Multiloop Control Strategies for a Dry Feeding ... - ACS Publications

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Multiloop Control Strategies for a Dry Feeding Gasifier in the Integrated Gasification Combined Cycle Hyojin Lee and Jay H. Lee* Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Korea S Supporting Information *

ABSTRACT: Three control strategies for a dry feeding gasifier are studied. First examined is the fixed-ratio control strategy which keeps fixed ratios of the different feeds. Simulation results show that this strategy moves the system along a suboptimal path during load changes as the fixed ratios are selected for a steady-state operation. Two of the three chosen controlled variables (CVs) did not settle back to their set-points in the disturbance rejection test. To improve the performance, a new control strategy is proposed which adds additional loops to adjust the feed ratios in a cascade manner. Under this strategy, the set-points are well tracked and disturbances are rejected properly. The third strategy examines the necessity of the ratio controllers by breaking the ratio loops. While the strategy works reasonably, the chosen manipulated variable (MV)−CV pairings are counterintuitive. In the load change test, one of MVs is reduced to the lower limit and CVs fluctuate significantly. academic control community, first in 1997 and again in 2002.2,3 This process involves several challenging issues including high order, strong nonlinearity, strong interactions, and stringent constraints. The second challenge provided a baseline control strategy based on decentralized PI loops, which did not satisfy the given limits in their disturbance rejection tests. In response, many strategies based on advanced control techniques were proposed, such as predictive control,4 multiobjective optimal PI control,5 etc. However, the ALSTOM gasifier is a fluidized-bed type, which is not common in IGCC power plants. The entrained-flow type, which is operated at a high temperature for continuous slag removal and high carbon conversion, is the representative gasifier type for IGCC. Shell and GE are the major players of this technology. The GE gasifier is a slurry feeding type, and the Shell gasifier is a dry feeding type. The Shell gasifier has high efficiency and relatively lower maintenance cost due to the robustness of the membrane wall although these come at higher capital cost.6 Currently, no published work exists on control of an entrained-flow gasifier, except for a PhD dissertation7 and a DOE report.8 The dissertation is about applying the model predictive control (MPC) strategy to an IGCC power plant with the GE slurry type gasifier. In the dissertation, the pseudofuel, composed of high molecular weight hydrocarbons, is used instead of coal because the handling of coal and ash is not straightforward in the used simulation software of AspenPlus/AspenDynamics. Therefore, it tested disturbances in coal quality as only small elemental composition changes of sulfur, carbon, and hydrogen, which does not fully reflect the real situation. The DOE report is also based on the slurry feeding GE gasifier. The focus of this work was an advanced control solution of MPC combined with

1. INTRODUCTION The integrated gasification combined cycle (IGCC) is being touted as a cleaner and more efficient way to use coal for electricity generation. IGCC combines a gasification system with a combined cycle system to generate power. The gasifier converts coal to synthesis gas, which is supplied to gas turbines in the combined cycle system. This combination, which is coal based, can have a higher thermal efficiency than a traditional pulverized coal (PC) power plant. Also, the IGCC process allows us to treat pollutants before combustion which is especially effective capturing in CO2. In addition, the gasifier also allows us to use other solid fuels such as refinery residues and biomass. Though it has many advantages, IGCC faces several challenges such as lower availability, lower acceptance, and higher capital costs compared to the pulverized coal plants and the current difficulty for financing brought by the emergence of shale gas and various renewables.1 The low availability mainly stems from the combination of the two technologies, gasification and combined cycle, which had been developed and used separately. For instance, a standalone gasification plant is normally operated at a steady state with only occasional load changes and scheduled maintenance outages. However, the power plant environment demands much more frequent load changes as well as rapid start-ups and shut-downs in order to follow time-varying load demands. It is generally agreed that the key to improving the availability lies in trouble-free operation of the gasifier as it supplies the feed to the gas turbine and therefore its dynamic behavior influences the downstream processes and, in turn, the entire plant. While tight control of the gasifier is an important missing link, most research works on modeling and simulation have been geared toward improvement of gasifier design rather than its operation and control. Studies on gasifier control are scarce, and the existing ones are mostly about the ALSTOM benchmark challenge problem, which is a simulation based problem posed by ALSTOM Power Technology to the UK © 2015 American Chemical Society

Received: Revised: Accepted: Published: 11113

May 15, October October October

2015 13, 2015 15, 2015 15, 2015 DOI: 10.1021/acs.iecr.5b01797 Ind. Eng. Chem. Res. 2015, 54, 11113−11125

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Industrial & Engineering Chemistry Research

Figure 1. Schematic representation of the gasifier.

the extended Kalman filter (EKF). However, the public report presents only the simulation results without any details about the controller design. This paper reports our findings on the control of the dry feeding type entrained-flow gasifier. For the study, a simple dynamic simulation model has been built and simulated using the software of MATLAB. Details of the dynamic simulation model are presented in Section 2. The main problem of gasifier control is addressed in Section 3. Three different control strategies are compared. We point out some inherent disadvantages of the current control practice, which is based on maintaining fixed ratios among the feed streams. In the second strategy, we propose to add additional control loops, cascaded to the existing ratio loops (without breaking them). Overall, the problem is viewed as a 3 × 3 multivariable system. Inputs and outputs are paired, and three PI controllers are designed on the basis of the relative gain array (RGA) analysis and the IMC tuning rules. Both disturbance and load change scenarios are tested. The simulation results shown in Section 4 clearly demonstrated the improved performance with the addition of these loops. As the third strategy, we break the ratio loops and then add three PI loops that manipulate the feed flows directly. This is to examine the potential benefits of the ratio loops. It turns out that the manipulated variable (MV)− controlled variable (CV) pairings chosen here are counterintuitive as the syngas mass flow (main product) rate is adjusted by the oxygen flow (auxiliary feed) rate rather than the coal flow (main feed) rate. In addition, the closed-loop performance, while reasonable, showed some fluctuations in the CVs due to the input saturation. Overall, the cascade control strategy turns out to deliver the best performance. The paper concludes with some remarks in Section 5.

In this study, the dry feeding entrained-flow gasifier, the “Shell gasifier” is considered. Dried pulverized coal along with oxygen, steam, and flux are introduced at the bottom of the gasifier. Nitrogen is used as a carrier for the coal. Oxygen is used as an oxidant leading to exothermic combustion reactions; steam is used as a temperature moderator because it leads to endothermic gasification reactions. Flux such as limestone is injected to decrease the ash melting point and slag viscosity. As the focus of this study is on control of the gasifier, not modeling, a simplified dynamic model is selected for the simulation. The gasifier model adopted is based on a previously published Shell gasifier model by Sun et al.18 However, some modifications as well as corrections have been made to it.19 2.1. Modified Sun’s Model of the Shell Gasifier. The introduced coal is rapidly devolatilized, and the volatiles are combusted. Remaining char also reacts with oxygen and steam. The following chemical reactions are considered (eqs 1−5). C+

1 O2 → CO 2

(1)

C + O2 → CO2

(2)

C + H 2O ↔ H 2 + CO

(3)

C + CO2 ↔ 2CO

(4)

CO + H 2O ↔ H 2 + CO2

(5)

The element material balances are shown in eqs 6−11. It is assumed that 99.5% of the coal is converted overall through the entire gasifier and oxygen of 100% purity is supplied. In addition, 90% of the sulfur is converted to H2S and the remaining 10% is converted to COS.

2. GASIFIER MODEL Significant research efforts have been devoted to modeling and simulation of gasifiers. In the early stage, Wen and Chaung9 and Govind and Shah10 developed mathematical models for a slurry feeding type GE gasifier. Also, Ni and Williams11 developed a model for a dry feeding type Shell gasifier. Recently, computational fluid dynamics12−15 and highly detailed submodels for the various physical and chemical phenomena have been employed for more accurate simulations. In turn, a reduced order model with less computational load was proposed by Monaghan and Ghoniem16 and Gazzani et al.17

2n N2 = nN

(6)

n H2S = 0.9nS

(7)

nCOS = 0.1nS

(8)

nCO + nCO2 = 0.995nC − nCOS

(9)

n H2 + n H2O = 0.5nH − n H2S

(10)

nCO + 2nCO2 + n H 2O = nO − nCOS

(11)

where nx denotes the molar amount of x specie. 11114

DOI: 10.1021/acs.iecr.5b01797 Ind. Eng. Chem. Res. 2015, 54, 11113−11125

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Industrial & Engineering Chemistry Research ⎛ dTf̅ dδf ⎞ ⎟⎟ = πDH(qg − ql) − minhin πDH ⎜⎜ρf δf Cpf + ρf hf̅ dt dt ⎠ ⎝

The number of moles of C, H, N, S, and O are calculated on the basis of the input streams (i.e., coal, oxygen, steam, and nitrogen) and the rest are the contents of the syngas and are calculated on the basis of eqs 6−12. Besides these balances, an equilibrium condition is assumed for the water gas shift reaction according to the following equilibrium constant relationship: Ke =

nCO2n H2 nCOn H2O

⎛ 4019 ⎞⎟ = exp⎜⎜ −3.689 + Tg ⎟⎠ ⎝

− mex hex + πDH Φmhm m qg − qf + Cpf ⎡⎣ Ain (Tg − Tf̅ ) + ϕm(Tm − Tf̅ )⎤⎦ dT0 = 1 dt ρf Cpf δf 2

(21) (12)

Tf̅ =

The heat of reaction is as in eq 13. 2 0 ΔH = − qg A = ΔHrxn +

Tg

∑∫ j=1

Tref

5

njCpj dT −

∑∫ i=1

Tini

Tref

where = = HHVsyngas − HHVcoal. j indicates products such as syngas and slag, and i indicates reactants such as coal, steam, oxygen, flux, and nitrogen (Figure 1). The slag layer is composed of a fluid slag layer and a solid slag layer. 70% of the coal ash is assumed to be attached to the fluid slag layer which is either solidified or flowed down to be discharged through a slag tap (see eq 31). The fluid slag layer thickness is calculated on the basis of the material balance below.

Nu =

Φm = −ρs

(15)

Ts̅ =

⎞⎤ αx ⎛ ρf gδf 2 ⎡ ⎛ 1 ⎢e α⎜ − 1 ⎞⎟ − e δf ⎜ x − 1 ⎟⎥ v (x ) = ⎜ ⎟ η(0) ⎢⎣ ⎝ α α2 ⎠ α 2 ⎠⎥⎦ ⎝ αδf

(18)

∫0

δf

πDδf v(x) dx =

dδs dt

(26)

dTs̅ dδ + ρs hs̅ s = qf − qs − Φmhm dt dt

Tw + Tm 2

qs = λs

(27)

(28)

Tm − Tw δs

(29) 1

qf − qs − 2 ρδ C dδs s s ps = (T − T ) dt ρs Cps w 2 m

(16)

(17)

(25)

The thickness of the solid slag layer in eq 26 can be rearranged to eq 30 by substituting eq 26 into eq 27 that describes the energy balance.

Viscosity is assumed to vary along the horizontal direction x according to eq 16. Substituting eq 16 into eq 15 and integrating it using the boundary condition of eq 17, the velocity and exiting mass flow rate become that as shown in eqs 18 and 19, respectively.

mex =

(ξ)

2

(14)

⎫ dv η = τ , when x = 0 ⎪ dx ⎬ v = 0, when x = δf ⎪ ⎭

(24)

(Re − 1000)Pr ⎡ αsD ⎛ D ⎞2/3⎤ 2 ⎢1 + ⎜ ⎟ ⎥ = ⎝ H ⎠ ⎥⎦ λf 1 + 12.7 ξ (Pr 2/3 − 1) ⎢⎣

ρδ C s s ps

⎛ η(δf ) ⎞ ⎟⎟ where α = −ln⎜⎜ ⎝ η(0) ⎠

(23)

where ξ = (1.58 ln(Re) − 3.28)−2

Assuming that the fluid slag behaves as a Newtonian fluid and ignoring the acceleration term, the Navier−Stokes equation becomes

⎛ αx ⎞ η(x) = η(0)exp⎜⎜ − ⎟⎟ , ⎝ δf ⎠

T0 − Tm δf

Heat flux from gas to the fluid slag, qg, is the sum of radiative heat and convective heat. The convective heat transfer coefficient, αs, is based on the Gnielinski correlation (eq 25):

dδf

d ⎛ ⎛⎜ dv ⎞⎟⎞ ⎜η ⎟ = −ρf g d x ⎝ ⎝ d x ⎠⎠

(22)

qg = σεs(Tg 4 − T0 4) + αs(Tg − T0)

−ΔH0c

m − mex ⎞ 1⎛ ⎟ = ⎜Φm + in ρf ⎝ πDH ⎠ dt

T0 + Tm 2

qf = λ f

niCpi dT (13)

ΔH0rxn

(20)

dTw dt

(30)

The gasifier dynamic simulation model requires inputs of coal, oxygen, steam, flux, and nitrogen mass flow rate, temperature and pressure of the input streams, and the coolant temperature. Outputs of the gasifier model include syngas mass flow rate and composition, temperature, and slag thickness. The gasifier model is represented as a semiexplicit index-1 DAE system. Therefore, the DAE can be solved using the ODE solver ode15s in MATLAB. The dependent variables, independent variables, and parameters of the model are listed in Tables S1 and S2, respectively. Overall, the model consists of 3 differential equations and 17 algebraic equations. For the dynamic simulation, the number of equations is reduced to 8 (3 differential equations and 5 algebraic equations), by substitution and simplification. 2.2. Physical Properties of Coal and Ash. The coal of El Cerrejón is assumed to be used in this study; the coal and ash are listed on Tables 1 and 2,20 respectively. The limestone compositions21 are listed in Table S3.

πDρf 2 gδf 3 ⎡ ⎛ 1 2 2⎞ 2⎤ α ⎢e ⎜⎝ − 2 + 3 ⎟⎠ − 3 ⎥ ⎣ η(0) α α α α ⎦

(19)

Energy balance is shown in eq 20. Assuming that the heat capacity of the fluid slag is constant and substituting eqs 22 and 14 into eq 20, one obtains eq 21 for the surface temperature of the fluid slag layer. 11115


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Industrial & Engineering Chemistry Research Table 1. Coal Compositiona

a

ρ=

coal analysis (wt %, AR)

nominal (El Cerrejón B)

disturbance 1 (El Cerrejón A)

disturbance 2 (El Cerrejón C)

moisture ash carbon hydrogen nitrogen sulfur oxygen HHV [kJ/kg]

11.5 7.2 66.6 4.6 1.27 0.74 8.06 27 444

11.1 5.9 68 4.6 1.26 0.63 8.47 28 156

11.5 8.7 65.7 4.5 1.23 0.78 7.56 26 983

Cp = x1Cp̅ 1 + x 2Cp̅ 2 + x3Cp̅ 3 + ... Cpi̅ = a + bT −

a

disturbance 1 (El Cerrejón A)

disturbance 2 (El Cerrejón C)

SiO2 Al2O3 Fe2O3 CaO MgO Na2O K2O TiO2 SO3 P2O5

61.3 19 8.7 3.1 1.6 0.5 1.9 0.95 2.4 0.16

60.8 19.1 8.9 2.9 1.6 0.5 1.9 0.92 2.8 0.16

61 19.5 8.66 3 1.7 0.5 1.8 0.95 2.2 0.17

λs = αCpsρs

In the model, the slag is a mixture of coal ash and flux. In the control study, coal and flux (limestone) mass flow rates are the manipulated variables. Therefore, the slag composition is not constant and its physical properties should be expressed as a function of the composition. min = 0.7mcoal ash + mflux (31) Melting transition temperature and viscosity are the most important physical properties which have a huge impact on the dynamics of slag. We assume that the melting transition temperature, Tm, is the temperature of critical viscosity Tcv which is calculated on the basis of the acid/base ratio of the slag composition.22 SiO2 + Al 2O3 + TiO2 Fe2O3 + CaO + MgO + Na 2O + K 2O

Tcv = 1385.44 + 74.1AB

(32) (33)

Viscosity is expressed as a function of the composition and temperature. S is the silica ratio defined as in eq 34. S=

SiO2 SiO2 + Fe2O3 + CaO + MgO

⎛ 104 ⎞ ⎟⎟ − 7.44 log(η) = 4.468S2 + 1.265⎜⎜ ⎝ Tf̅ ⎠

(37)

(38)

3. GASIFIER CONTROL 3.1. Control Problem Definition. The syngas mass flow rate, H2/CO ratio, and gasifier temperature are selected as the controlled variables, and coal mass flow rate, oxygen, and steam flow rate are selected as the manipulated variables. The gasifier temperature should be controlled for a desired degree of carbon conversion, slagging, and safety. The temperature of generated syngas is around 1500 °C, and it is cooled down to around 900 °C by quenching at the gasifier outlet to protect downstream equipment and piping.6 The recycled syngas is used for the quench, and the quenched syngas is cooled again in a syngas cooler. The Shell gasifier also uses the membrane wall which protects the gasifier wall with the solid slag layer and recovers the heat through steam generation. The quench gas directly impacts the latter unit (i.e., syngas cooler) rather than inside of the gasifier, and the cooling by the membrane wall is indirectly related to the chemical reaction. In this study, we focused on the control of the temperature which is directly related to the chemical reaction. Therefore, the quench was not considered, and a constant cooling temperature was assumed. Thus, the syngas temperature before the quench is selected as the controlled variable, and the permitted fluctuation limit is selected as ±10K. Among several components of the syngas, hydrogen and carbon monoxide are the main components. The ratio between two components, the H2/CO ratio, should be maintained at a reference value for a trouble-free operation of gas turbine. The high temperature of hydrogen combustion causes the high NOx emission, preignition, and flashback problems, in turn; the H2/CO ratio fluctuation can increases the instability in gas turbine operation.26 In this study, we choose the tight control limit to be ±0.01 because there is no known rule-of-thumb to figure out the acceptable range of the H2/CO ratio. Also, the syngas flow rate must be controlled properly according to the load change of the gas turbine. In transient conditions, the limit violations are unavoidable because the limits are tightly selected for the steady state operation. Alternatively, it is necessary to minimize the integrated error during the transience. In addition, nitrogen to coal ratio is fixed because the nitrogen is used only as carrying gas. The flux to coal ratio is also fixed because the slag thickness (or slag viscosity) is not measured in an online manner. Therefore, a 3 × 3 multi-input multioutput (MIMO) control problem is constructed (Figure 2). The limits of input variables are listed in Table 3, and the control requirements are in Table 4.

Scenario 1: coal spec step change.

AB =

c T2

Thermal conductivity of the solid slag is derived as in eq 38 with thermal diffusivity α being 4.5 × 10−7 m2/s. Thermal conductivity of the fluid slag is 1 W/mK.25 The slag emissivity, εs, is 0.83.25

Table 2. Ash Compositiona nominal (El Cerrejón B)

(36)

Heat capacity of the slag is based on the summation of partial molar quantities of the individual components.24

Scenario 1: coal spec step change.

mineral ash analysis (wt %, of total ash)

∑ xiMi (1 + 0.0001(T − 1773)) ∑ xiVi̅

(34)

where η in poises (35)

This serves as the value for η (0) and η (δf) in eq 16. Mills and Rhine proposed the slag density model which is based on the constituent molar volume and structure of the silicate slag.23 The temperature coefficient was assumed to have a value of 0.01% K−1. The partial molar volumes of slag constituents are presented in Table S4. 11116


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this control study. For the simulation, the nonlinear plant model given by the DAEs in Section 2 was used to get the plant responses. We also checked the open-loop step responses of the plant under various magnitudes of input changes and they exhibited some mildly nonlinear behavior (see the open-loop step response plots included in Supporting Information). Even though the system is nonlinear, it could be adequately controlled by linear PI controllers. It means that the nonlinearity of the system under closed loop is sufficiently mild to be controlled well by linear controllers. Additionally, input streams are assumed to be supplied instantaneously (without lags). In other words, the time constants and constraints on the ramp rate for the MVs were not included. The focus of this study is the control strategy for the gasifier rather than the combined operation of the gasifier, ASU, and/or boiler. Thus, we did not consider the effect of the dynamics of the connected processes. For minimum and maximum flow rates of the coal and oxygen, 40% and 115% of the nominal values were assumed, respectively. As for the steam flow rate, because the boiler drum size is thought to be sufficiently large, we assumed a wider range. We note that the assumptions regarding the process time constants and the limits of the MVs should be rechecked when extending this study to multiobjective optimization or plant-wide control studies for the combined operation of the gasifier and the connected equipment. The time constants as well as the ramp rate and min−max limits of the MVs can affect the control performance significantly. 3.2. Control Strategy 1 (CS1): Fixed-Ratio Control. A commonly used control strategy in industry is based on maintaining fixed (oxygen-to-coal, steam-to-coal) ratios among the feed component.7,27 The supervisory control is based on the operator’s experience and insights.8,28 Basically, the coal mass flow rate is determined by the syngas demand, which in turn is based on the electrical load of the gas turbine. Then, oxygen and steam are supplied at prefixed ratios to the coal mass flow rate but they can be fine adjusted by the operator, if needed (Figure 3). The ratio values are usually determined on the basis of prior experience of the steady-state operation.27 In a transient condition such as a load change, the operator typically follows a predetermined procedure. Therefore, the fixed-ratio control strategy can move the system along a highly suboptimal path during transience.

Figure 2. Block diagram of the 3 × 3 gasifier control system.

Table 3. Manipulated Variable Limits variable limits

unit

nominal value

min

max

coal flow rate (u1) oxygen flow rate (u2) (oxygen to coal ratio) steam flow rate (u3) (steam to coal ratio)

[kg/s] [kg/s]

22.2 19.1 (0.86036) 1.528 (0.06882)

8.88 7.64

25.5 21.2

[kg/s]

0

8

Table 4. Control Requirements variable limits

unit

syngas flow rate (y1) syngas temperature (y2) syngas H2/CO ratio (y3)

[kg/s] [K]

nominal value 43.73 1797 0.465

% ±0.1 [kg/s] ±10 [K] ±0.01

0.23 0.55 2.15

The Alstom benchmark challenge is a 4 × 4 system of which the CVs are the bed-mass, syngas calorific value, syngas pressure (PGAS), and syngas temperature and the MVs are the char extraction rate, air flow rate, coal flow rate, and steam flow rate.2,3 The configuration and dynamics of the Alstom gasifier are quite different with the Shell gasifier because the Alstom gasifier is a fluidized-bed type gasifier. Due to the nature of a fluidized-bed reactor, maintaining a constant pressure is important but an abrupt pressure drop occurs when the sink valve is opened. Therefore, the Alstom benchmark challenge considered the PGAS as a CV and PSINK as an unmeasured disturbance. Al Seyab and Cao reported the PGAS response exhibits significant nonlinearities.4 However, the Shell gasifier is an entrained flow type gasifier, so the PGAS is not a CV and such strong nonlinearities are not observed for the system in

Figure 3. Block diagram of Control Strategy 1. 11117


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Figure 4. Block diagram of Control Strategy 2.

disturbance control method. Thus, the coal-to-oxygen and steam-to-oxygen ratios are only roughly controlled. However, they did not provide a rationale for the chosen control structure even though the pairings are counterintuitive (oxygen flow rate and syngas mass flow rate). In addition, disturbance rejection tests were not run. Furthermore, there is a myth regarding the ratio control practice. Traditionally, the ratio control strategy was popular in combustion processes. The air(or oxygen)-to-fuel ratio directly affects the efficiency and environmental emissions. An air-tofuel ratio higher than the stoichiometric ratio wastes fuel by heating the excess air. A lower ratio, on the other hand, leads to an incomplete combustion and increases pollutant generation. Theoretically, the process achieves a maximum efficiency when air is provided at a mass flow rate corresponding to the stoichiometric ratio. However, in a combustion process, it is typical to supply 5−20% excess air in consideration of imperfect mixing and to avoid an incomplete combustion. Due to this practice, operators can mistakenly think that excess oxygen supply is also safe and conservative for the gasifier control. This can be seen in some literature stating that more oxygen than the optimum value is supplied to ensure near complete conversion to the syngas.30−32 However, unlike the combustion process, in the gasification process, excess oxygen does not increase the probability of complete conversion to syngas. Excessive oxygen supply leads to the reaction of full oxidation (CO2) rather than partial oxidation (CO) and also converts carbon monoxide (CO) to carbon dioxide (CO2) as below:

For the slurry feeding type GE gasifier, oxygen-to-coal ratio and water-to-coal ratio are usually fixed. In this case, it is typical to adjust only the oxygen-to-coal ratio at the operator’s supervisory control level as a certain level of flowability must be ensured for the slurry.8 On the other hand, in the Shell type gasifier control, both ratios can be adjusted in the operator supervision.27 This gives more flexibility but also means that high-level supervision can be more complex and burdensome for the operator to handle. We note that several alternative strategies have been investigated in order to address the difficulty with the supervisory control. In a Korean patent, a kind of shooting method is proposed for choosing the fuel, oxygen, and steam flow rates.27 In order to decrease dependency on operator during operation, the supervisory controller calculates the initial values of the flow rate of the three MVs based on the previous experimental data and a gasifier numerical model. Starting with the initial values, the MVs are adjusted by the SISO control loops. The suggested pairings for SISO loops are oxygen flow rate and syngas temperature, steam flow rate and CO/H2 ratio, and fuel and syngas flow rate. If measurements show that two of three CVs are within their respective acceptable ranges, then the supervisory control system is terminated. If not, the supervisory controller calculates the MVs flow rates again. The introduction of a supervisory controller with a prediction model makes it a more advanced approach, but there, the supervisory and regulatory controllers work separately. That is, even though the supervisory controller calculates optimal MV values considering the interactions or effects of unmeasured disturbances, the regulatory controller can still move the MVs to suboptimal or inappropriate points. Because the control structure does not coordinate the regulatory controller with the supervisory controller, convergence of only two of the three CVs are considered. Shihe et al. proposed an active disturbance rejection control for the Shell gasifier.29 The selected controller structure adopts two separate controllers. An IGCC load controller determines the oxygen flow rate on the basis of the syngas flow demand, and a 2 × 2 gasifier controller adjusts the coal and steam flow rates to control the syngas temperature and heat value, respectively. An additional two proportional controllers are used to adjust the coal and steam flow rates again based on the oxygen flow rate. In the 2 × 2 gasifier controller’s point of view, the oxygen flow rate is considered as an external disturbance and therefore they suggested an active

C + O2 → CO2

C+

ΔH = −393.51kJ/mol

1 O2 → CO 2

CO +

1 O2 → CO2 2

ΔH = −110.5kJ/mol

ΔH = −283.01kJ/mol

(39)

(40)

(41)

Therefore, in the gasification process, excess oxygen decreases the yield of the product (CO) and generates more heat which has to be removed. Moreover, in the dry feeding type entrained-flow gasifier, excessive oxygen could make the system use up more steam to decease the temperature in the reaction zone. The supplied steam then changes the syngas composition because it is not only a temperature moderator but 11118


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Industrial & Engineering Chemistry Research also a reactant. Eventually, excessive oxygen supply makes the system more unpredictable. CO + H 2O ↔ H 2 + CO2

ΔH = −41.1kJ/mol

(42)

As a result, excess oxygen and steam supply does not help in controlling the temperature or the heating value of syngas. Later in Section 4, results from a load change test will be presented to check performance of the fixed-ratio control strategy in transient situations. This will serve as a baseline performance against which our proposed control strategies will be evaluated. 3.3. Control Strategy 2 (CS2): Multiloop PID Control Cascaded to the Ratio Loops. In this strategy, we propose to add a PI loop that manipulates the coal mass flow rate and two additional PI loops that manipulate the set-points of the two ratio loops in the previous fixed-ratio control strategy (Figure 3). This leads to a 3 × 3 control problem shown in Figure 4. In order to check the directionality (i.e., level of ill-conditioning) and degree of interactions, the condition number (CN) of the steady-state gain matrix was checked.33 CN was around 21, which indicates substantial interactions but is still low enough for a decentralized control (as a rule of thumb, a CN lower than 50 indicates that it may be feasible to decouple34). Input− output pairings were determined on the basis of the relative gain array (RGA) matrix, which is shown in Table 5. The RGA

Figure 5. Frequency response of the DRGA for Control Strategy 2 (solid line corresponds to chosen pairings).

temperature, (2) oxygen mass flow rate and syngas mass flow rate, and (3) steam mass flow rate and syngas H2/CO ratio. However, this pairing is physically counterintuitive. The syngas (main product) mass flow rate is adjusted by the oxygen (secondary feed) mass flow rate. In this case, adjustments in the oxygen mass flow rate can lead to changes in the syngas temperature which in turn lead to adjustment of the coal mass flow rate. The pairings are physically counterintuitive, but the steady-state RGA and dynamic RGA results indicate that the chosen pairings are the only feasible option. In Figure 7, the RGA elements of the chosen pairs are indicated by a solid line. By looking at the magnitudes of the RGA elements only, pairing the syngas mass flow rate (yellow line) with the coal flow rate (λ11) rather than with the oxygen flow rate (λ12) seems plausible, but it leads to negative elements for the rest of the pairings (λ22 and λ23 are both negative at steady state). In addition to the nonintuitiveness, this can lead to some deterioration in the control performance as we shall see in the simulation.

Table 5. RGA Matrix for Control Strategy 2

syngas mass flow rate syngas temperature syngas H2/CO ratio

coal flow rate

oxygen to coal ratio

steam to coal ratio

1.18 −0.2 0.02

−0.08 1.43 −0.35

−0.1 −0.23 1.33

matrix indicates that only one pairing was possible (in order to avoid a pairing with a negative relative gain). The chosen pairings were (1) coal mass flow rate and syngas mass flow rate, (2) oxygen-to-coal ratio and syngas temperature, and (3) steam-to-coal ratio and syngas H2/CO ratio. It makes physical sense that the syngas (main product) mass flow rate is adjusted by the coal (main feed) mass flow rate. To make sure the chosen pairing is correct not just at steady state but throughout the relevant frequency range, dynamic RGA (DRGA) was checked (Figure 5).35,36 DRGA provided the same pairing result as the steady-state RGA. As Figure 5 shows, the system remains diagonally dominant throughout the tested frequency range. The IMC tuning method was used to find initial settings for the PI loops, which were fine adjusted through simulations. An additional advantage of keeping the ratio loops (as opposed to breaking them) is that the ratio loops provide feedforward control action of sorts: The oxygen and steam mass flow rates are immediately and automatically adjusted in response to fluctuations in the coal mass flow rate. A potential downside is that it requires two more control loops to tune and maintain. 3.4. Control Strategy 3 (CS3): Multiloop PID Control with the Ratio Loops Eliminated. To check the necessity of the ratio loops, another strategy is tested that eliminates the ratio loops. In this case, the three PID loops manipulate the three feed flow rates directly (Figure 6). The RGA for the 3 × 3 system resulting under this strategy is shown in Table 6, and the DRGA result is shown in Figure 7. On the basis of the RGA matrix, chosen pairs are (1) coal mass flow rate and syngas

4. RESULTS In this section, we test and compare closed-loop performances of the three control strategies introduced in the previous section. Two scenarios were considered. Scenario 1 is a disturbance rejection test. Eighteen variables, which represent the coal specs, are changed in a step fashion (Tables 1 and 2). During the gasifier operation, coal composition can vary slightly from its design value due to some inherent variability in the natural feedstock. Variations in the coal specs act as unmeasured disturbances to the system, which must be compensated for. In this study, grade B of El Cerrejón coal is selected as the design coal. We assumed the 18 variables of the coal specs can vary between the range of grade A and grade C coals. Thus, two types of disturbances are tested in Scenario 1: coal spec step change from grade B to grade A (Disturbance 1) and that from grade B to grade C (Disturbance 2). The second scenario is the set-point change test. In this study, the load of the gasifier is changed from 100% down to 50% and then back up to 100%, with a rate of 5% per minute.37 4.1. Open-Loop Response. 4.1.1. Scenario 1: The Disturbance Rejection Test. Open-loop responses of Scenario 1, where coal/ash compositions change from those of grade B 11119


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Figure 6. Block diagram of Control Strategy 3.

Table 6. RGA Matrix for Control Strategy 3 syngas mass flow rate syngas temperature syngas H2/CO ratio

coal flow rate

oxygen flow rate

steam flow rate

0.67 1.32 −0.99

0.44 −0.08 0.64

−0.11 −0.24 1.35

Figure 8. Open-loop responses for Scenario 1. Figure 7. Frequency response of the DRGA for Control Strategy 3 (solid line corresponds to chosen pairings).

4.2.2. Scenario 2: The Set-Point Tracking Test. Closed-loop responses under the described set-point changes in the syngas mass flow rate are shown in Figure 10. The syngas mass flow rate approximately tracks the set-point changes; however, transient errors in the syngas temperature and H2/CO ratio are significant, and the upset in the gasifier can lead to further problems downstream. Integral squared errors (ISEs) during transience are calculated and plotted in Figure 15. 4.3. Control Strategy 2 (CS2): Multiloop PID Control Cascaded to the Ratio Loops. 4.3.1. Scenario 1: The Disturbance Rejection Test. Closed-loop responses under the same disturbance changes are shown in Figure 11. The oxygen to coal ratio and steam to coal ratios are continuously adjusted by the outer-loop controllers under this strategy. Closed-loop performance is much improved as all the CVs converge to their set-points without offset.

to grade A (Disturbance 1) and grade C (Disturbance 2), are shown in Figure 8. The colored regions correspond to limit violation according to the control requirements we set forth earlier. The unmeasured disturbance in the coal spec is introduced to the system at the 2 min mark. The gasifier system is open-loop stable, but it takes a long time to settle down to a new steady state, which lies outside the feasible regions. Therefore, active control is necessary to satisfy the performance requirement. 4.2. Control Strategy 1 (CS1): Fixed-Ratio Control. 4.2.1. Scenario 1: The Disturbance Rejection Test. Closedloop responses under the disturbance changes are plotted in Figure 9. Under the fixed-ratio control strategy, only the syngas mass flow rate converges back to the original steady state. The syngas temperature clearly violates the limit. 11120


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Figure 9. Closed-loop responses under CS1 for Scenario 1 of disturbance changes (left: MVs; right: CVs).

Figure 10. Closed-loop responses under CS1 for Scenario 2 of load changes (left: MVs; right: CVs).

4.3.2. Scenario 2. The closed-loop responses under the same load changes in the syngas mass flow rate are plotted in Figure 12. The ISE values plotted in Figure 15 indicate that this control strategy leads to a minimum ISE. 4.4. Control Strategy 3 (CS3): Multiloop PID Control with the Ratio Loops Eliminated. 4.4.1. Scenario 1: The Disturbance Rejection Test. Under CS3 where the ratio loops are removed, all the CVs also converge to their set-points after about 50 min. The performance is slightly worse but nevertheless comparable to that of CS2 for the disturbance change scenario (Figure 13). 4.4.2. Scenario 2: The Set-Point Tracking Test. In this case, in response to the set-point change of the syngas mass flow rate, oxygen mass flow rate is first changed. The oxygen mass flow rate change then perturbs the syngas temperature, which in turn leads to adjustments in the coal mass flow rate. This

control sequence is rather awkward and certainly not intuitive, but the control is nevertheless maintained (Figure 14). In the syngas H2/CO ratio control loop using the steam mass flow rate, the MV hits the low limit twice at the time marks of 14 and 18 min; this makes the CVs fluctuate a bit. In the other strategies, we did not observe such a problem. In terms of ISEs (plotted in Figure 15), we can see that the closed-loop errors are slightly larger than those under CS2. We may conclude that the ratio loops are better kept (as in CS2), both from the points of intuitive operation and closed-loop performance.

5. CONCLUSIONS Three control strategies for the dry feeding type entrained-flow gasifier were studied. The conventional gasifier control practice (CS1) is to fix the ratios of oxygen-to-coal and steam-to-coal 11121


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blown multivariable control strategy like MPC) was judged to be sufficient. The resulting 3 × 3 MIMO systems were analyzed, and the pairings were decided using the relative gain array (RGA). In each option, the three PI controllers were designed using the IMC tuning method and were fine-tuned through simulation. It was demonstrated in simulation that all the CVs were controlled satisfactorily to their set-points by both CS1 and CS2, during disturbances in the coal spec as well as load changes in the syngas mass flow rate. However, under CS3, some lower limit MV violations leading to fluctuations in the CVs were observed during the load transition test. In addition, CS3 led to input−output pairings that were counterintuitive, leading to an awkward sequence of control actions during load changes. Furthermore, the ratio controllers, when kept as in CS2, act to give feedforward actions so the

during operation (with some additional heuristic adjustments by the operator at the supervisory level). It was argued (and later demonstrated through simulation) that this strategy is not sufficient to control the gasifier satisfactorily in the face of expected disturbances and load changes, and additional control loops needed to be installed. We explored the option of cascading additional PI control loops to the existing ratio loops (CS2) (to control the CVs) and the alternative of eliminating the ratio loops and then manipulating the individual flow rates directly (CS3). Both options resulted in a 3 × 3 control problem where MVs are coal, oxygen, and steam flow rates and CVs are syngas flow rate, syngas temperature, and H2/CO ratio. In both cases, the condition number of the 3 × 3 system was low enough that the decentralized multiloop control strategy (rather than a full11122


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oxygen and steam flow rates are changed stably and predictably according to changes in the coal flow rate. Therefore, we may conclude that the cascade control strategy of CS2 should be the best option (among the three strategies considered) for the gasifier control. In conclusion, the common practice of fixing the input ratios can be improved by adding cascade loops to adjust the ratios based on feedbacks of the important CVs. Additionally, the ratio control strategy has advantages to control the multi-input streams of the gasifier, which brings feedforward effect in set-point changes. Another important aspect of the gasifier control is the control of the slack layer, especially its thickness, as its behavior can have a major impact on the stability and therefore availability of the gasifier. A major obstacle to controlling the slack layer thickness is that it cannot be easily measured online. Nevertheless, slack thickness dynamics can be modeled (as

Figure 15. Integral squared errors (ISEs) in the CVs during transience of Scenario 2 of load changes. 11123


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Industrial & Engineering Chemistry Research has been in our model) and estimated using other online measurements, resulting in opportunities to monitor and inferentially control it. This is our current focus. Additionally, this study can be developed further to address the problems of multiobjective optimization or plant-wide control of the IGCC power plant. To do that, the dynamics of the processes supplying the input streams of the gasifier such as ASU and boiler should be added.

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REFERENCES

(1) Barnes, I. Recent Operating Experience and Improvement of Commercial IGCC; IEA: London, 2013. (2) Dixon, R.; Pike, a W.; Donne, M. S. The ALSTOM Benchmark Challenge on Gasifier Control. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 2000, 214 (6), 389−394. (3) Dixon, R.; Pike, A. Introduction to the 2nd ALSTOM Benchmark Challenge on Gasifier ControlIn Proceedings of Control, University of Bath, UK, 2004; pp 585−601. (4) Al Seyab, R. K.; Cao, Y. Nonlinear Model Predictive Control for the ALSTOM Gasifier. J. Process Control 2006, 16 (8), 795−808. (5) Xue, Y.; Li, D.; Gao, F. Multi-Objective Optimization and Selection for the PI Control of ALSTOM Gasifier Problem. Control Eng. Pract. 2010, 18 (1), 67−76. (6) Maurstad, O. An Overview of Coal Based Integrated Gasification Combined Cycle (IGCC) Technology; Publication No. LFEE 2005-002 WP; MIT: Cambridge, MA, 2005. (7) Mahapatra, P. Model Predictive Control of Integrated Gasification Combined Cycle (IGCC) Power Plants. PhD Thesis, Rensselaer Polytechnic Institute, Troy, New York, 2010. (8) Kumar, A. Integrated Sensing and Controls for Coal Gasification − Development of Model-Based Controls for GE’s Gasifier and Syngas Cooler Final Technical Report; DOE: United States; 2010, DOI: 10.2172/1038536. (9) Wen, C. Y.; Chaung, T. Z. Entrainment Coal Gasification Modeling. Ind. Eng. Chem. Process Des. Dev. 1979, 18 (4), 684−695. (10) Govind, R.; Shah, J. Modeling and Simulation of an Entrained Flow Coal Gasifier. AIChE J. 1984, 30 (1), 79−92. (11) Ni, Q.; Williams, A. A Simulation Study on the Performance of an Entrained-Flow Coal Gasifier. Fuel 1995, 74, 102−110. (12) Bockelie, M. J.; Denison, M. K.; Chen, Z.; Senior, C. L.; Linjewile, T.; Sarofim, A. F. CFD Modeling for Entrained Flow Gasifiers in Vision 21 Systems. In Gasification Technologies Conference, 2002; pp 1−42. (13) Zitney, S. E.; Guenther, C. Gasification CFD Modeling for Advanced Power Plant Simulations. In 22nd Annual International Pittsburgh Coal Conference, University of Pittsburgh School of Engineering, Pittsburgh, PA, Sept. 12−15, 2005. (14) Jeong, H. J.; Seo, D. K.; Hwang, J. CFD Modeling for Coal Size Effect on Coal Gasification in a Two-Stage Commercial Entrained-Bed Gasifier with an Improved Char Gasification Model. Appl. Energy 2014, 123, 29−36. (15) Silaen, A.; Wang, T. Effect of Turbulence and Devolatilization Models on Coal Gasification Simulation in an Entrained-Flow Gasifier. Int. J. Heat Mass Transfer 2010, 53 (9−10), 2074−2091. (16) Monaghan, R. F. D.; Ghoniem, A. F. A Dynamic Reduced Order Model for Simulating Entrained Flow Gasifiers. Fuel 2012, 91, 61−80. (17) Gazzani, M.; Manzolini, G.; MacChi, E.; Ghoniem, A. F. Reduced Order Modeling of the Shell-Prenflo Entrained Flow Gasifier. Fuel 2013, 104, 822−837. (18) Sun, B.; Liu, Y.; Chen, X.; Zhou, Q.; Su, M. Dynamic Modeling and Simulation of Shell Gasifier in IGCC. Fuel Process. Technol. 2011, 92 (8), 1418−1425. (19) Lee, H.; Lee, J. H. Comments on “Dynamic Modeling and Simulation of Shell Gasifier in IGCC. Fuel Process. Technol. 2015, 129, 75. (20) Columbia steam coal quality analysis, China Coal Resource; http://en.sxcoal.com/574/DataShow.html (accessed Aug 28, 2015). (21) Bartone, L. M., Jr.; White, J. Industrial Size Gasification for Syngas, Substitute Natural Gas and Power Production; DOE/NETL401/040607; DOE/NETL: Pittsburgh, PA, 2007.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.5b01797. Table S1, dependent variable list; Table S2, independent variable and parameters list; Table S3, limestone composition; Table S4, recommended values for the partial molar volumes of slag constituents at 1773 K; Figures S1−S6, open-loop response of the plant (PDF)

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αs = convective heat transfer coefficient, kW/m2K D = diameter, m H = height, m A = area, m2 g = gravitational acceleration, m/s2 σ = Stefan−Boltzmann constant, kW/m2K4

AUTHOR INFORMATION

Corresponding Author

*Tel.: +82-42-350-3926. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Advanced Biomass R&D Center (ABC) of Global Frontier Project funded by the Ministry of Science, ICT, and Future Planning (ABC-20110031354).

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NOMENCLATURE Ke = equilibrium constant ΔHc = heat of reaction, kJ/s δf = fluid slag thickness, m δs = solid slag thickness, m Φm = melting mass flux per unit area, kg/m2s min = deposited slag mass flow rate, kg/s mex = exiting slag mass flow rate, kg/s mcoal ash = mass flow rate of coal ash, kg/s mflux = mass flow rate of flux, kg/s Tg = syngas temperature, K Tin = input stream temperature, K Tw = membrane wall temperature, K Tm = melting transition temperature, K Tcv = critical viscosity temperature, K T0 = fluid slag layer surface temperature, K T̅ f = average temperature of fluid slag, K T̅ s = average temperature of solid slag, K qg = heat flux from gas to fluid slag layer, kW/m2 qf = heat flux across fluid slag layer, kW/m2 qs = heat flux across solid slag layer, kW/m2 ρf = density of fluid slag, kg/m3 ρs = density of solid slag, kg/m3 Cpf = heat capacity of fluid slag, kJ/kgK Cps = heat capacity of solid slag, kJ/kgK Cpj = heat capacity of product, kJ/kgK Cpi = heat capacity of reactant, kJ/kgK η = viscosity, kPas v = longitudinal velocity of fluid slag, m/s λ = conductivity, kW/mK εs = slag emissivity 11124


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Industrial & Engineering Chemistry Research (22) Massoudi, M.; Wang, P. Slag Behavior in Gasifiers. Part II: Constitutive Modeling of Slag. Energies 2013, 6, 807−838. (23) Mills, K. C.; Rhine, J. M. The Measurement and Estimation of the Physical Properties of Slags Formed during Coal Gasification: 1. Properties Relevant to Fluid Flow. Fuel 1989, 68, 193−200. (24) Mills, K. C.; Keene, B. J. Physical Properties of BOS Slags. Int. Mater. Rev. 1987, 32, 1−120. (25) Mills, K. C.; Rhine, J. M. The Measurement and Estimation of the Physical Properties of Slags Formed during Coal Gasification: 2. Properties Relevant to Heat Transfer. Fuel 1989, 68, 904−910. (26) Walton, S. M.; He, X.; Zigler, B. T.; Wooldridge, M. S. An Experimental Investigation of the Ignition Properties of Hydrogen and Carbon Monoxide Mixtures for Syngas Turbine Applications. Proc. Combust. Inst. 2007, 31, 3147−3154. (27) Yoo, J. S.; Kim, Y. S. System for Controlling Operation of Gasification and Control Method. Korean Patent 10-2007-0119377, May 26 2009. (28) Kim, B. K.; Yoo, J. S.; Paek, M. S. Automatic Control System for the Slagging Gasifier. Korean Patent 10-2009-0110069, May 24 2011. (29) Shihe, C.; Jia, L.; Le, W.; Yuqiong, Z.; Donghai, L.; Yali, X. Active Disturbance Rejection Control of Shell Gasifier in IGCC Power Plants 2014, 7 (6), 285−296. (30) Wang, P.; Massoudi, M. Slag Behavior in Gasifiers. Part I: Influence of Coal Properties and Gasification Conditions. Energies 2013, 6 (2), 784−806. (31) Phillips, J. Different Types of Gasifiers and Their Integration with Gas Turbines; EPRI: Charlotte, NC, 2006. (32) Kim, D. S. 300MW Taean IGCC Demonstration Plant Construction Technology Development. In Green Energy International Business Conference, Daegu, Korea, 6 April, 2011. (33) Seborg, D. E.; Edgar, T. F.; Mellichamp, D. A. Process Dynamics and Control; Wiley: New York, 1989. (34) Svrcek, W. Y.; Mahoney, D. P.; Young, B. R. A Real-Time Approach to Process Control, 2nd ed.; Wiley: New York, 2006. (35) Mc Avoy, T.; Arkun, Y.; Chen, R.; Robinson, D.; Schnelle, P. D. A New Approach to Defining a Dynamic Relative Gain. Control Eng. Pract. 2003, 11 (8), 907−914. (36) Khaki-Sedigh, A.; Moaveni, B. Control Configuration Selection for Multivariable Plants; Springer-Verlag: Berlin; Heidelberg, 2009. (37) IEAGHG. Operating Flexibility of Power Plants with CCS; IEAGHG: Gloucestershire, UK, June 2012.

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