Advanced design optimization of combustion equipment for biomass combustion

Renewable Energy 145 (2020) 1597e1607

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Advanced design optimization of combustion equipment for biomass combustion Joseph D. Smith a, *, Vikram Sreedharan b, Mark Landon c, Zachary P. Smith b a

Department of Chemical and Biochemical Engineering, Missouri University of Science and Technology, Rolla, MO, 65401, United States Elevated Analytics, 3575 North 100 East, Suite 375, Provo, UT, 84604, United States c Optimal Solutions Software, 2825 West1700 North, Idaho Falls, ID, 83402, United States b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 February 2019 Received in revised form 24 June 2019 Accepted 15 July 2019 Available online 16 July 2019

Design: of engineered combustion equipment normally involves laborious “build and try” designs to identify the best possible configuration. The number of design iterations can be reduced with engineering experience of what might work. The expensive cut-and-try approach can be improved using computational aided engineering tools coupled with optimization techniques to find the optimal design. For example, the “best” air duct configuration with the lowest pressure loss and smallest fan size for an air-fed biomass gasifier may take several weeks using the standard computational fluid dynamics (CFD) “cut and try” approach. Alternatively, coupling an efficient design optimization algorithm with an existing CFD model can reduce the time to find the best design by more than 50% and can allow the engineer to examine more design options than possible using the “cut-and-try” approach. Combining an efficient optimization algorithm with an existing CFD model of a biomass gasifier to find the “optimal” design is the focus of this work. Shape optimization has been performed by combining the optimization tool Sculptor® with the commercial CFD code STARCCMþ. This work illustrates how the “linked” approach is used to examine design factors to optimize an entrained flow biomass gasifier to improve overall system performance in a methodical comprehensive fashion. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Biomass combustion Computational fluid dynamics Reduced design cycle time Advanced burner design Design optimization

1. Introduction System design optimization is an important part of meeting growing challenges related to environmental concerns related to NOx, CO and greenhouse gas emissions. Biomass combustion has received considerable attention to provide electric power in regions without traditional fossil energy resources. Hybrid combustion systems that combine coal with non-design fuels (i.e., biomass) have been used to reduce greenhouse gas emissions. However, large differences in fuel quality for non-design biomass fuels (i.e., pine, poplar, switch grass, etc.) result in concerns about increased levels of NOx and CO. Several biomass burner designs (see Fig. 1) have been developed to maintain combustion efficiency while reducing NOx and CO emissions for co-fired systems.

* Corresponding author. 1101 North State Street, 210c Bertelsmeyer Hall, Rolla, MO, 65401, United States. E-mail address: [email protected] (J.D. Smith). 1 Developed by Optimal Solutions Software (OSS) as described at http:// gosculptor.com/. https://doi.org/10.1016/j.renene.2019.07.074 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

The present work applies a novel approach using an advanced shape optimization tool called a tool referred to as Sculptor1 to investigate engineering design optimization of a biomass burner system. Previous work by several others have applied CFD to optimize combustion equipment including Smith et al. [1], Hoseman [2], Goel et al. [3]. and Hajitaheri [4]. In this work, Arbitrary Shape Deformation (ASD), as implemented in Sculptor, has been used to control and manipulate the reactor shape and modify the associated computational grids. Using ASD, a design engineer defines control points around the shape being optimized and moves them to morph the shape into a new design. This allows the engineer to mold the shape into any arbitrary geometry, which expands the engineer's ability to examine general shapes instead of being restricted to a single design. This approach also allows the design engineer to deform the computational mesh in a CFD model without “remeshing” the geometry for each new design considered. In addition, linking shape changes directly to governing physics (i.e. pressure drop, fuel/ oxidizer, flow uniformity, mass flow, drag, mechanical stress, etc.) allows the engineer to investigate the impact design changes have on system performance to find the best design not possible using

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Nomenclature EBU DOM-S4 Yl Charl ASD CAE Soot Al El B LLNL

Eddy breakup combustion model Discrete Ordinates Model e 4 Ordinates Low Temperature Devolatilization Stoichiometric Coefficient Char Fraction for Low Temperature Devolatilization Arbitrary Shape Deformation Computer Aided Engineering Soot particles Pre-exponential factor for low temperature devolatilization reaction (s1) Activation energy for low temperature devolatilization reaction (J/kmol) Temperature coefficient in devolatilization reactions Lawrence Livermore National Lab

WGS CFD Yh Charh BERL CAD HHV DOE Ah Eh GUI ISAT

Water-Gas Shift reaction Computational Fluid Dynamics High Temperature Devolatilization Stoichiometric Coefficient Char Fraction for High Temperature Devolatilization Burner Engineering Research Laboratory Computer Aided Design High Heating Value ((MJ/kg) Design of Experiments Pre-exponential factor for high temperature devolatilization reaction (s1) Activation energy for high temperature devolatilization reaction (J/kmol) Graphical User Interface In-Situ Adaptive Tabulation

Fig. 1. Typical Biomass/coal burners: a) Low-Emission Scroll-type Biomass/Coal burner (Coen Sales brochure), b) BWE Bio-dust burner equipped with a gas lance and/or an oil lance (BWE Sales brochure).

the standard “model-build-and-try” approach commonly used with CFD analysis. 2. Biomass combustion Coal has been a primary choice amongst fossil fuels, which are a primary source of copious amounts of energy. Owing to combustible content and a high calorific value (HHV: 25e30 MJ/kg) [5], coal fired power plants have previously provided a reliable energy supply to society and industry. With depleting coal and other fossil fuel reserves, it is imperative to switch to renewable energy sources that may continue to provide energy of a similar magnitude by combustion processes. Biomass is a renewable fuel with comparable calorific value to coal (HHV: 10e20 MJ/kg) [5] which is generally available and represents a prime alternative to coal as feedstock. With coal-like compositions, the goal of this work is to design and optimize systems for biomass firing to reduce society's dependence on fossil fuels for power production. Considering this goal, co-fired units have become more popular. When appropriate amounts of biomass and coal are mixed and cofed to an optimized burner, SOx, NOx, and greenhouse gas emissions decrease [6]. Using demonstrated co-firing technology reported elsewhere [7], significant cost savings in power generation using biomass range from $80,000/year to $400,000/year units [8]. In addition, energy security is enhanced using a sustainable energy of renewable fuel sources such as biomass to operate combustion

devices. To support these goals, computational and numerical techniques have been applied to optimize a biomass fired burner. The use of detailed chemistry schemes for gas phase reactions is well established for the combustion of hydrocarbons such as methane, acetylene, ethane, propane and butane. The most widely used detailed mechanism for the combustion of methane and methane hydrogen mixtures is GRI-Mech 3.0 (developed by the Gas Research Institute) consisting of 53 species and 325 chemical equations showing the formation and destruction of intermediate radicals/species [9]. Mechanisms such as the LLNL (Lawrence Livermore National Lab) developed C1eC4 oxidation/combustion mechanism also includes a general NOx formation mechanism. These and other such detailed mechanisms are often available for use in ChemKin/CANTERA format which commercial CFD codes can readily use. However, the inclusion of a large numbers of species and chemical reactions makes their usage “as-is” prohibitively expensive from a computation point of view for industrial combustion applications. A common workaround for this issue applied to gas phase flows is the use of chemistry agglomeration or ISAT (In-Situ Adaptive Tabulation) methods to reduce the effective number of chemical species accounted for in the detailed combustion model. Another option is the use of steady and unsteady laminar flamelet models and flamelet generated manifold models, which solve for species in the mixture fraction space to simulate gas phase combustion. The current model incorporates heterogeneous (particle surface) reactions as well as gas phase reactions, and

J.D. Smith et al. / Renewable Energy 145 (2020) 1597e1607

biomass volatiles typically include H2O, H2, CO, and N2. Since biomass has a higher oxygen content compared to coal, more CO is generally produced in the gas phase during biomass devolatilization. Biomass char oxidation also generates more CO and H2 than coal char does which provides more energy, which tends to sustain biomass devolatilization and consequently affects near combustion near the burner. The homogeneous gas phase reaction mechanism with associated chemical kinetic parameters together with the heterogenous particle devolatilization and char oxidation reactions are now presented.

Table 1 Proximate and ultimate analyses of the woody biomass (pulverized wood). Proximate analysis (wt%) Moisture Volatile Matter Fixed Carbon Ash

5.06 82.95 16.15 0.90

Ultimate analysis (wt%) Carbon Hydrogen Oxygen Nitrogen Sulfur Low heating value (MJ/kg)

1599

48.6 6.4 44.0 0.1 0.9 18.6

2.1. The combustion model The homogeneous gas-phase reaction mechanism is based on extensive work reported in the literature and validation against specific experimental tests. The Arrhenius reaction kinetic parameters required for these reactions from the literature were used to build this combustion model (see Table 2).

hence in this case, a global reduced chemistry mechanism as presented is considered a practical compromise for modeling biomass combustion applied to industrial burner optimization. Previous work has shown that a simplified two-step methane combustion mechanism can perform nearly as well as a complicated detailed kinetic mechanism such as GRI-Mech3.0 in terms of estimating temperature and species mass fraction profiles within a natural gas combustor [10]. As high-speed computers have become more widely available, advanced turbulent mixing models have been developed and used in CFD analysis of flow problems. For example, the standard RANS (Reynolds Averaged Navier-Stokes) based turbulence models have given way to higher fidelity turbulence models such as DES (discrete eddy simulation), LES (Large Eddy Simulation) and hybrid LES- RANS models. For this study aimed at optimizing industrial combustion equipment, the steady-state RANS technique was used to reduce the computational effort required to run several design cases. This approach was justified because most industrial combustion equipment operate in a “quasi steady” condition plus typical experimental data of species compositions, velocity profiles, and temperature profiles are collected as time averaged values. Thus, this assumption is valid as long as the combustion equipment does not operate in a highly non-steady condition such as flame liftoff, extinction, re-attachment or ignition. Since the present work considers optimizing industrial biomass fired burners operating in a “quasi-steady” condition, the RANS based models are justified. Comparing numerous coal and biomass compositions shows that coal generally has a higher C content and a lower O content than biomass [5,7]. Accordingly, the contribution to the combustion process from volatile matter is much higher for biomass combustion. The mass fraction of volatile content plays a pivotal role in the devolatilization reactions, char oxidation reactions and gas phase reactions. Devolatilization dominates the reaction process in the near burner region, where volatiles evolve from the biomass particles and react in the gas phase while the resulting char particles oxidize away from the burner. Based on the fuel composition,

1 k1 CH4 þ O2 /CO þ 2H2 2

(1)

1 k2 H2 þ O2 /H2 O 2

(2)

1 k3 CO þ O2 /CO2 2

(3)

k4f ;k4r

CO þ H2 O

⇔

CO2 þ H2

(4)

Reaction rate constants for the first three homogeneous gasphase reactions are denoted by k1, k2 , and k3 . The water gas shift (WGS) reaction includes both a forward step (k4f ) and a reverse step (k4r ). Heterogenous combustion includes particle devolatilization and char oxidation described next. The particle devolatilization model used in this work is based on the Kobayashi et al. [11] “competing-rate” model. Overall weight loss is determined by:

 ðt   dmv
¼ mp;0  ma ða1 R1 þ a2 R2 Þexp  ðR1 þ R2 Þdt dt

(5)

0

where R1 ¼ A1 expðE1 =RTp Þ and R2 ¼ A2 expðE2 =RTp Þ are the two competing rates that control the devolatilization over different temperature ranges. The yield factors a1 and a2 represent devolatilization at low and high temperatures, respectively. The yield factors are feed specific and are set based on the fuel's proximate analysis (see Table 1). The heterogeneous reactions included in the combustion model

Table 2 Reaction rates for bio-char oxidation and gas combustion reactions. Reaction Rate

Ar (s1)

Er (J/kmol)

k1 k2 k3 k4f

4.4 x 1011 2.5 x 1016 3.16 x 1012 5 x 1012

1.25 1.68 1.67 2.83

k4r k5 k6 k7 k8

9.5 x 1010 0.052 0.0782 0.0732 1.2 x 105

2.39 x 108 6.1 x 107 1.15 x 108 1.125 x 108 7.53 x 107

x x x x

108 108 108 108

0 00 Q ½C j;r ðhj;r þhj;r Þ

A

B

Source

0 1 0 0

[CH4]0.5[O2]1.25 [H2]0.5[O2]2.25[H2O]1 [CO]1.5[O2]0.25 [CO]0.5[H2O]1

11.5 3.1 2.1 4

2.75 0.75 0.53 0.5

Jones & Lindstedt [12] Jones & Lindstedt [12] Wu et al. [13] Callaghan [14]

0 0 0 0 0

[CO2] [H2]0.5 e e e e

4 e e e e

0.5 e e e e

Callaghan [14] Chen et al. [15] Chen et al. [15] Chen et al. [15] Govind & Shah [16]

nr

j

1600

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The gas/solid reactions for biomass devolatilization and char oxidation along with the gas phase reactions are included in the overall combustion reaction mechanism used in this work. All kinetic parameters required by these reactions are listed in Tables 2 and 3. The reaction rates for these homogeneous and heterogenous reactions are based on information collected from the literature for the gas phase reactions listed in Eqs. (1)e(4), char oxidation steps listed in Eq. (9)e12 and the two-step competing reaction devolatilization mechanism listed in Eq. (6)e7. Volatile decomposition described in Eq. (8) represents gas products evolved from the biomass particles during devolatilization.

Table 3 Devolatilization kinetic parameters used in Eqs. (6) and (7) [15]. Parameter

Value

Yl Yh Al (1/s) Ah (1/s) El (J/kmol) Eh (J/kmol) Pressure (MPa)

0.8 0.81038 370,000 1.5 E13 7.4 E7 2.5 E8 2

describe biomass devolatilization, which occurs when the biomass particles are heated to the devolatilization temperature. Prior to devolatilization, biomass particles also evolve moisture which enters the gas phase and affects the WGS reaction. As biomass particles heat, the model predicts the rate at which volatiles are produced according to Kobayashi's “competing rate” model (Eqs (6) and (7)). These volatiles then decompose into gas phase species according to Eq. (8). As volatiles are driven off from the biomass particles, biochar also forms which then oxidizes heterogeneously according to the reactions listed in Eqs (9)e(12). Competing Steps Devolatilization Reactions Biomass / (1-Yl) * CharlþYl * Volatile (Low Temperature)

(6)

Biomass / (1-Yh) * CharhþYh * Volatile (High Temperature)

(7)

Volatile Production from Biomass

C0:4273 H1:142 O0:4634 N0:003 / 0:4273CO þ 0:57105H2 þ 0:0016N2 þ 0:01805O2

(8)

Bio-Char Oxidation via Oxygen

1 k5 C þ O2 /CO 2

(9)

Bio-Char Oxidation via Steam k6

C þ H2 O/CO þ H2

(10)

Bio-Char Oxidation via Carbon Dioxide k7

C þ CO2 /2CO

(11)

Bio-Char Oxidation via Hydrogen k8

C þ 2H2 /CH4

(12)

2.2. Biomass combustion base case Based on the reaction mechanism listed above, biomass combustion was modeled using a CFD model implemented in StarCCMþ. Input required for this model included the biomass composition defined in the Proximate and Ultimate Analysis (see Table 1). The geometric information for the computational mesh was based on the biomass process burner shown in Fig. 1. Based on the general burner configuration shown in Fig. 2, the burner design can be optimized to improve biomass combustion efficiency.

2.3. Burner geometry The Burner Engineering Research Laboratory (BERL) 300 kW burner design [17] was selected for this optimization study. The geometry of the burner is detailed in Fig. 3. The burner configuration was designed for natural gas, but this study modified the geometry by omitting the radial holes shown for injecting natural gas while allowing air swirl from the secondary air stream to enhance dispersion of biomass particles entrained by the carrier air. This swirling improves mixing, and consequently provides better temperature distribution near the burner outlet. Based on a swirl number of 0.56, the swirl vanes CAD model was developed so that they created a realistic swirl flow of the inlet air across the swirl vanes. Optimizing the local mixing at the inlet has been shown to improve combustion performance in coal flames by controlling the location of the devolatilization zone and resulting flame height/ base flame width and the location of the internal recirculation zone. Accordingly, this work used a Design of Experiments (DOE) methodology to systematically vary the burner design by adjusting the parameters listed in Table 4 with a computational fluid dynamics (CFD) code coupled to an optimization tool.

Fig. 2. Biomass burner design optimization metrics.

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Fig. 3. BERL 300 kW burner geometry [17].

Table 4 Optimization parameters for initial designed experiment. Parameter

Design Change Considered

Quarl Slope Quarl Design Step Height

Slope increase from 20 to 250 Straight surface compared to convex surface Step height Increased by 20% in radial direction

Fig. 4. Mesh of a sphere embedded in an ASD grid with control points that can move and deform the ASD grid deforming the sphere mesh.

This tool has been applied to internal and external flow problems in many industries where the system geometry affects the fluid dynamics, heat or mass transfer, chemical reactions, or combustion performance using the following capabilities:

3. Burner design optimization The optimization tool selected for this work was based on the Arbitrary Shape Deformation (ASD) technique implemented in Sculptor and coupled with a commercial CFD code called StarCCMþ. This linked approach closely links CFD predicted results for turbulent fluid flow and combustion to the reactor shape to reduce the effort required to evaluate how the reactor design affects local fluid flow and associated combustion performance. StarCCMþ was selected for this analysis because of its ease-of-use and extensively validation for combustion and mixing applications [18]. ASD has also been included in other commercial CFD codes (i.e., Ansys-Fluent) which to perform design optimization as reported by the authors in subsequent work [19]. The traditional Computer Aided Design (CAD) tools do not account for the coupled fluid-solid interactions inherent in a physicsbased shape optimization. The ASD technique enables one to control and manipulate the system geometry based on criteria related to local fluid flow by manipulating the CAD shape and the CFD mesh simultaneously. This is done by defining a set of control points around the physical geometry to be optimized. When these control points are moved, underlying functions describing the geometry morph the design into a new shape which provides the ability to find a design that satisfied the optimization criteria. This is best illustrated using a physical analogy. Consider a volume of clear, flexible plastic, in which the object to be optimized is embedded. The embedded object has the same degree of flexibility as the volume so that as the plastic volume is deformed, the embedded geometry is also deformed in the same manner (see Fig. 4). The volume is modeled as a tri-variate parametric volume with its deformation controlled by a small set of control points. By creating the ASD, the user moves the control points and the associated geometry as desired. ASD allows the designer to freely create general shape parameters beyond those found in the CAD model which allows a smooth volumetric deformation in real-time without having to re-CAD or re-mesh the geometry. This tool provides the ability to parameterize, deform and optimize shapes into new and improved designs where the CAD model did not offer the necessary parameters or degrees of freedom required to perform this process.

(1) Import and export CAE model to general CFD tools using common file formats (i.e., STL, IGES, STEP) (2) Graphical User Interface (GUI) to create ASD mesh for general shapes with user-defined shape change parameters to translate, rotate, or scale CFD mesh (3) Monitor mesh quality during deformation process that allows mesh quality to be used as constraint in optimization process (4) Optimization algorithms include Generalized Reduced Gradient Algorithm or Design of Experiments with Optimal Latin Hypercube and Response Surface (5) Close integration between CFD and CAD tools allows transfer of optimized shape in general file format (i.e., STL, IGES, STEP) The present application considers a biomass/coal combustion burner (as shown in Fig. 1) whose geometry was optimized by adjusting the step height, quarl slant, and quarl shape. A cylindrical ASD volume was created using four radial control points with ten control points around the circumference and six control points along the length of the inlet portion of the burner (see Fig. 5). The model shown below considers a 72-degree section of the burner (Fig. 6). The burner step height was adjusted (see Fig. 7), the burner quarl slant was adjusted (see Fig. 8) and the burner quarl shape was adjusted (see Fig. 9).

3.1. Design of experiments and response surface methodology The Design of Experiments (DOE) strategy evaluates a product design by analyzing product performance in several possible operational modes and conditions in an organized fashion to determine which design factors have the greatest effect on performance. The goal is to adjust the design factors to optimize product design. Thus, understanding how design factors effects system performance, product manufacturability, and product robustness facilitates choosing the best (optimal) design. One way to use a DOE to optimize product design is to create an approximation of the feasible design space using an Optimal Latin

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Fig. 5. A 4 x 10 x 6 cylindrical ASD volume with control points arranged to change burner step height, quarl slope, and quarl shape.

Fig. 7. Burner Step height modified by moving control points at outer edge in radial direction by 20% resulting in a larger step height (original height ¼ 0.00783 m ¼> modified height ¼ 0.009396 m). For a fixed reactor inlet diameter increasing the burner step height also decreases the burner quarl slope.

varying multiple inputs simultaneously without an organized methodology makes it impossible to separate couple effects. The systematic methodology inherent in a formal DOE allows defining a design matrix by specifying values of the design parameters for each experiment. The optimizer tool used in this study incorporated a formal DOE for this project.

Fig. 6. Close-up view of the undeformed inlet region of the burner {{}}

Hypercube. This type of DOE picks points in the design space to approximate potential designs in an efficient fashion without having to perform every analysis in the design evaluation. This feature is especially important when performing large expensive CFD/FEA analyses. Experimental design, in which a prescribed set of experiments or trials (design evaluation) is performed, can be used to investigate which design parameters have the greatest effect on system performance to identify the best (optimal) design. Primary considerations to consider in developing an experimental design include: Fig. 2. A 4x10x6 cylindrical ASD volume with control point planes placed in a fashion to change the step height, quarl slope, and quarl shape.  Number of experiments possible given cost and time constraints  Values for parameters for each experiment  Proper interpretation of results Although modifying one variable at a time (as in a factorial design) may lead to an improved design, this approach often fails to identify coupling between design variables and can be very costly. Varying one input at a time cannot capture interaction effects and

3.1.1. Optimal Latin Hypercube The Latin Hypercube Design as described by Park [20] randomly samples parameter space in performing a computational designed experiment. An Optimal Latin Hypercube2 is a modified Latin Hypercube, in which the combination of factor levels for each factor is optimized, rather than randomly combined. With this technique, as with random Latin Hypercubes, the design space for each factor is uniformly divided (the same number of divisions [n] for all factors). These levels are then randomly combined to generate a random Latin Hypercube as the initial DOE design matrix with “n” points (each factor level is studied only once). An optimization process is then applied to this initial random Latin Hypercube design matrix. By swapping the order of two factor levels in a column of the matrix, a new matrix is generated and the new overall spacing of points is evaluated. The goal for this optimization is to design a matrix where points are spread as evenly as possible over the design space defined by the lower and upper level of each factor. 3.1.2. Response surface model Response Surface Models (RSM) use polynomials of low order (from 1 to 4), kriging, or radial basis functions, to approximate response of an actual analysis code. A number of exact analyses using the simulation code(s) must be performed initially to construct a model, or alternatively a data file with a set of analyzed design points can be used. The model then can be used in optimization and sensitivity studies with a very small computational expense, since evaluation only involves calculating the value of a polynomial (or other function) for a given set of input values. Accuracy of the model is highly dependent on the amount of data used for its construction (number of data points), the shape of the

2 Latin hypercube sampling (LHS) is a sampling method often used to construct computer experiments [22].

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producing lowest NOx emissions, etc.). Also, a response surface model can be fit with the function data and used as a surrogate model to explore design space further. 3.2. Design variables: ASD control point groups

Fig. 8. Burner quarl slope is increased via radial expansion of control points located at intersection between quarl and main burner (deformation represents changing slope from 20 to 25 while keeping the original step size constant by increasing the reactor inlet diameter).

The design variables used for this preliminary analysis were defined in Table 4 and included a change in step height, the quarl slant, and the quarl curvature. The angles of the swirl vanes were also investigated but are not shown here. Although this paper does not include a complete DOE set of design experiments, this work has been performed and will be reported in the presentation. Results completed and presented in this paper focused on varying each design variable (step height, quarl slope, quarl curvature). The subsequent presentation includes results of the complete DOE optimization. However, results from the 3 exploratory cases illustrated the impact of changing the burner design and demonstrated how the optimization tool can be applied to combustion equipment. 3.3. Objective functions and design goals The goal of the design optimization was to find the ‘best’ design that would improve burner operation. In this case, the key is identifying how the design changes affected the flare shape and size of the Internal Recirculation Zone (IRZ). Ultimately, the optimization will directly impact NOx reduction from the burner. 4. Design optimization results

Fig. 9. Quarl shape changed via radial expansion of control points between step and combustion chamber wall resulting in deforming quarl shape concave inward.

exact response function that is approximated, and the volume of the design space in which the model is constructed. In a sufficiently small volume of the design space, any smooth function can be approximated by a quadratic polynomial with good accuracy. For highly non-linear functions, polynomials of 3rd or 4th order can be used. If the model is used outside of the design space where it was constructed, its accuracy is impaired, and refining of the model is required. The burner design problem consists of 3 design variables: 1) step height, 2) quarl slope, and 3) quarl shape. Using an optimal Latin Hypercube DOE and knowledge of how much computational resources are available, one decides on how many complete CFD analyses can be run. Using the OLH-DOE, the ‘optimal’ burner design will be found by arrangement of the design sets within the 4-dimensional design space. Once these were defined, all CFD runs were executed using parallel computing since each design is a separate CFD calculation. All cases were run simultaneously on a large High-Performance Computer (HPC). Once each case was run, the key function values were post-processed and returned to the optimization tool to allow the best design to be found (i.e. design

Biomass combustion was initially modeled with no geometric shape changes to establish a base operating condition. STARCCM þ v8.04.010 was used to model the biomass combustion. Simulations were run using two Intel Xeon X-5690 CPUs (2 x 6 processors) running at 3.46 GHz with 48 GB RAM. The Euler-Lagrangian formulation referred to as the Discrete Phase Method (DPM) was used to simulate the multi-phase flow. The standard k-e turbulence model with partially-premixed finiterate/eddy dissipation (EBU) gas combustion model was used for turbulent reaction chemistry. Gas phase radiation was modeled using the Discrete Ordinates Model (DOM-S4) by specifying an optical path length calculated with the weighted sum of the constituent gray gases. Radiation was not included for the particle phase. The particle size distribution was assumed to follow a RosinRammler distribution (see Table 5). Ultimate and Proximate analyses were performed on the biomass particles which were used in the reaction scheme as described earlier. The Semi-Implicit Pressure Linked Equations (SIMPLE) scheme was used for pressurevelocity coupling with least squares technique for spatial discretization gradient. A second-order spatial discretization upwind scheme was used for the momentum, turbulence, energy, species, and radiation finite difference equations. The boundary conditions for the “faces” of the threedimensional segment were assigned periodic boundary conditions with interfaces. The input conditions shown in Table 6 were

Table 5 Rosin-Rammler particle size distribution parameters. Rosin-Rammler parameters Minimum diameter (mm) Maximum diameter (mm) Mean diameter (mm) Spread parameter Number of diameters

0.05 0.25 0.10 2.5 12

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J.D. Smith et al. / Renewable Energy 145 (2020) 1597e1607 Table 6 Base case input data. Lagrangian Phase Particle Mass flow: 0.025 kg/s Particle temperature: 550 K Gas Phase Carrier air velocity: 10 m/s Carrier air temperature: 800 K Secondary air velocity: 15 m/s Secondary air temperature: 1100 K

used for the base case conditions. These conditions were also used to analyze biomass combustion for the three cases with modified geometries. Solutions for each of the cases were obtained on the same mesh for each case. CFD predictions for gas phase temperature, particle temperature and velocity profiles in the reactor based on the input data listed in Table 6 are presented in Fig. 10, Fig. 11 and Fig. 12.

4.1. Analysis of optimized burner design Examining Fig. 10 shows that the flame temperatures for all cases are similar. The base case peak flame temperature is approximately 400 K lower than the peak flame temperature for all other cases. Further, it is observed that the base case shown in Fig. 10a and d have similar and shorter flame lengths compared to Fig. 10b and c, which appear to be longer with similar flame lengths. Based on these observations, it is concluded that the 20% change in step height produces a more stable flame anchored near the burner,

Wall Boundary Conditions Furnace wall maintained at 800 K Biomass Heat Content: Higher Heat Value: 18.75 MJ/kg Biomass Elemental Composition (wt. %, dry): Carbon: 46.78 Hydrogen: 6.38 Nitrogen: 0.25 Oxygen: 46.59

which supports early devolatilization compared to the other cases. A closer look at Fig. 10d reveals a ‘second flame’, closer to the flow axis, originating from the region where the high-temperature devolatilization occurs, based on plots of volatile mass fraction. This phenomenon shows only approximately a 5% reduction in peak flame temperature compared to Fig. 10b and c. Also, the bulk gas temperature in Fig. 10d appears to be lower than 1400 K, compared to the design other cases, which show bulk gas temperature above 1600 K. Secondly, based on the streamlines of particles colored by temperature from Fig. 11, it is seen in Fig. 11d that the recirculating particle paths arise primarily from backflow along the wall from top to bottom. In comparison with the unmodified geometry in Fig. 11a, there is a drastic reduction in the stagnancy of particle flow caused by internal recirculation zones (IRZs). It is observed in both Fig. 11b and c, that the IRZ seen in Fig. 11a on the right of the flame, gets elongated and tilts towards the wall, at the top. This may be attributed to the gas temperature profiles seen in Fig. 10b and c,

Fig. 10. Temperature profiles of biomass combustion: (a) Base case (b) Curved quarl case (c) Increased quarl slope case (d) Increased step height case.

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Fig. 11. Particle temperature streamlines: (a) Base case (b) Curved quarl case (c) Increased quarl slope case (d) Increased step height case.

where the flame is anchored further away from the burner than those in Fig. 10a and d. This is due to relatively inefficient particleair mixing, which delays particle heating and subsequent particle devolatilization. This observation is supported from the velocity vector seen in Fig. 12. The base case in Fig. 12a shows an elliptical IRZ centered to the right of the end of the flame. The geometry changes in Case b and Case c deform flow profiles as seen in the vector plots Fig. 12b and c which lead to elongation of these IRZs. As mentioned earlier, the changes in IRZ shape and location result in inefficient mixing, causing the flames (Fig. 12b and c) to be anchored higher than those in Fig. 12a and d, due to a delayed onset in particle devolatilization. However, the velocity profile seen in vector plot Fig. 11d indicates a breakdown of the velocity component tangential to the flame, contributing to more uniform mixing in regions with large temperature gradients, leading to a more homogeneous temperature distribution. It is believed that this explains the uniform bulk temperature near the flame shown in Fig. 10d. Based on these observations, it is suggested that the increased step height before ignition provides a longer draft with a smaller cone angle, allowing the swirling air from the annular inlet to mix with a narrower stream of particle laden air. The particle streamlines from this region appear to anchor the flame in Fig. 11d, producing a narrower flame than seen in Fig. 11b and c, which is anchored closer to the burner and has a more uniform temperature distribution near the flame and a smaller IRZ in the bulk fluid.

5. Conclusions and recommendations Combustion of biomass in the 300 kW BERL setup was modeled using the eddy break-up partially-premixed combustion model. The geometry was subjected to shape deformations to develop three unique burner designs. Of the three, it appears the increase in step height produced the most significant impact on the flame due to a reduced size and location of the IRZ. The flame also appeared to be anchored closer to the burner for the step change case which impacted the region where the first biomass particle devolatilization occurs. This was attributed to increased mixing efficiency created by the swirling air facilitated by a larger step size creating a narrower primary air-particle stream. The longer step appeared to create a smaller area for mixing particle laden air with the swirling secondary air. Although, devolatilization kinetics control the rate of volatile release which also impacts local flame temperature near the burner, the increased mixing leads to earlier devolatilization which also anchors the flame closer to the burner. Consequently, the dissipation of energy into the bulk gas produces a more uniform temperature distribution near the flame. The observation of most important factor effects is based on visual inspections of the CFD predictions shown in the figures above. Specific factor effects can be quantified using traditional statistical design of experiments (DOE) techniques. The purpose of this paper has been to demonstrate the power of the linked ASD þ CFD methodology using Scupltor and StarCCM þ to examine the impact of burner design on mixing and combustion

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Fig. 12. Vectors of velocity showing recirculation zone changes to the right of the flame in biomass combustion from (a) base case (b) curved quarl (c) quarl slope increase from 200 to 250 (d) 20% increase in step height.

performance. It is recommended that additional work be completed using this linked approach in a traditional DOE to quantify factor effect.

[10]

Acknowledgment

[11]

Financial support for this work was provided by the Wayne and Gayle Laufer Foundation and by Elevated Analytics.

[12] [13]

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J.D. Smith et al. / Renewable Energy 145 (2020) 1597e1607 Plan. Inference 39 (1) (April 1994) 95e111. [22] Wikipedia, Latin hypercube sampling [Online]. Available: https://en. wikipedia.org/wiki/Latin_hypercube_sampling. (Accessed 18 February 2019).

Further readings [21] W. Chen, C. Chen, C. Hung, C. Shen, H. Hsu, A comparison of gasification

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phenomena among raw biomass, torrefied biomass and coal in an entrainedflow reactor, Appl. Energy 112 (2013) 421e430. December. [23] C. Chen, M. Horio, T. Kojima, Numerical simulation of entrained flow coal gasifiers. Part I: modeling of coal gasification in an entrained flow gasifier, Chem. Eng. Sci. 55 (2000) 3861e3874.