. Sohn and coworkers2-13 have performed many experimental and bench-scale investigations, kinetics determination, flowsheet development, and economic analyses to prove the technical and economic feasibilities of the novel technology. A set of experiments were performed by Elzohiery et al.14 where a mixture of hydrogen and 15 carbon monoxide was used to reduce the magnetite concentrate in different operating temperature ranges. Those experiments were performed to prove the ability of the syngas mixture (H2 + CO) to achieve reduction degrees higher than 90% within the few seconds of residence time available in a flash furnace. Elzohiery et al.15 obtained a rate expression to represent the reduction of magnetite in a hydrogen and carbon monoxide mixture as related to the individual reduction by hydrogen and carbon monoxide. A LSBR has been installed on the campus of the University of Utah for the bench-scale study of the novel flash ironmaking technology. The bench-scale study aims at the research and testing of the effect of system variables like feed flow rates, residence time, oxygen to natural gas ratio, and temperature. The solid feed to the LSBR is magnetite concentrate with an average mass flow rate of 1-7 kg/h. The burner of the LSBR was uniquely designed to make a swirling flow in the reactor. Swirl is introduced to increase the residence time of the magnetite concentrate particles, thus increasing the reduction degree. The unique design of the burner also shortens the flame length and ensures a larger uniform temperature zone in the reactor. A large number of safety precautions were included in the reactor system and its operation. The off-gas was fed into a flare stack where excess oxygen incinerates it to ensure the combustion of the harmful components. A gas analyzer was used to measure the composition of the off-gas stream. A CFD model was developed to simulate the LSBR runs, in which the kinetics of magnetite concentrate reduction was incorporated. The reduction degrees and composition of the off-gas obtained from the CFD model were compared with experimental results to validate the model. 16 2.3. Materials and Methods The LSBR vessel is a refractory-lined furnace of 0.8 m in diameter and 2.1 m in length, as shown in Figure 2.1. The reactor made of a carbon steel shell is lined with refractory and insulation layers to minimize the heat loss to the surroundings. The reactor was preheated by the complete combustion of natural gas (equivalent to 100.3% CH4 and 2% N2) with industrial oxygen for more than 20 h. A restriction of ramping temperature less than 100°C per hour was applied during preheating to avoid damaging the refractory. The wall temperature was measured during the feeding of the concentrate powder by Ktype thermocouples extending from through the reactor shell and the insulation/refractory layers to 2.54 cm (one inch) from the inner wall of the LSBR. The off-gas composition was analyzed using NOVA® Model 875A Steel Making Analyzer. The gas analyzer used an NDIR (infrared) detector for analyzing the CO, CO2, and CH4 contents and a thermal conductivity cell for analyzing the H2 content and finally an electrochemical sensor for analyzing the O2 content. The H2O gas content was calculated as the balance. After the reactor cooled down, the reduced sample was collected from the quench chamber using several magnets that were installed to capture the powder. The collected sample was kept in a sealed vial for analysis by ICP. In order to calculate the reduction degree from magnetite to iron, the total iron content of the reduced sample was determined using ICP-OES analysis. The ICP analysis method was well developed in our lab to ensure the accuracy of the analysis to be within 1%.16 Six typical operating conditions of the LSBR listed in Table 2.1 were simulated. The CFD model is a steady state model while the temperature of the experiment varied with time as shown in Table 2.1. A program was created to calculate the 17 appropriate inner wall temperature to use in the CFD model. The rate of reaction varies with the temperature according to the Arrhenius equation. The calculated temperature was equivalent to the temperature of the average rate of reaction calculated using the MATLAB program. Inner wall temperatures used in the simulation are shown in Table 2.1 as well. 2.4. Model Equations The LSBR vessel is shown schematically in Figure 2.2. The burner used in the LSBR is shown in Figure 2.3. The reactor vessel is made up of the layers of refractory, insulation, and carbon steel. The inclined angle of the oxygen inlet ports (total of ten ports) causes a swirl flow in the reactor, which increases the particles residence time. This particular design of the burner shortens the flame length and ensures a larger uniform temperature zone in the whole reactor. 2.4.1. Model assumptions The following assumptions were made in our simulation: 1- Steady state conditions: The gas phase responds to changes much more quickly compared to condensed phases such as walls, as the density of the gas is about three orders of magnitude lower. Thus, the accumulation term for the gas phase energy balance is much smaller than the accumulation term for condensed phases. Thus, the steady state condition is assumed for the gas-particle phase. 18 2- Inter-particle collisions between magnetite concentrate particles in the reactor are neglected as the volume of the solid particles occupying the reactor is of three orders of magnetite lower than the volume of the gas. The ANSYS Fluent 17.1 software was used for modeling and simulating the LSBR where the gas/solid phase interactions occur. The governing equations used in the system are listed in Table 2.2. 2.4.2. Gas-phase governing equations The Eulerian approach was used for simulating the gas phase (continuum). User defined functions (UDF) were constructed to incorporate the chemical reduction of iron concentrate with reducing gas into the continuity, momentum, and species transport equations. In the continuity equations, the source term represents volumetric momentum exchange rate between the gas and solid particles (discrete) phases due to the drag force exerted on the particle. The realizable k-ε model was chosen for simulating the turbulent swirl flow.17 Radiation was taken into account using the discrete ordinate (DO) model.18 The governing equations for the gas-phase are summarized in Table 2.2. 2.4.3. Incorporation of natural gas combustion The reaction kinetics for the partial combustion of natural gas (actual composition of 96 mol% CH4, 2 mol% C2H6, and 2 mol% nitrogen) was incorporated in the simulation. Natural gas was considered as 100.6 mol% CH4 (1 mol% of C2H6 equivalent to 2.6 mol% of CH4 in heat production and 2 mol% of CH4 in hydrogen and carbon monoxide production, both for generating a representative hot gas mixture from the partial combustion; thus considering these two factors, 1 mol% of C2H6 was treated as 19 being equivalent to 2.3 mol% of CH4) and 2 mol% nitrogen to avoid the complexity of including ethane. The kinetics of natural gas oxidation used in this work consisted of four chemical reactions involving six species,19 as shown in Table 2.3. The eddy dissipation concept (EDC) approach was used to take the turbulence-chemistry interaction into consideration.20 The rates of reactions, according to Jones and Lindstedt,19 are given by: 𝑟1 = 𝑘𝑓,1 𝐶𝐶𝐻4 0.5 𝐶𝑂1.25 2 (2.1) 𝑟2 = 𝑘𝑓,2 𝐶𝐶𝐻4 𝐶𝐻2𝑂 (2.2) 𝑟3 = 𝑘𝑓,3 𝐶𝐻2 0.25 𝐶𝑂1.5 2 (2.3) 𝑟4 = 𝑘𝑓,4 𝐶𝐻2𝑂 𝐶𝐶𝑂 − 𝑘𝑏,4 𝐶𝐶𝑂2 𝐶𝐻2 (2.4) The forward reaction rate constant for each reaction is given by 𝑘𝑓,𝑖 = 𝐴𝑇𝛽 𝑒𝑥𝑝 (− 𝐸𝑎,𝑖 𝑅𝑇 ) (2.5) The backward reaction rate constant was linked to the forward reactions by equilibrium relationships in the fourth reaction: 𝑘𝑏,4 = 𝐾𝐶,4 = ( 𝑘𝑓,4 (2.6) 𝐾𝑐,4 𝐶𝐶𝑂2 𝐶𝐻2 𝐶𝐻2 𝑂 𝐶𝐶𝑂 ) (2.7) 𝑒𝑞 2.4.4. Reduction kinetics of iron concentrate particles The particle phase was treated as a discrete phase. The force balance that equates the particle inertia with the forces (mainly gravitational force and drag force) acting on the particle is expressed in the Lagrangian frame of reference as ⃗𝑝 𝑑𝑢 𝑑𝑡 = 𝐹𝐷 (𝑢 ⃗ −𝑢 ⃗ 𝑝) + 𝐹𝐷 = 3𝜇𝑐𝐷 𝑅𝑒 2 4𝜌𝑝 𝑑𝑝 𝑔⃗(𝜌𝑝 −𝜌) 𝜌𝑝 (2.8) (2.9) 20 0.44, 𝑐𝐷 = { 24 𝑅𝑒 𝑅𝑒 > 1000 (1 + 0.15𝑅𝑒0.678 ), 𝑅𝑒 ≤ 1000 (2.10) The particle thermal energy equation is expressed as 𝑚𝑝 𝑐𝑝 where the term 𝑑𝑇𝑝 𝑑𝑡 𝑑𝑚𝑜 𝑑𝑡 = ℎ𝐴𝑝 (𝑇 − 𝑇𝑝 ) − 𝑑𝑚𝑜 𝑑𝑡 𝐻𝑟𝑒𝑎𝑐 + 𝐴𝑝 𝜀𝑝 𝜎(𝑇 4 − 𝑇𝑝4 ) (2.11) is related to particle chemical reaction rate. The rate equations for reduction used in this work based on those developed by Fan et al.21 where both H2 and CO gases contributed to the reduction of magnetite concentrate particles. Thus, the global nucleation and growth rate expressions for magnetite reduction by H2 [Fe3O4 + 4H2 = 3Fe + 4H2O] and by CO [Fe3O4 + 4CO = 3Fe + 4CO2] were used in the kinetics. The mass balance of the particle is described by the following equation:22 𝑑𝑚𝑂 𝑑𝑡 𝑑𝑋 𝑑𝑡 = [1 + (−0.004𝑇 + 7.004) ∙ = 𝑚𝑂° ∙ 𝑝𝑐𝑜 𝑝𝑐𝑜 +𝑝𝐻2 ]∙ 𝑑𝑋 𝑑𝑡 𝑑𝑋 𝑑𝑡 | (2.12) + 𝐻2 𝑑𝑋 | ; T in K and t in s 𝑑𝑡 𝐶𝑂 (2.13) where 𝑚𝑂 is the mass of iron-bonded oxygen in the particle at time t, 𝑚𝑂° is the initial mass of oxygen in the particle, and 𝑑𝑋 𝑑𝑡 is the rate of fractional reduction. A user defined function (UDF) code was created to add the reduction kinetics of iron concentrate. The UDF code, shown in the Appendix, was imported into the CFD simulation. The gasparticle heat transfer coefficient h was evaluated using the correlation of Ranz and Marshall.23 A value of 0.8 was chosen as the particle emissivity 𝜀𝑝 , as recommended by Hahn and Sohn.24-25 21 2.4.5. Numerical details The gas-phase governing equations were discretized and solved using the commercial CFD software package ANSYS FLUENT 17.1. Three-dimensional mesh was generated using ICEM-CFD ANSYS with a total number of 398,380 hexahedral cells. Mesh optimization was confirmed by doubling the number of cells without changing the computational results. Figure 2.4 shows the meshing of the LSBR. Total particle streams of 480 were released from the injection ports to establish a statistical representation of the spread of the particles due to turbulence. Turbulence effect on the particles dispersion was considered by using the stochastic tracking model. In the stochastic tracking approach, ANSYS Fluent predicts the turbulent dispersion of particles by integrating the trajectory equations for individual particles, using the instantaneous fluid velocity, 𝑢̅+u'(t), along with the particle path during the integration. The path in this manner is computed for a sufficient number of representative particles (termed the “number of tries”). A stochastic method (random walk model) is used to determine the instantaneous gas velocity. In the discrete random walk (DRW) model, the fluctuating velocity components are discrete piecewise constant functions of time. Their random value is kept constant over an interval of time given by the characteristic lifetime of eddies. The particle trajectories and velocities were determined by numerically integrating the equation of particle motion, Eq. (12). As the particle trajectory was computed, Eqs. (15), (16), and (17) were integrated to obtain the particle temperature and mass. The calculation was carried out by a steady state pressure-based solver and the convergence was achieved by having a residual less than 10-3 in the momentum equation, less than 10-6 in the energy balance equation, and less than 0.01% variation in the mass-weighted 22 average temperature at the exit of the LSBR for more than 500 consecutive iterations. A second-order upwind scheme was chosen for momentum, species transport, and energy equation discretization for the convection term. 2.5. Results and Discussions The simulated temperature distributions and particle streams under the six experimental conditions are shown in Figure 2.5 and Figure 2.6, respectively. Higher temperatures compared with other runs are noticed for Runs 5 and 6 in which the higher mole ratio of oxygen to natural gas was at 0.94 and 0.93, respectively. Lowest temperature is noticed for Run 2 where the mole ratio of oxygen to natural gas was the lowest at 0.64. The temperature distribution of the six runs show largely uniform temperature distributions and short flame lengths thanks to the particular design of the burner. The particle streams for the six runs show a swirl flow because of the particular design of the burner. The particle streams of Runs 5 and 6 occupy smaller volumes of the LSBR compared to the other runs, which can be explained by the smaller total inlet gas flow rates of 33.71 and 30.67m3 h-1, respectively. Comparisons of the measured off-gas contents of H2, CO, CO2, and H2O with the computed values for the six runs are shown in Figure 2.7. It can be seen that overall a good agreement is achieved. The simulation model was able to predict the contents of the reducing gases H2 and CO with accuracies greater than 93% compared to experimental data in most of the runs. The experimental reduction degrees and the gas composition of the runs versus the calculated values are listed in Table 2.4. 23 There is a good agreement for the reduction degree in the first three runs. The last three runs do not show a good agreement between simulation and experiment; the reason may be the assumption of no interactions between particles is not accurate at the Runs of 4, 5, and 6. The later three runs have higher oxygen to natural gas ratio and higher gas temperatures compared to other runs. Gas temperatures in those runs were above 1850 K, which is higher than the melting point of iron (1811 K). Particle at these high temperatures will tend to coalesce together. This effect has been previously pointed out to be significant in copper flash smelting.26-28 Particle coalescence slows down the reaction and cause a lower reduction degree in experiments compared to the simulation in those runs. Although the gas temperature in Run 1 is above 1850 K, the mass feeding rate of concentrate powder in this run was nearly half the values in Runs 4, 5, and 6. The low mass feeding rate in Run 1, in addition to the losses of concentrate in the reactor because of accretion on the inner wall, greatly decreased the probability of droplet to coalesce in Run 1. We could not check that suggested reason experimentally because of the complexity of the system and the lack of quantitative measurements to observe changes on the particles during the experiment. However, we took pictures of the product collected in Run 4 using scanning electron microscope (SEM) and Figure 2.8 shows that particles agglomerations occurred and the size of the particles in the picture is > 60𝜇m, which is much larger than the 32 𝜇m mass averaged size of the concentrate particles used in the simulation. We can also notice the wider range of reduction degrees in Runs 4, 5, and 6, which is another indication that agglomeration occurred and the sizes of the particles greatly varied and as a result, the reduction degree values greatly varied as well. 24 2.6. Concluding Remarks A LSBR for flash ironmaking has been simulated using the CFD method. The model proposed in this work described accurately the fluid flow and heat transfer within the LSBR. The model also predicted the reduction degrees with good agreement for those experiments where the gas temperature was below the melting/fusion point of iron (1811 K). The off-gas composition obtained by incorporating the user defined function for reduction kinetics of H2 and CO gases with iron concentrate, previously determined in a drop-tube reactor at our research lab, agreed with the off-gas composition readings from the experiments. The model can be used in the design and optimization of much larger reactors when the gas operating temperature is within the range of 1423 – 1811 K. The model had lower degree of agreement for reduction degree in the experiments where the gas temperature was above iron melting, which caused the particles to coalescence. Particle/droplet coalescence made the assumption of no interactions between particles less accurate. This points out the need to incorporate the particle/droplet coalescence into the model to improve its performance at operating temperatures above 1811 K. The work we performed here is validation of the model using experimental results that can save time and money for anyone who will use the LSBR in the future and we provided all the model details to be used for future users as well. 2.7. Acknowledgments The support and resources from the Center for High Performance Computing at the University of Utah are gratefully acknowledged. The authors acknowledge the financial support from the U.S. Department of Energy under Award Number DE- 25 EE0005751 with cost share by the American Iron and Steel Institute (AISI) and the University of Utah. Disclaimer: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. 2.8. Nomenclature A 𝐴𝑝 𝑐𝐷 𝑐𝑖 𝑐𝑝 𝑑𝑝 Ea,i 𝐹𝐷 𝐹𝑝,𝑖 𝑔𝑖 ℎ ℎ𝑔 𝐻𝑟𝑒𝑎𝑐 Arrhenius constant. Units change with the order of the reaction Area of particle [m2] Drag coefficient [-/-] Concentration of species i at equilibrium [mol m-3] Specific heat capacity [J kg-1 K-1] Particle diameter [m] Activation energy of reaction i [cal mol-1] Drag force [kg m-1 s-2] External body force that arises from gas/particles interactions due to drag forces applied to the particles [J m-1] Acceleration of gravity [m s-2] Heat transfer coefficient [W m-2 K-1] Sensible enthalpy of the gas mixture [J m2 kg-1] Heat released by the surface reaction per unit mass [J kg-1] 26 𝐽𝑗 𝑘𝑏,4 𝐾𝐶,4 𝑘𝑒𝑓𝑓 𝑘𝑓,𝑖 𝑚𝑂 𝑚𝑂° 𝑚𝑝 𝑄𝑟 R 𝑅𝑒 𝑟𝑖 𝑆𝑔 𝑆𝑃 𝑆𝑝,𝑖 𝑇 𝑇𝑝 𝑢̅ u'(t) 𝑢𝑖 𝑢 ⃗𝑝 𝑋 𝑌𝑖 Greek letters 𝛿𝑖𝑗 𝜀𝑝 𝜌 𝜌𝑝 𝜎 𝛽 𝜇 Diffusion flux of species j due to gradients of concentration and temperature [kg m-2 s-1] Reverse (backward) reaction-rate constant of the fourth reaction [m3 mol-1 s-1] Equilibrium constant based on molar concentration [-/-] Effective thermal conductivity (sum of molecular thermal conductivity and turbulent thermal conductivity) [W m-1 K-1] Forward reaction-rate constant of reaction i. Units change with the order of the reaction. Mass of oxygen in the particle [kg] Initial mass of oxygen in the particle [kg] Particle mass [kg] Source term representing radiation per unit volume [W m-3] Universal gas constant =1.987 [cal mol-1 K-1] Reynolds number [-/-] Net rate of production of species i by the gas phase chemical reaction [mol m-3s-1] Net volumetric heat because of the heat generation from gaseous species chemical reactions and the heat consumption from the reduction of the solid particles [W m-3] Source term for the weight added to the fluid phase from the solid phase (magnetite concentrate particles) due to the gas-solid reaction [kg m-3] Net rate of mass addition/reduction of species i from the magnetite concentrate particles [W m-3] Gas temperature [K] Particle temperature [K] Time-averaged velocity [m s-1] Velocity fluctuation [m s-1] Gas velocity [m s-1] Particle velocity [m s-1] Fractional conversion [-/-] Mass fraction of gas component i [-/-] Kronecker delta [-/-] Particle emissivity [-/-] Gas mixture density [kg m-3] Particle density [kg m-3] Stefan-Boltzmann constant 5.670367 × 10-8 [W m K-4] Temperature exponent in the rate equation [-/-] Molecular viscosity [pa s] 27 Terms in equations 𝐴𝑝 𝜀𝑝 𝜎(𝑇 4 − 𝑇𝑝4 ) 𝐹𝐷 (𝑢 ⃗ −𝑢 ⃗ 𝑝) ℎ𝐴𝑝 (𝑇 − 𝑇𝑝 ) ′ ′ ̅̅̅̅̅̅ 𝜌𝑢 𝑖 𝑢𝑗 Heat added to the particle due to radiation [W] Drag force per unit particle mass [m s-2] Heat added to the particle due to convection [W] Reynolds stress term, apparent stress owing to the fluctuating velocity field. Calculated according to the Boussinesq approach [kg m-1 s-2] Differential terms in equations 𝑑𝑇𝑝 𝑑𝑡 𝑑𝑚𝑂 𝑑𝑡 𝑑𝑚𝑜 𝐻 𝑑𝑡 𝑟𝑒𝑎𝑐 𝑑𝑢 ⃗𝑝 𝑑𝑡 𝑑𝑋 𝑑𝑡 𝑑𝑋 | 𝑑𝑡 𝑖 𝜕𝑝 𝜕𝑥𝑖 𝜕 (𝜌𝑢𝑖 ) Change in enthalpy of the particle with time [W] 𝜕 (𝜌𝑢𝑖 ℎ𝑔 ) 𝜕𝑥𝑖 𝜕 (𝜌𝑢𝑖 𝑢𝑗 ) 𝜕𝑥𝑖 𝜕 (𝜌𝑢𝑖 ℎ𝑔 ) 𝜕𝑥𝑖 Enthalpy of the gas mixture per unit volume [W m-3] 𝑚𝑝 𝑐𝑝 𝜕𝑥𝑖 Change in mass of the oxygen in the particle with time [kg s1 ] Heat released by the reaction with time [W] Change of particle velocity with time [m s-2] Change of fractional conversion with time [s-1] Change of fractional conversion with time due to gas component i [s-1] Change in Static pressure per unit volume [pa m-3] Mass flow rate per unit volume [kg m-5 s-1] Change in the mean momentum of fluid element per unit volume [kg m-4 s-2] Enthalpy of the gas mixture per unit volume [W m-3] 2.9. Appendix The user defined function for reduction kinetics of iron concentrate: #include "udf.h" DEFINE_PR_RATE(iron_pr_rate_tempo_rev,c,t,r,mw,pp,p,sf,dif_i,cat_i,rr) { real one_minus_conv; real k; real Keq; 28 real driv_force; real f_conv; real one_minus_conv_2; real k_2; real Keq_2; real driv_force_2; real f_conv_2; real p1; real p2; real p3; real p4; real p5; real p6; real p7; real p8; real p1_2; real p2_2; real p3_2; real p4_2; real p5_2; real p6_2; real p7_2; real p8_2; real rr_f; real A1; real E1; real A1_2; real E1_2; if (P_T(p) <= 1625.0) { p1 = 1.5993e-21; p2 = -1.0112e-17; p3 = 2.5378e-14; p4 = -3.1573e-11; p5 = 1.8986e-08; p6 = -3.374e-06; p7 = -0.00058815; p8 = 0.17418; p1_2 = 0; p2_2 = 0; p3_2 = 0; p4_2 = 0; p5_2 = 0; p6_2 = 0.0028; p7_2 = -1.3789; p8_2 = 294.5768; 29 rr_f = 1.3; A1 = 8.65e6; E1 = 193000.0; A1_2 = 2.09e13; E1_2 = 437000.0; } else if (P_T(p) > 1625.0 && P_T(p) <= 1873) { p1 = 0; p2 = 0; p3 = 0; p4 = 0; p5 = 0; p6 = 7.7521e-07; p7 = -0.0034345; p8 = 4.4065; p1_2 = 0; p2_2 = 0; p3_2 = 0; p4_2 = 0; p5_2 = 0; p6_2 = 0; p7_2 = 0.0249e+06; p8_2 = -6.165e+06; rr_f = 2.0; A1 = 2.065625e5; E1 = 150000.0; A1_2 = 34.6875; E1_2 = 62000.0; } else { p1 = 0; p2 = 0; p3 = 0; p4 = 0; p5 = 0; p6 = 8.8856e-08; p7 = -0.00060173; p8 = 1.5251; p1_2 = 0; p2_2 = 0; p3_2 = 0; p4_2 = 0; p5_2 = 0; p6_2 = 0.0014; 30 p7_2 = -0.9658; p8_2 = 281.9324; rr_f = 2.0; A1 = 2.065625e5; E1 = 150000.0; A1_2 = 34.6875; E1_2 = 62000.0; } Keq = p1*pow(P_T(p),7) + p2*pow(P_T(p),6) + p3*pow(P_T(p),5) + p4*pow(P_T(p),4) + p5*pow(P_T(p),3) + p6*pow(P_T(p),2)+ p7*P_T(p)+ p8; Keq_2 = p1_2*pow(P_T(p),7) + p2_2*pow(P_T(p),6) + p3_2*pow(P_T(p),5) + p4_2*pow(P_T(p),4) + p5_2*pow(P_T(p),3) + p6_2*pow(P_T(p),2)+ p7_2*P_T(p)+ p8_2; k = A1*exp(-E1/8.314/P_T(p)); k_2 = A1_2*exp(-E1_2/8.314/P_T(p)); one_minus_conv = MAX(0.0000001,sf[0]*P_MASS(p) / P_INIT_MASS(p)); driv_force = pp[4]/86500 - pp[5]/86500/Keq; driv_force_2 = pp[2]/86500 - pp[3]/86500/Keq_2; if (driv_force < 0.0) driv_force = 0; if (sf[0]<=0.001) driv_force = 0; *rr= rr_f*(-P_INIT_MASS(p)*k*driv_force*one_minus_conv)+(P_INIT_MASS(p)*k_2*driv_force_2*one_minus_conv*-pow(log(one_minus_conv),1)); C_UDMI(c,t,0) = *rr; C_UDMI(c,t,1) = one_minus_conv; C_UDMI(c,t,2) = k; C_UDMI(c,t,3) = k_2; C_UDMI(c,t,4) = pp[4]; C_UDMI(c,t,5) = pp[2]; C_UDMI(c,t,6) = f_conv; C_UDMI(c,t,7) = pp[5]; C_UDMI(c,t,8) = pp[3]; C_UDMI(c,t,9) = P_T(p); C_UDMI(c,t,10) = Keq; C_UDMI(c,t,11) = Keq_2; C_UDMI(c,t,12) = pp[4]/86500 - pp[5]/86500/Keq; 31 C_UDMI(c,t,13) = pp[2]/86500 - pp[3]/86500/Keq; C_UDMI(c,t,14) = sf[0]; C_UDMI(c,t,15) = P_MASS(p); C_UDMI(c,t,16) = P_INIT_MASS(p); C_UDMI(c,t,17) = P_DIAM(p); C_UDMI(c,t,18) = sf[1]; } 2.10. References 1. Sohn, H.Y. Suspension Ironmaking Technology with Greatly Reduced Energy Requirement and CO2 Emissions. Steel Times International 2007. 31 (3): p. 68-72. 2. Sohn, H.Y.; Choi, M.E. An AISI-Utah Project on Novel Green Ironmaking Technology with Greatly Reduced CO2 Emission and Energy Consumption. The 5th International Congress on the Science and Technology of Ironmaking (ICSTI’09) 2009: Shanghai, China: p. 1283-1287. 3. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Process Simulation and Economic Feasibility Analysis for a Hydrogen-Based Novel Suspension Ironmaking Technology. Steel Research International 2011. 82(8): p. 951-963. 4. Wang, H.; Sohn, H.Y. Hydrogen Reduction Kinetics of Magnetite Concentrate Particles Relevant to a Novel Ironmaking Process. Metallurgical and Materials Transactions B 2013. 44(1): p. 133-145. 5. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Flow Sheet Development, Process Simulation and Economic Feasibility Analysis for a Novel Suspension Ironmaking Technology Based on Natural Gas: Part I. Flow Sheet and Simulation for Ironmaking with Reformerless Natural Gas. Ironmaking & Steelmaking 2013. 39(6): p. 398-408. 6. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Flow Sheet Development, Process Simulation and Economic Feasibility Analysis for a Novel Suspension Ironmaking Technology Based on Natural Gas: Part II. Flow Sheets and Simulation for Ironmaking Combined with Steam-Methane Reforming. Ironmaking & Steelmaking 2013. 40(1): p. 32-43. 7. Pinegar, H.K.; Moats, M.S.; Sohn, H.Y. Flow Sheet Development, Process Simulation and Economic Feasibility Analysis for a Novel Suspension Ironmaking Technology Based on Natural Gas: Part III: Economic Feasibility Analysis. Ironmaking & Steelmaking 2013. 40(1): p. 44-49. 32 8. Yuan, Z.; Sohn, H.Y.; Olivas-Martinez, M. Re-Oxidation Kinetics of Flash Reduced Iron Particles in H2-H2O(g) Atmosphere Relevant to a Novel Flash Ironmaking Process. Metallurgical and Materials Transactions B 2013. 44(6): p. 1520-1530. 9. Sohn, H.Y. From Sulfide Flash Smelting to a Novel Flash Ironmaking Technology. EPD Symposium on Pyrometallurgy 2014, 143rd TMS Annual Meeting, San Diego, California, TMS/Wiley, Hoboken, NJ, p. 69-76. 10. Sohn, H.Y.; Olivas-Martinez, M. Methods for Calculating Energy Requirements for Processes in Which a Reactant is also a Fuel – Need for Standardization. JOM 2014. 66(9): p. 1557-1564. 11. Sohn, H.Y.; Mohassab, Y. Development of a Novel Flash Ironmaking Technology with Greatly Reduced Energy Consumption and CO2 Emissions. Journal of Sustainable Metallurgy 2016. 2(3): p. 216-227. 12. Sohn, H.Y.; Mohassab, Y.; Elzohiery, M.; Fan, D.-Q.; Abdelghany, A. Status of the Development of Flash Ironmaking Technology. Applications of Process Engineering Principles in Materials Processing, Energy and Environmental Technologies 2017, 146th TMS Annual Meeting, Springer, Cham, Switzerland, p. 15-23. 13. Sohn, H.Y.; Choi, M.E.; Zhang, Y.; Ramos, J.E. Suspension Reduction Technology for Ironmaking with Low CO2 Emission and Energy Requirement. Iron & steel technology 2009. 6(8): p. 158-165. 14. Elzohiery, M.; Mohassab, Y.; Pal, J.; Zhang, S.; Sohn, H.Y. Reduction Kinetics of Magnetite Concentrate Particles with H2 + CO at 1200 TO 1600 oC Relevant to a Novel Ironmaking Process. 7th International Symposium on High-Temperature Metallurgical Processing 2016, 145th TMS Annual Meeting, Springer, Cham, Switzerland, p. 35-41. 15. Elzohiery, M.; Fan, D.Q.; Mohassab, Y.; Sohn, H.Y. Flash Itonmaking from Magnetite Concentrate in a Laboratory Reactor: Experimental and CFD Work. 8th International Symposium on High-Temperature Metallurgical Processing 2017, 146th TMS Annual Meeting, Springer, Cham, Switzerland, pp. 3-10. 16. Mohassab, Y.; Elzohiery, M.; Chen, F.; Sohn, H. Y. Determination of Total Iron Content in Iron Ore and DRI: Titrimetric Method versus ICP-OES Analysis. EPD Congress 2016, ed. by A. Allanore et al., TMS (The Minerals, Metals & Materials Society), p. 125-133. 17. Shih, T.-H.; Liou, W.W.; Shabbir, A.; Yang, Z.; Zhu, J. A New k-ϵ Eddy Viscosity Model for High Reynolds Number Turbulent Flows. Computers & Fluids 1995, 24(3), p. 227-238. 33 18. Chui, E.H.; Raithby, G.D. Computation of Radiant Heat Transfer on a Nonorthogonal Mesh Using the Finite-Volume Method. Numerical Heat Transfer, Part B: Fundamentals 1993, 23(3), p. 269-288. 19. Jones, W.P.; Lindstedt, R.P. Global Reaction Schemes for Hydrocarbon Combustion. Combustion and Flame 1988. 73: p. 233-249. 20. Gran, I.R.; Magnussen, B.F. A Numerical Study of a Bluff-Body Stabilized Diffusion Flame. Part 2. Influence of Combustion Modeling and Finite-Rate Chemistry. Combustion Science and Technology 1996, 119(1-6), p. 191-217. 21. Fan, D.; Mohassab, Y.; Sohn, H. Y. CFD Simulations of a Laboratory Flash Reactor Relevant to a Novel Flash Ironmaking Process. AISTech 2016 Proceedings 2016, AIST, p. 551-558. 22. Fan, D.; Mohassab, Y.; Elzohiery, M.; Sohn, H.Y. Analysis of the Hydrogen Reduction Rate of Magnetite Concentrate Particles in a Drop Tube Reactor Through CFD Modeling. Metallurgical and Materials Transactions B 2016, 47(3), p. 16691680. 23. Ranz, W.E.; Marshall, W.R. Evaporation from drops: Part 2. Chemical Engineering Progress 1952, 48(4), p. 173–180. 24. Hahn, Y.B.; Sohn, H.Y. Mathematical Modeling of Sulfide Flash Smelting Process: Part II. Quantitative Analysis of Radiative Heat Transfer. Metallurgical and Materials Transactions B 1990, 21(6), p. 959-66. 25. Theodore, A.S.L.; Bergman, L.; Incropera, F. P.; DeWitt, D. P. Fundamentals of Heat and Mass Transfer, 7th Edition 2011, Wiley, NJ, USA. 26. Themelis, N.J.; Wu, L.; Jiao, Q. Some Aspects of Mathematical Modeling of Flash Smelting Phenomena. In: Flash Reaction Processes, Proceedings of Center for Pyrometallurgy Conference 1988. University of Utah, Published by the center for pyrometallurgy, Rolla, MO, p. 263–285. 27. Kimura, T.; Ojima, Y.; Mori, Y.; Ishii, Y. Reaction Mechanism in a Flash Smelting Reaction Shaft. In: The Reinhardt Schuhmann International Symposium on Innovative Technology and Reactor Design in Extraction Metallurgy 1986. TMS/AIME, Colorado Springs, Colorado, 9 November, p. 403–418. 28. Kemori, N.; Ojima, Y.; Kondo, Y. Variation of the Composition and Size of Copper Concentrate Particles in the Reaction Shaft. In: Flash Reaction Processes, Proceedings of Center for Pyrometallurgy Conference 1988. University of Utah, Published by the center for pyrometallurgy, Rolla, MO, p. 47–68. 34 Table 2.1. The experimental conditions for the LSBR Parameters Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 2.5 4.3 5.0 5.0 4.6 4.0 32 - 90 less than less than 32 - 90 less than less 90 90 90 than 90 45 32 32 45 32 32 25.16 30.56 20.36 24.80 17.36 15.86 300 300 300 300 300 300 19.85 19.67 14.27 21.53 16.35 14.81 300 300 300 300 300 300 45.01 50.23 34.63 46.33 33.71 30.67 0.79 0.64 0.70 0.87 0.94 0.93 Inner wall 1483- 1503- 1573- 1403- 1563- 1573- temperature,** K 1563 1603 1673 1473 1623 1623 1526 1548 1626 1440 1594 1599 Concentrate feeding rate, kg h-1 Particle size range, µm Mass average particle size, µm (used for simulation) Natural Gas flow rate,* m3 h-1 Natural Gas input temperature, K O2 flow rate,* m3 h-1 O2 input temperature, K Total inlet gas flow rate,* m3 h-1 O2 to Natural Gas mole ratio Inner wall temperature, K (used for simulation) *Flow rates are calculated at 298 K and 0.85 atm, the barometric pressure at Salt Lake City (1 atm = 101.32 kPa). ** Wall temperatures were measured during feeding of the concentrate in the experiment. 35 Table 2.2. Gas phase governing equations 𝜕 Continuity: Momentum: Energy: Species: 𝜕𝑥𝑖 𝜕 (𝜌𝑢𝑖 𝑢𝑗 ) = − 𝜕𝑥𝑖 𝜕 𝜕𝑥𝑖 (𝜌𝑢𝑖 ℎ𝑔 ) = 𝜕 𝜕𝑥𝑖 (1) (𝜌𝑢𝑖 ) = 𝑆𝑃 𝜕𝑝 𝜕𝑥𝑖 𝜕 𝜕𝑥𝑖 (𝜌𝑌𝑖 𝑢𝑖 ) = − + 𝜕 𝜕𝑥𝑖 (𝑘𝑒𝑓𝑓 𝜕𝐽 𝜕𝑥𝑖 [𝜇 ( 𝜕𝑇 𝜕𝑥𝑖 𝜕𝑢𝑖 𝜕𝑥𝑗 + 𝜕𝑢𝑗 𝜕𝑥𝑖 2 𝜕𝑢𝑙 3 𝜕𝑥𝑙 − 𝛿𝑖𝑗 )] + 𝜕 𝜕𝑥𝑗 ′ ′ ̅̅̅̅̅̅ (−𝜌𝑢 𝑖 𝑢𝑗 ) + 𝜌𝑔𝑖 + 𝐹𝑝,𝑖 (2) (3) ) + 𝑄𝑟 + 𝑆𝑔 (4) + 𝑟𝑖 + 𝑆𝑝,𝑖 Table 2.3. Arrhenius constant, temperature exponent, and activation energy of CH4-O2 partial combustion reactions 𝛽 Ea,i (cal mol-1) 0.0 30,000 3.00×10 m mol s 0.0 30,000 2.50×1019 m2.25 mol-0.75 s-1K -1.0 40,000 0.0 20,000 Reaction 1 A CH4+0.5O2=CO+2H2 2 CH4+ H2O=CO+3H2 3 H2+0.5O2=H2O 4 H2O+CO=CO2+H2 4.40×1014 m2.25 mol-0.75 s-1 11 12 3 3 -1 -1 -1 -1 2.75×10 m mol s Table 2.4. Experimental vs. calculated reduction degrees Run Experimental (pct) Simulation (pct) 1 94.0±4.0 99.8 2 77.5±2.5 84.5 3 94.5±4.5 99.6 4 65±7 99.8 5 72.5±12.5 99.5 6 48.5±5.5 85.0 36 Figure 2.1. The LSBR vessel Figure 2.2. Schematic representation of the LSBR 37 Figure 2.3. The burner used in the LSBR Figure 2.4. The meshing of the LSBR 38 Figure 2.5. Temperature distributions in K: (a) Run 1 (b) Run 2 (c) Run 3 (d) Run 4 (e) Run 5 (f) Run 6 39 Figure 2.6. Particle stream distribution with time in s: (a) Run 1 (b) Run 2 (c) Run 3 (d) Run 4 (e) Run 5 (f) Run 6 40 Figure 2.7. Comparison of the measured off-gas contents of H2, CO, CO2, and H2O with the computed values: (a) Run 1 (b) Run 2 (c) Run 3 (d) Run 4 (e) Run 5 (f) Run 6 41 Figure 2.8. The SEM picture of product from Run 4 CHAPTER 3 NOVEL FLASH IRONMAKING TECHNOLOGY BASED ON IRON CONCENTRATE AND PARTIAL COMBUSTION OF NATURAL GAS: PART II: OPTIMIZATION OF OPERATING CONDITIONS WITH CFD Amr Abdelghany, De-Qiu Fan, and H. Y. Sohn Department of Metallurgical Engineering, University of Utah 135 S 1460 E, RM 412, Salt Lake City, Utah, USA, 84112 Phone: [phone] Email: [e-mail] Keywords: CFD simulation, flash Ironmaking, partial oxidation, methane This is Chapter 3 of the dissertation and it is also a manuscript for a journal article that we will submit for publishing. 3.1. Abstract A large-scale bench reactor (LSBR) has been operated at the University of Utah to develop a novel flash ironmaking process. A computational fluid dynamics (CFD) model was developed to simulate the LSBR runs, in which the kinetics of magnetite concentrate reduction, separately determined in a drop-tube reactor, was incorporated. The CFD model was previously validated in Chapter 2 using experimental runs. In this 43 Chapter 3, we used the CFD model after adding more comprehensive boundary conditions to test the effect of operating conditions including O2/natural gas ratio and gas flow rates on the product gas composition, temperature, heat loss to the walls of the LSBR, and reduction degree of the magnetite concentrate. Finally, optimized operating conditions were given. 3.2. Introduction A novel ironmaking technology was developed at the University of Utah for direct production of iron from iron oxide concentrate using reducing gases generated from the partial combustion of natural gas with oxygen. The CFD approach has been actively used for the advancement of that novel technology starting from the kinetic determination step on the small experimental reactor followed by the lab-scale reactor and ending with the LSBR. Fan et al.1 used the CFD approach to develop improve the kinetics of the small experimental drop tube reactor (DTR) developed by Elzohiery et al.2-3 by taking into consideration the variation of particles temperature and velocity in the DTR. Fan et al.4-5 developed a three-dimensional CFD model using ANSYS Fluent® to study the reduction of magnetite concentrate particles in a lab-scale reactor called the Utah Flash reactor (UFR) in the presence of hydrogen and water vapor mixture. The mixture was produced from the combustion of hydrogen with oxygen, which makes the reactor reach 1175±25 °C with the help of electrical heating elements. The UFR achieved a reduction degree as high as 91% under the operating conditions tested. They compared temperature profiles 44 and reduction degrees obtained from CFD with the experimental results6 and obtained a satisfactory agreement. The final stage of the development of this technology at the University of Utah was the operation of the LSBR described in Chapter 2 where a CFD model was created and validated by experimental data. The following is a CFD study on the effects of oxygen/natural gas ratio and total inlet gas flow rates on the product gas composition, temperature, heat loss of the LSBR, and the reduction degree of the product. An optimized value of oxygen/natural gas ratio and total inlet gas flow rate are suggested. 3.3. Materials and Methods The LSBR reactor is a refractory-lined furnace of 0.8 m in diameter and 2.1 m in length, as shown schematically in Figure 3.1. The reactor made of a carbon steel shell is lined with refractory and insulation layers to minimize the heat loss to the surroundings. Thickness and thermal properties of the three layers are shown in Table 3.1. The reactor was fed natural gas (equivalent to 100.6% CH4 and 2% N2) with oxygen to provide heat through a particular burner shown schematically in Figure 3.2. It should be noted that the oxygen inlet ports (total of ten ports) have an inclined angle that is causing a swirl flow in the reactor, which increased the residence time of particles. This particular design of the burner shortened the flame length and ensured a larger uniform temperature zone in the whole reactor. Ratios of oxygen and natural gas were adjusted to provide the hydrogen and carbon monoxide reducing gases and minimum ratio to provide sufficient reducing gases was found to be 0.7. The changes in the oxygen/natural gas ratio and the total inlet gas flow rates were tested by CFD model in seven simulation runs as listed in Table 3.2. 45 3.4. Model Equations The model used was described in Chapter 2. More comprehensive boundary conditions were used in that model compared to the constant wall temperature used in Chapter 2. The CFD model of the LSBR was adjusted to include the effect of the layers of refractory, insulation, and carbon steel surrounding the fluid zone and the free convection on the outer wall of the reactor in addition to the internal heat transfer. Eqs. (3.1-3.3) are used to calculate the heat flux to the wall from the local fluid cell. 𝑞 = ℎ𝑓 (𝑇𝑤𝑖 − 𝑇𝑓 ) + 𝑞𝑟𝑎𝑑 (3.1) 𝑞 = ℎ𝑒𝑥𝑡 (𝑇𝑒𝑥𝑡 − 𝑇𝑤𝑜 ) (3.2) 𝑞 = 𝑘𝑡ℎ𝑤 (𝑇𝑤𝑜 −𝑇𝑤𝑖 ) ∆𝑥 (3.3) The external temperature Text was assumed 300 K and the external heat transfer coefficient on the outer wall of the LSBR hext was calculated using General Electric data book7 and a value of 6.4 [W/m2.K] was used for the side and bottom parts and a value of 6.0 [W/m2.K] was used for the top part of the LSBR reactor. 3.5. Results and Discussions The model used in the simulation program was validated by comparison of the results with experimental data with satisfactory agreement in Chapter 2. A number of simulation runs were performed on this more comprehensive model to study the effect of the following on the metallization degree of the magnetite concentrate in the LSBR reactor. 46 3.5.1. Effect of the inlet oxygen to natural gas ratio with the same total gas flow rate Natural gas is partially combusted by industrial oxygen to produce the reducing gases (H2 + CO) in the temperature range of 1150-1600 °C. The flow rates of natural gas and oxygen in the LSBR affect the operating temperature of the reactor and the mole percentages of hydrogen, carbon monoxide, carbon dioxide, and water vapor, thus affecting the chemical driving force of the gas. The temperature distribution for the 726 SLPM total gas flow rate with the oxygen/natural gas ratio of 0.7, 0.8, and 1.0 is shown in Figure 3.3. For total gas flow rates of 726, 1036, and 1810 SLPM, an oxygen/natural gas ratio of 0.7 resulted in a metallization degree of 86.4±8.4%. Run 7 with a ratio of oxygen/natural gas of 1.0 gave an operating temperature of the LSBR above the melting point of iron. This was found to cause problems in this model as particles/droplets will tend to coalescence together and the results of this model will not be accurate as we assumed no particles interactions. An oxygen/natural gas ratio of 0.8 achieved a high metallization degree of 97.7±2.2%. These effects have been summarized in Table 3.3. 3.5.2. Effect of total gas flow rate with constant oxygen/natural gas ratio The total gas flow rate at a constant oxygen/natural gas ratio affects the operating temperature and the particle residence time. Although the latter decreases at a higher gas flow rate, the resulting higher operating temperature affects the reduction degree more strongly. This explains the results shown for Runs 4-6 in Table 3.3. The temperature distribution of the oxygen/natural gas ratio of 0.8 for the 726, 1036, and 1810 SLPM total 47 gas flow rate is shown in Figure 3.4. 3.5.3. Comparing the simulation model with equilibrium The product gas compositions for H2, CO, H2O, and CO2 from simulation at the reactor exit were compared with the equilibrium gas composition using HSC program and high accuracy were achieved in all runs as shown in Table 3.4. It should be noted that the effect of reduction of magnetite concentrate was included in the calculation. 3.5.4. Reducing gases contours The reducing gases (H2 and CO) contours are shown in Figures 3.5 and 3.6, respectively. The homogenous distribution of gases resulted from the particular burner which provides as well a shorter flame length and swirl flow. 3.5.5. Particle flow pattern and profile of metallization degree Particle streams swirl in the LSBR because of the particular design of the burner as shown in Figure 3.7. The number density distribution was shown numerically in Figure 3.8 where the white areas represent no particles flowing. The particle history data were exported in (.fvp) format and a MATLAB code shown in Appendix I was used to read the data. Another MATLAB code shown in Appendix II was used to draw the profile of mass averaged iron mass fraction with Z direction. Figure 3.9 shows the mass averaged iron mass fraction profile where the horizontal axes of the plots represents the axial direction (Z direction) in the reactor starting from the inlet on the reactor top and ending with 2.1 meter at the outlet of the reactor. It should be noted that iron mass 48 fraction represents the fraction of metallic iron mass with respect to the whole particle mass including mass of iron oxide while the metallization fraction represents the fraction of iron in the metallized form with respect to the total mass of iron in the particle as shown in Eq. (4). The values of the iron mass fraction calculated at the exit of the reactor in Figure 3.10 were used to calculate the metallization degrees previously shown in Table 3.3. Metallization fraction = 𝑊𝐹𝑒𝑀 𝑊𝐹𝑒𝑇 (3.4) where 𝑊𝐹𝑒𝑇 and 𝑊𝐹𝑒𝑀 represents the total weight of iron and metallic iron, respectively, in the reduced sample. The larger total flow rate made the particles occupy more area in the LSBR as we can see from the number density distribution in Figure 3.8 as Run 4 made particles occupy larger area compared to Runs 5 and 6. The Runs 1 and 4 made particles occupy larger area compared to Run 5 and 6 because of the swirl effect and the larger flow rates. The more area is occupied by particles, the higher metallization degree achieved as the concentrate particles become more uniformly exposed to reducing gases. The profiles of mass averaged iron mass fraction along the LSBR shown in Figure 3.9 have irregular variations because of the eddies inside the reactor cause higher mass averaged particles to be present at areas closer to the top part of the reactor. Farther down in the Z-direction, fewer eddies will be present and we can see the mass averaged iron mass fraction increasing with fewer irregular variations. We can notice from Figure 3.9 that Run 4 with total gas flow rate of 1810 SLPM and 0.8 oxygen/natural gas ratio will have the highest iron mass fraction in the shortest length of the reactor. That means we can achieve the 99.9% metallization using operating conditions of Run 4 in a shorter reactor, almost two thirds of the current reactor as we achieved the 99.9% metallization 49 degree after 1.4 m of the 2.1 m length of the LSBR. 3.5.6. Heat loss to the surroundings Calculating the heat loss from the generated heat of the inlet gases to the walls of the reactor helps us to evaluate the percentage of energy loss in the reactor and gives us a better view on how energy efficient is our reactor with certain operating conditions. We calculated the amount of heat generated from the reaction of natural gas with oxygen and the heat lost to the surroundings of the LSBR through the walls. We calculated the percentage of heat loss in each of the 7 runs and summarized those numbers in Table 3.5. We can notice from the Table 3.3 that Runs 3, 6, and 7 have the highest percentage of heat loss because of the longer nominal residence time (10 s) in those runs. Also, the runs with the lowest nominal residence time have the lowest percentage of heat loss. Finally, Runs 1 and 4 have shown the lowest percentage of heat loss compared to all other runs due to lowest nominal residence time of gas in the LSBR. Using operating conditions of Runs 1 and 4 means that we get the lowest percentage of heat loss to the walls of the reactor with respect to the generated heat from gases reaction. Based on the results, we can easily recommend the operating conditions of Run 4 to be the optimum operating conditions because of the higher metallization degree and lowest percentage of heat loss through the walls. We can even achieve the metallization degree in Run 4 using a shorter reactor. 50 3.6. Conclusions A simulation study using the CFD approach has been made on the LSBR for flash ironmaking. The effects of the inlet oxygen to natural gas ratio with the same total gas flow rate, total gas flow rate with constant oxygen/natural gas ratio, and the location of the powder feeder inlets on the product gas composition, temperature, and reduction degree of the magnetite were studied. An oxygen/natural gas ratio of 0.8 achieved the highest reduction degree of 99.2±0.7% while keeping the LSBR operating temperature below the melting temperature of iron. An oxygen/natural gas ratio of 1.0 caused operating temperature above the melting posit of iron and high percentage of heat loss in the reactor. Run 4 with a total gas flow rate of 1810 SLPM (nominal residence time of 4 s) with an oxygen/natural ratio of 0.8 has the optimum operating conditions. Run 4 achieved 99.9% metallization degree after only two thirds of the reactor and had the lowest percentage of heat loss through the walls. Using the operating conditions of Run 4, we can decrease the volume of the LSBR by one third while achieving 99.9% metallization degree. 3.7. Acknowledgments The technical support and resources provided by the Center for High Performance Computing at the University of Utah are gratefully acknowledged. The authors acknowledge the financial support from the U.S. Department of Energy under Award Number DE-EE0005751 with cost share by the American Iron and Steel Institute (AISI) and the University of Utah. Disclaimer: This report was prepared as an account of work sponsored by an 51 agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. 3.8. Nomenclature hext hf kthw q qr Text Tf Twi Two wfeM wfeT External heat transfer coefficient [W m-2 K-1] Fluid heat transfer coefficient [W m-2 K-1] Thermal conductivity of the wall [W m-1 K-1] Heat flux to the wall from the local fluid cell [W m-2] Radiative heat flux [W m-2] The external temperature [K] The local fluid temperature [K] Local wall inner surface temperature [K] Local wall outer surface temperature [K] Total weight of metallic iron [kg] Total weight of iron [kg] Greek letters Δx The wall thickness [m] 52 3.9. Appendices 3.9.1. Appendix I The MATLAB code to read the history data of particles where the file name is FieldViewReader.m function strucD=FieldViewReader(filename) %filename = 'ParticleHistory1.fvp'; filepath = filename; fileOpen = fopen(filepath); readinfo_ = false; while ~readinfo_ l = fgetl(fileOpen); if strcmp(l,'Variable Names') l = fgetl(fileOpen); VarNum = str2num(l); break; end end strucD.VarNum = VarNum; for i=1:VarNum l = fgetl(fileOpen); strucD.VarNames(i).name = l; end l = fgetl(fileOpen); partc = 1; stepcounter = 1; while true l = fgetl(fileOpen); if l==-1 break; end; lnum = str2num(l); if length(lnum) ==1 partc = partc+1; stepcounter = 1; continue; end % position strucD.particle(partc).x(stepcounter)= lnum(1); strucD.particle(partc).y(stepcounter)= lnum(2); strucD.particle(partc).z(stepcounter)= lnum(3); % variables for i=4:(VarNum+3) strucD.particle(partc).var(i-3).data(stepcounter)=lnum(i); 53 end stepcounter = stepcounter + 1; end fclose(fileOpen); 3.9.2. Appendix II The MATLAB code to calculate mass averaged properties of particles where the file name is Zaverage.m function [zloc density partavg]=Zaverage(strucD,var,nz) % average over length; np = length(strucD.particle); minz = min(strucD.particle(1).z); maxz = max(strucD.particle(1).z); for i=2:np minz = min(minz,min(strucD.particle(i).z)); maxz = max(maxz,max(strucD.particle(i).z)); end dz= (maxz-minz)/(nz-1); counterz = 1; z=linspace(minz*0.9999,maxz,nz-1); for j=1:nz-1 partavg(counterz) = 0; density(counterz) = 0; for i=1:np list = find(strucD.particle(i).z > z(j) & strucD.particle(i).z <= (z(j)+dz)); density(counterz) = density(counterz)+length(list); if isempty(list) continue; end partavg(counterz) = partavg(counterz) + sum(strucD.particle(i).var(var).data(list)); end if density(counterz)==0 partavg(counterz) = []; density(counterz) = []; continue; end partavg(counterz) = partavg(counterz)/density(counterz); zloc(counterz) = z(j); counterz = counterz+1; end %% 54 if (var==28) zloc(1)=0; partavg(1)=0; end plot(zloc,partavg,'-','LineWidth',3); xlabel('Z (m)'); ylabel(strucD.VarNames(var).name); 3.10. References 1. Fan, D.; Mohassab, Y.; Elzohiery, M.; Sohn, H.Y. Analysis of the Hydrogen Reduction Rate of Magnetite Concentrate Particles in a Drop Tube Reactor Through CFD Modeling. Metallurgical and Materials Transactions B 2016, 47(3), p. 16691680. 2. Elzohiery, M.; Sohn, H.Y.; Mohassab, Y. Kinetics of Hydrogen Reduction of Magnetite Concentrate Particles in Solid State Relevant to Flash Ironmaking. Steel Research International 2017. 88(2): p. 1-14. 3. Elzohiery, M.; Mohassab, Y.; Abdelghany, A.; Zhang, S.; Chen, F.; Sohn, H.Y. Reduction Kinetics of Magnetite Concentrate Particles with Hydrogen at 1150–1600 °C Relevant to a Novel Flash Ironmaking Process. EPD Congress 2016, 145th TMS Annual Meeting, Wiley, NJ, USA, p. 41-49. 4. Fan, D.; Mohassab, Y.; Elzohiery, M.; Sohn, H.Y. Computational Fluid Dynamics Simulation of the Hydrogen Reduction of Magnetite Concentrate in a Laboratory Flash Reactor. Metallurgical and Materials Transactions B 2016, 47(6), p. 34893500. 5. Fan, D.; Mohassab, Y.; Sohn, H.Y. Computational Fluid Dynamics Simulations of a Laboratory Flash Reactor Relevant to a Novel Ironmaking Process. CFD Modeling and Simulation in Materials Processing 2016, 145th TMS Annual Meeting, Wiley, NJ, USA, p. 11-18. 6. Mohassab, Y.; Elzohiery, M.; Sohn, H.Y. Flash Reduction of Magnetite and Hematite Concentrates with Hydrogen in a Lab-Scale Reactor for a Novel Ironmaking Process. 7th International Symposium on High-Temperature Metallurgical Processing 2016, 145th TMS Annual Meeting, Springer, Cham, Switzerland, p. 3-10. 7. General Electric Company. Research and Development Center. Heat Transfer and Fluid Flow; Data book V.1 1970, Schenectady, NY, USA. 55 Table 3.1. Thicknesses, and thermal properties of refractory, insulation, and carbon steel layers in the LSBR Carbon Steel Property Refractory Layer Insulation Layer Top part thickness, mm 321.5 79.4 19.1 Side part thickness, mm 177.8 73 9.5 bottom part thickness, mm 193.5 73 9.5 Density, kg/m3 2891 1081 7850 Specific heat, J/g.K 0.0003*T + 0.3621 0.714 0.47 Thermal conductivity, 10-6*T2 - 0.0032*T + 3*10-8*T2 + 4*10-5*T + W.m.K 4.5396 0.1797 shell Table 3.2. The simulation runs of the LSBR Run# Natural gas in (SLPM*) Oxygen in (SLPM*) Oxygen /Natural gas mole ratio Conc. Feed Rate (kg/h) Nominal Resident Time (s) 1 1065 745 0.7 5.0 4.0 2 598 438 0.7 5.0 7.0 3 411 315 0.7 5.0 10.0 4 1005 805 0.8 5.0 4.0 5 575 460 0.8 5.0 7.0 6 403 322 0.8 5.0 10.0 7 352 305 1.0 5.0 10.0 *SLPM: Standard liter per minute. 52 56 Table 3.3. CFD simulated results Temp. (K) Oxygen /Natural gas mole ratio Total inlet gas flow rate in SLPM 1 0.7 2 Run# Metallization (%) Half Length of LSBR Outlet of LSBR 1810 1519 1484 94.8 0.7 1036 1491 1466 84.7 3 0.7 726 1462 1438 78 4 0.8 1810 1798 1777 99.9 5 0.8 1036 1665 1647 99.9 6 0.8 726 1569 1548 95.5 7 1.0 726 1893 1879 99.3 *SLPM: Standard liter per minute. 57 Table 3.4. Comparison of gas mole% at outlet of simulation vs. HSC Run# Sim. / HSC H2 CO H2O CO2 Sim. 54.03 30.40 12.33 2.78 HSC 54.05 30.57 12.61 2.77 Sim. 52.60 30.02 14.02 3.29 HSC 51.65 29.86 15.02 3.47 Sim. 50.45 29.35 16.22 3.97 HSC 49.15 29.09 17.51 4.25 Sim. 48.38 30.25 18.29 3.08 HSC 47.50 30.14 19.17 3.19 Sim. 48.10 29.73 18.56 3.60 HSC 47.22 29.61 19.45 3.73 Sim. 47.87 29.21 18.80 4.12 HSC 46.95 29.06 19.72 4.27 Sim. 37.92 28.11 28.74 5.22 HSC 35.69 30.98 27.65 5.68 1 2 3 4 5 6 7 58 Table 3.5. Heat generated from the combustion of natural gas, heat loss through the walls of the reactor, and percentage heat loss. Percentage heat Run# Heat generated (W) Heat loss (W) loss 1 -61,666.7 -22,078 35.8 2 -36,451.9 -20,248 55.5 3 -25,475.6 -19,399 76.1 4 -87,870.6 -29,183 33.2 5 -47,371.7 -24,960 52.7 6 -29,266.9 -22,267 76.1 7 -43,877.5 -31,137 71.0 59 Figure 3.1. Schematic representation of the LSBR (b) Side plane (a) Top plan Figure 3.2. Schematic of the particular burner used in the LSBR 60 Figure 3.3. The LSBR temperature distribution in K of the 726 SLPM total gas flow rate for: (a) Run 1 (b) Run 4 (c) Run 7 Figure 3.4. The temperature distribution in the LSBR of the oxygen/methane ratio of 0.8 for: (a) Run 4 (b) Run 5 (c) Run 6 61 Figure 3.5. H2 contours in mole fraction: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 Figure 3.6. CO contours in mole fraction: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 62 Figure 3.7. Particle stream distribution with time in s for: (a) Run 1 (b) Run 4 Figure 3.8. Number density distribution in particles per cm3 volume: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 63 Figure 3.9. Profile of mass averaged iron mass fraction along the LSBR: (a) Run 1 (b) Run 4 (c) Run 5 (d) Run 6 CHAPTER 4 DESIGN OF INDUSTRIAL IRONMAKING REACTORS USING COMPUTATIONAL FLUID DYNAMICS MODELING 4.1. Introduction The industrial production is the ultimate goal of any new technology. Our novel flash ironmaking technology has shown good potential for industrial production. The operating temperature of our novel technology is 1423 – 1873 K, which is close to that of the blast furnace. We determined that producing 0.3 - 1.0 million tons/yr of the metallic iron using the flash ironmaking technology was compatible with industrial-scale production as the modern blast furnaces can produce from 0.3 – 3.0 million tons/yr of the metallic iron. 4.2. Model The same model that has been previously described in Chapter 3 was used for designing two industrial reactors. One minor change from the referred model was that the standard k-ε model was used in the industrial reactor instead of the realizable k-ε that was used in the LSBR. The reason for that is we used constant angle feeding for the inlet gases instead of swirl flow in the industrial reactor as for large reactors, swirl does not last through the whole length of the reactor even if it was applied. 65 4.3. Dimensions and Operating Conditions Table 4.1 shows the operating conditions of the two industrial reactors. The smaller reactor produces a 0.3 million tons of iron per yr. while the larger reactor produces 1.0 million ton of iron per yr. The schematic representation of the industrial reactor and its burner are shown in Figures 4.1 and 4.2, respectively, while the dimensions for the two reactors are shown in Table 4.2. It should be noted that the radial location of the powder feeders’ center was half the radial dimension of the reactor. Also, the volumetric flow rates of oxygen in inlets one and two were equal. 4.4. Meshing The industrial reactors were designed to have a single burner in the center of the reactor with four feeding ports evenly distributed and have the same radial position equal to half the radius of the reactor. The symmetry of the reactor was used to decrease the computational cost by taking a quarter of the reactor as a representation for the whole reactor. This will decrease the time required for simulation as the number of mesh cells will decrease to be one quarter of the entire reactor. The mesh consisted of 264,000 hexahedral cells in the smaller reactor and 279,000 hexahedral cells in the larger reactor. The top section of the meshing for Reactor 2 is shown in Figure 4.3. 66 4.5. Results and Discussion 4.5.1. Mass weighted average gas composition and product metallization at the outlet We used the velocity of 100m/s for the inlet gases in Reactor 1 while for the second reactor, we increased the area of the burner to increase the use of generated heat from gas reactions. As a result, the inlet velocity of 37 m/s was used for the inlet gases in Reactor 2. The product from reactors at the exit point can tell us how efficient the design was. As shown in Table 4.3, the metallization % of the concentrate from the two reactors is nearly identical and both were above the 90%. The higher temperature of the gas mixture in the second reactor indicates that design did use the generated heat from natural gas combustion better than the first reactor. 4.5.2. Contours of gas velocity, temperature, and product gas content Visualization of the contours of different variables inside the reactor helps to evaluate the performance of the reactor. Although the main product of the reactors is the solid phase (particles), the gas phase controls how the particles will move inside the reactor, their temperature, reduction of particles, and their residence time. Figure 4.4 shows the contours of velocity magnitude in the gas phase, Figure 4.5 shows the temperature distribution, and the H2, CO, H2O, and CO2 gases content are shown in Figures 4.6 – 4.9, respectively. The velocity and temperature distributions in Reactor 1 in Figures 4.4(a) and 4.5(a) show a single flame where methane partial oxidation produces the reducing gases at higher temperature. The gas mixture will expand because of the increase in the 67 temperature and the total molar flow rate of gas products as hydrogen is the dominant gas in the product mixture, thus we can see how Figure 4.6(a) is relevant to Figure 4.4(a). Velocity, temperature, and hydrogen mole fraction distribution in Reactor 2 shown in Figures 4.4(b), 4.5(b), and 4.6(b), respectively, can be explained similar to Reactor 1. However, Figure 4.5(b) shows a split flame, which is different from the single flame of Reactor 1 in Figure 4.5(a). The split flame in Reactor 2 arose from the higher surface area of natural gas compared to Reactor 1. The higher flow rate of natural gas in Reactor 2 with lower inlet velocity compared to Reactor 1 required larger feeding area. The flame was generated from the combustion of methane with oxygen, which led to the split flame as oxygen ports were not close enough (the natural gas has large feeding area) to cause a single flame similar to the Reactor 1 where the natural gas feeding area was smaller. The CO and CO2 mole fractions in Figures 4.7 and 4.9, respectively, show uniform distribution in the last one third of the reactors. This differs from the H2O mole fraction in Figure 4.8, which shows a uniform distribution in the last one fourth of the reactors. These results can be explained from the reduction kinetics as H2 participated more in the reduction of concentrate powder compared to CO. The reduction of concentrate by H2 produced H2O, which increased through the reactor, and a noticeable change in H2O content can be noticed. The reduction of concentrate by CO produced CO2, which did not show a noticeable change as the participation of CO in reduction was lower compared to H2. 68 4.5.3. The concentrate particles The concentrate particles were reduced in the reactor by H2 and CO. Particles need to be distributed through the volume of the reactor to increase their reduction. The number density, which represents the number of particles per cm3 volume, is shown in Figure 4.10. We can notice a higher density number near the wall in Reactor 1, which is not favorable. Higher density numbers near the walls mean that particles will likely hit the wall, which should be avoided as particles will tend to stick on the wall and accumulate with time. Particles sticking will decrease the production rate. Reactor 2 shows good distribution of particles and low probability of particle sticking on the wall. Sticking was not considered at the inner walls of the industrial reactors and total reflection of particles hitting the wall was assumed in the model. The particle streams were exported to a the MATLAB and the mass weighted average of mass fraction of metallic iron in particles and particle temperature profiles were plotted in Figure 4.11 and 4.12, respectively, where the x axis of the plots represents the axial distance in the reactors starting zero at inlet on the top of the reactor and ending with 35 meter at the outlet of the reactor The irregular variations in the curves at Figures 4.11 and 4.12 arose from the generation of recirculating flows in the reactors as particles will stay longer on the average in those areas. The particles have temperature high enough to reduce in the presence of H2 and CO gases and, on the other hand, their temperature did not go higher than the 1811 K, which is the melting temperature of iron. We favored temperatures below 1811 K as we suggested in Chapter 2 that particle will coalesce at temperatures higher than 1181 K and the assumption of no interaction between particles will not be 69 valid. 4.5.4. Heat loss Calculating the heat loss from the generated heat of the inlet gases to the walls of the reactor helps us to evaluate the percentage of energy loss in the reactor and gives us a better view on how energy efficient is our industrial reactor. We calculated those amounts of heat and the percentage of heat loss in each of the two reactors and summarized those numbers in Table 4.4. The numbers showed that the design of the second reactor was better than the first reactor from the prospective of heat loss percentage. The reason for that was Reactor 2 has smaller surface area per volume compared to Reactor 1. 4.6. Conclusions Two industrial reactors with different production rates were designed. The metallization degrees of product from these reactors were sufficiently high for use in the subsequent steelmaking step. The symmetry of each reactor was used to decrease the computational time by simulating one quarter of the reactors and specifying the symmetry faces of that quarter. The burner in Reactor 2 was modified from the burner used in Reactor 1 by increasing the relative surface area of burner to decrease the inlet gas velocity and to generate a split in the flame. This modification in Reactor 2 caused higher outlet gas temperature, temperature distribution along the reactor, concentrate particle temperature, and slightly higher metallization degree of the product. Particle distribution showed that Reactor 2 had a better distribution with respect to low 70 probability in particle sticking. Reactor 2 also showed lower percentage of heat loss from the gas reactions generated when compared to Reactor 1 because of the lower surface area per volume in Reactor 2. 71 Table 4.1. Operating conditions of the two industrial reactors Parameter Reactor 1 Reactor 2 Scale of production Lower limit Upper limit 0.3 1.0 0.4146 1.3820 Natural gas feeding rate in m3/s 17.5 45.8 Oxygen feeding rate in m3/s 13.2 34.7 Target production of iron in million ton/yr. Feed of the magnetite concentrate in million ton/yr. Table 4.2. The dimensions of the industrial reactors Parameter Definition Reactor 1 Reactor 2 35.00 35.00 7.00 12.00 0.05 0.30 0.02 0.02 0.26 0.8 0.44 1.6 0.51 1.8 The height of the reactor H in m The inner diameter of D1 the reactor in m The diameter of the D2 powder feeder (4 feeders in total) in m The inner diameter of D3 the oxygen inlet 1 in m The outer diameter of D4 the oxygen inlet 1 in m The outer diameter of D5 the natural gas inlet in m The outer diameter of D6 the oxygen inlet 2 in m 72 Table 4.3. Mass weighted average gas composition, EDF, and metallization% of concentrate product for the two reactors. Reactor T (K) H2 CO CO2 H2O EDF Metallization% 1 1519 40.2 26.3 5.9 24.1 0.34 91.2 2 1578 39.9 26.6 5.7 24.8 0.29 91.4 Table 4.4. Heat generated from the combustion of natural gas, heat loss from the walls, and percentage heat loss. Reactor Heat generated (MW) Heat loss (MW) Percentage heat loss 1 -89.8 -1.8 2.0 2 -327.0 -3.8 1.2 73 Figure 4.1. Schematic representation of the industrial reactor Figure 4.2. Schematic representation of the burner Figure 4.3. Meshing of the top section for a quarter of Reactor 2 74 Figure 4.4. Directional arrows representation of gas velocity magnitude in m/s where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 75 Figure 4.5. The contours of temperature in K in the gas phase where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 Figure 4.6. The contours of H2 gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 76 Figure 4.7. The contours of CO gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 Figure 4.8. The contours of H2O gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 77 Figure 4.9. The contours of CO2 gas content in mole fraction where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 Figure 4.10. The number density in particles/cm3 where the right vertical line represents the axis of symmetry: (a) Reactor 1, (b) Reactor 2 78 Figure 4.11. The mass weighted average mass fraction of metallic iron in particles: (a) Reactor 1, (b) Reactor 2 Figure 4.12. The mass weighted average particle temperature in K: (a) Reactor 1, (b) Reactor 2