Article pubs.acs.org/EF
Simulation of a Commercial-Scale Entrained Flow Gasifier Using a Dynamic Reduced Order Model Rory F. D. Monaghan*,† and Ahmed F. Ghoniem Reacting Gas Dynamics Laboratory, Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States ABSTRACT: The development of accurate, flexible, and robust dynamic reduced order models (ROMs) of entrained flow gasifiers (EFGs) is an important step toward greater commercialization of that technology. Previous work by the authors described the development, validation, and sensitivity analysis of such a ROM.1,2 This paper presents the results of dynamic simulation of a commercial-scale General Electric (GE or Texaco) gasifier and syngas cooling system. The base case for simulation is introduced, and the ROM is used to simulate six cases of dynamic gasifier operation. The objective of this work is to develop a computationally efficient simulator to assess steady-state and dynamic performance of entrained flow gasifiers under a wide range of realistic operating conditions. The six cases simulated are (1) removal of fluxant, (2) load following, (3) feed switching, (4) coal−petroleum coke cofiring, (5) coal−biomass cofiring, and (6) gasifier cold start. The results of dynamic simulation show that slagging properties (viscosity, temperature, and layer thickness) are always the last variables to reach steady state. This is primarily due to the large heat capacity of gasifier refractory walls. For the fuel-switching and cofiring cases, slag viscosity is found to be extremely sensitive to feedstock ash composition and gasifier refractory wall temperature. For cases 1−5, syngas production is predicted to reach steady state with very little lag. The ROM predicts gasifier cold start to occur over a time-scale of 50−60 h. The main reason for such a long start-up time is the requirement to limit maximum rates of heating of refractory faces. The simulation result agrees with industrial experience of start-up times of 2−3 days. All dynamic simulations took 15−45 min on a desktop personal computer, with the exception of the gasifier cold start case, which took 6 h.
1. INTRODUCTION Carbon dioxide capture and storage (CCS) is recognized as one of a suite of technology options that can be used to reduce greenhouse gas emissions from continued usage of relatively cheap and abundant fossil fuels. Several approaches to CO2 capture, the most expensive step in CCS, have been suggested, among them, precombustion capture systems (integrated gasification combined cycle, or IGCC), which employ entrained flow gasifiers (EFGs).3,4 Previous work by the authors has highlighted the necessity for dynamic, flexible, and robust reduced order models (ROMs) for simulating EFGs and described the development, validation, and sensitivity analysis of such a dynamic ROM created in Aspen Custom Modeler (ACM), a process and equipment model development and simulation environment developed by Aspen Technologies.5 The ROM is briefly summarized below. For full descriptions of ROM development, validation, and sensitivity analysis, refer to references.1,2 1.1. ROM Structure. The ROM is built upon a reactor network model (RNM) that is a modification of that developed by Pedersen et al. for axially fired swirling coal combustors.6,7 The original Pedersen RNM consists of two well-stirred reactors (WSRs) and two plug flow reactors (PFRs) to model internal and external recirculation zones (IRZ and ERZ), the jet expansion zone (JEZ), and the downstream zone (DSZ). Modifications to the original RNM consist of the following: (i) the addition of an upstream WSR to enable the simulation of the first stage, coal combustion zone (CCZ) of two-stage gasifiers and (ii) the addition of a downstream PFR-WSR for simulation of four possible syngas cooling configurations; radiant-only, quench-only, © 2011 American Chemical Society
radiant-and-quench, and no cooling. The modified RNM used for the ROM is shown in Figure 1. In addition to gasifier geometry and boundary conditions, five parameters define the ROM: (1) length and (2) diameter of the IRZ (LIRZ and dIRZ), (3) jet expansion angle (θ), (4) recirculation ratio (α = ṁ r/ṁ in), and (5) fraction of flow entering the JEZ directly from the ERZ (f JEZ = ṁ ERZ‑JEZ/(ṁ ERZ‑JEZ + ṁ ERZ‑IRZ)). These parameters are evaluated in the same manner as that employed by Pedersen et al.; namely, LIRZ = dIRZ, θ = 9.7°, and α = 0.47(dgasifier/dquarl) − 0.5, with dquarl = 0.5 m. The RNM consists of a number of switches that enable the simulation of a variety of EFG designs. The switches allow a single ROM to simulate: (1) up- and down-flow gasifiers by changing the sign of the gravitational constant (g) in the particle, gas-phase, and slag layer momentum conservation equations, (2) slurry-fed and dry-fed (steam) gasifiers by calculating properties for H2O inlet streams in liquid or gaseous H2O states, (3) axial and opposed injector configurations by changing dIRZ, (4) O2- and air-blown gasifiers by changing the concentration of O2 and N2 the inlet streams, (5) one- and two-stage gasifiers by excluding or including the mass flow from the CCZ to the IRZ, (6) refractory-lined, membrane-lined, and electrically heated wall designs by changing the form of the mass and energy conservation equations used for the gasifier wall layers, and (7) radiant-only, quench-only, radiant-and-quench, and no syngas Received: October 11, 2011 Revised: December 19, 2011 Published: December 27, 2011 1089
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Figure 1. Flexible RNM for a one- or two-stage gasifier with syngas cooling.
cooling designs by activating or deactivating reactors in the PFRWSR cooler block. Within each reactor or zone of the RNM, the ROM expresses unsteady mass, energy, and momentum conservation equations in a fixed reference frame, treating solid and gas phases as pseudofluids. In addition to the conservation equations for the gas−solid flow in the gasifier, mass and energy balances are performed on the walls of the gasifier, to establish the wall temperature profile and slag layer thickness. The conservation equations are one-dimensional in the axial direction and are expressed in the ROM as dynamic equations, even for steadystate simulations. The ACM allows easy switching between steady-state and dynamic simulation runs. The ACM was chosen as the simulation environment for this work for the following reasons: (1) This reduced order modeling work is part of a broader simulation effort of gasificationbased power and polygeneration plants underway at MIT. The plant-wide simulation is under development in the Aspen Plus process flowsheet simulation environment. A ROM developed in the ACM can easily be incorporated into such a simulation tool. (2) The ACM can readily access the large physical and thermodynamic properties database shared by all AspenTech products. (3) The industrial sponsor of this work currently uses AspenTech products for process and equipment modeling. The ACM is the most recent development of the SpeedUp chemical engineering simulation environment developed at Imperial College London.8 The underlying linear equation solver used in the ACM is MA48, part of the Harwell Subroutine Library developed at Rutherford Appleton Laboratory and described in detail in ref 9. Nonlinear equations are solved using a mixed Newton method, meaning that, for steady-state simulation, a standard Newton method is used, which calculates a new Jacobian matrix with each iteration, while for dynamic simulation, a fast Newton method is employed, which calculates a new Jacobian matrix only when convergence progress is slow. Convergence is achieved for groups of nonlinear equations using the expression ΔXerr < ΓrelX + Γabs, where X is the quantity being for solved for, ΔXerr is the error between local values calculated analytically within the procedure and calculated numerically by the ACM, and Γrel and Γabs are the relative and absolute tolerances, respectively, and have default values of 10−5. 1.2. Submodels. The ROM incorporates numerous submodels for the following: multiple feedstocks, mixing and recirculation, particle properties, drying and devolatilization, chemical kinetics, fluid dynamics, heat transfer, pollutant formation, slag behavior, and syngas cooling. These submodels are summarized below. For full details of these submodels and their incorporation into the ROM, refer to ref 1.
• Multiple feedstocks: Mass-weighted average feedstock properties (particle sizes, temperatures, compositions, rate parameters) are used for multiple feeds. • Particle density: Initial density, hydrogen mole fractionbased correlation;10 instantaneous density, function of particle consumption, linked to random pore model via density evolution parameter.11 • Particle enthalpy: Spatially uniform function of particle and char compositions.12 For pulverized particles at entrained flow gasification conditions, assumption of spatially uniform particle properties is valid. • Particle surface area: Initial surface area, correlated to volatile and fixed carbon fractions;13 instantaneous surface area, random pore model, linked to heterogeneous kinetic submodel.14,15 • Devolatilization: Instantaneous rate with volatiles composition and yield correlated to coal composition. Tracked products are CO, CO2, H2, H2O, CH4, C2H6, NH3, H2S, tar, and char.16 • Gas-phase kinetics: Reaction rates of CO, H2, CO2, H2O, O2, N2, CH4 modeled.17,18 Kinetic rates of C2H6 and tar oxidation modeled as CH4. Tar assumed to have physical properties of C6H6. All major species gas-phase kinetics much faster and therefore unimportant compared to heterogeneous chemistry. Local gas-phase equilibrium reached under all conditions. • Heterogeneous kinetics: Choice of high-pressure intrinsic (particle surface area-based) or extrinsic (lumped diffusion-chemistry) kinetic rate expressions for char reacting with O2, H2O, and CO2. Extrinsic data used for this study (see Table 1).19 Kinetic submodel linked to Table 1. High-Pressure Heterogeneous Kinetic Rate Parameters Used in ROM reactant temp. range (°C) ψ Aex,m (106/MPan/s) Eex,m (MJ/kmol) nex,m
O2 14 136 130 0.68
H2O 1260 3 0.0855 140 0.84
1200 3 0.0678 163 0.73
particle surface area and density submodels by random pore model. Pollutant products of char consumption released in proportion to N and S composition of char. • Fluid dynamics: Gas−particle and gas−wall viscous interaction.20,21 • Heat transfer: Particle-to-particle radiation, Rosseland radiation-as-diffusion model for optically thick media;22,23 particle-to-gas and gas-to-wall forced convection, Nusselt number correlations.24,25 Axial and 1090
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radial conduction in gasifier wall layers. Convective heat transfer to wall cooling streams if option is selected. Natural convection and radiation to black environment from outer walls of gasifier. Convective term calculated by Nusselt number correlation. • Pollutant formation: Nitrogenous compounds, released from solid phase by devolatilization and char consumption to form HCN and NH3. Gas phase reactions modeled by reduced global kinetic submodel, which tracks HCN, NH3, NO, N2 ,and N-containing tar.26,27 Sulfurous compounds, released from solid phase by devolatilization and char consumption to form H2S. Gas phase reactions modeled by instantaneous, complete oxidation of H2S and S-containing tar to SO2, followed by local equilibrium of SO2, COS, and H2S.28 Mercury compounds are not tracked, as a suitable reduced reaction model could not be found in the literature. • Slag behavior: Slag flow on walls, continuity and momentum equations with spatially variant viscosity;29 slag viscosity, single-layer temperature- and compositiondependent viscosity submodel.30 • Syngas cooling: Quench cooling, frozen chemistry due to virtually instantaneous temperature drop; radiant cooling, high-pressure kinetic rate expression that shows departure from equilibrium as temperature drops in cooler.31 An advantage of reduced order modeling over CFD-based modeling is the ability to incorporate many physical phenomena using the large number of submodels shown at minimal computational cost. By means of comparison, the state-of-theart CFD-based gasifier simulation performed by Watanabe and Otaka19 does not include submodels for pollutant formation or slag behavior, which are two key constraints of gasifier operation. Additionally, CFD simulations of gasifier operation are for steady-state operation only. The computational expense of dynamic CFD simulation is, in most cases, prohibitive. Because of the fact that the (i) heterogeneous kinetics and (ii) slag behavior submodels are referred to frequently in this work, they are summarized as follows. The high-pressure extrinsic heterogeneous kinetic parameters for pulverized bituminous coal used by Watanabe and Otaka19 are employed in this work and are shown in Table 1. The extrinsic approach to heterogeneous reaction rates lumps diffusive and kinetic processes for small particles (
