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Model predictive control for BioPower combined heat and power (CHP) plant
I.A. Karimi and Rajagopalan Srinivasan (Editors), Proceedings of the 11th International Symposium on Process Systems Engineering, 15-19 July 2012, Singapore c 2012 Elsevier B.V. All rights reserved.
Model predictive control for BioPower combined heat and power (CHP) plant Jukka Kortela ∗ Sirkka-Liisa Jämsä-Jounela Aalto University School of Chemical Technology; PL 16100, FI-00076 Aalto, Finland
Abstract This paper presents a model predictive control (MPC) development for BioGrate boiler. Amount of fuel and moisture in the furnace are chosen as the state variables for the MPC model in order to take into account fuel quality. To this end, dynamic models for fuel decomposition and water evaporation in the furnace are used. As a result, the drum pressure can be predicted accurately and an efficient stabilization of the plant operations is possible by using the MPC. The performance of the MPC is evaluated using real industrial plant data and compared with the currently used control strategy. Finally, the results are presented, analyzed and discussed. Keywords: biopower, combustion, biomass, fuel quality, MPC, moisture, advanced control, power plant
1. Introduction The usage of biomass fuel for heat and power production is growing due to an increasing demand for replacing of fossil energy sources with renewable energy. The fuel is usually a blend of different batches, for example, spruce bark and dry woodchips with varying moisture content between 30% and 55% (Yin et al. (2008)). This varying moisture content of the fuel results in uncertainty in the energy content of the fuel and complicates the operation of combustors. One of the latest developed processes, which can burn biomass fuel with high moisture is BioGrate technology developed by MW Biopower. In the BioGrate system, this is achieved by feeding the fuel onto the center of a grate, thus improving water evaporation due to the heat of the surrounding burning fuel and thermal radiation from the brick walls (Wärtsilä Biopower (2005)). An important step in the control strategy development for BioGrate boiler has been to develop a method for estimating the furnace fuel flow and combustion power, as shown in theoretical studies and practical tests by Kortela and Lautala (1981). On-line measurements of oxygen consumption were used when a new cascade compensation loop was built to optimally control the fuel flow. It was reported that the amplitude and the settling time of the response of the generator power decreased to about one third of the original. In addition, advanced combustion control has been applied to control air and fuel. Havlena and Findejs (2005) used model-based predictive control to enable tight dynamical coordination between air and fuel to take into account the variations in power levels. The results showed that this approach enabled boiler to be permanently operated with optimum excess air, resulting in reduced O2 and a significant increase in the boiler efficiency. Similar results have also been reported for the application of a multivariable long-range predictive control (LRPC) strategy based on a local model network (LMN) in the simulation of a 200 ∗ jukka.kortela@aalto.fi
J. Kortela et al.
436
MW oil-fired drum-boiler thermal plant (Prasad et al. (1998)) and for a scheme presented by Swarnakar et al. (1998) for robust stabilization of a boiler, based on linear matrix inequalities (LMIs). However, there are still some challenges and unattained objectives in the development combustion power control. For example, variations in the moisture of fuel should be considered in order to correct any estimation errors of combustion power. This paper presents a model predictive control (MPC) development of BioGrate boiler. The paper is organized as follows: Section 2 presents the process description, the MPC control strategy, and the models for the MPC controller for BioPower CHP plant. The test results of MPC control strategy are presented in Section 4, followed by the conclusions in Section 5.
2. Description of the process and model predictive control strategy for Biopower 5 CHP plant In the BioPower 5 CHP plant, the heat used for steam generation is obtained by burning solid biomass fuel: bark, sawdust and pellets, which are fed to the steam boiler together with combustion air. Heat and flue gases generated in the result of combustion are used to produce steam which accumulates in the drum. The aim of the control is to keep the energy production at the target level that allows to stabilize the steam pressure in the drum. The suggested MPC control strategy presented in Fig. 1 utilizes fuel flow and fuel moisture soft-sensors and furnace state estimators to handle the inherent large time constants and long time delays of the boiler. In addition, models for fuel decomposition and water evaporation are used to predict the combustion power and to stabilize the drum pressure. This results in a radical reduction of settling time of the drum pressure and enables faster steam load changes, while maintaining the stability of the boiler. Soft-sensors
State estimators
O2
Prediction and regulator
Furnace model
Combustion power computation
Air
Amount of burned fuel Dry fuel flow
Air
Drum pressure model
Fuel
Fuel moisture soft-sensor
Boiler
Fuel in the furnace Eq. 11
Flue gas moisture
Drum pressure
Fuel moisture (Kortela and Jämsä-Jounela 2010, 2012)
Water in the furnace Eq. 10
Combustion power
Figure 1. Model predictive control (MPC) strategy of BioGrate boiler
Furnace state
Drum pressure set point
Steam demand
Model predictive control for BioPower combined heat and power (CHP) plant
437
2.1. MPC Controller for BioGrate boiler The MPC control is used as a master control, manipulating simultaneously set points for air and fuel flows. The configuration of the models is as follows: the fuel flow MV1 and the primary air flow MV2 are the manipulated variables; the fuel moisture DV1 is the estimated disturbance and the steam demand DV2 is the measured disturbance; and the combustion power CV1 and the drum pressure CV2 are controlled variables. The models are stacked into a linear state space system: xk+1 zk
= Axk + Buk + Edk = Cz xk
(1)
where x are the states, u are the manipulated variables (MVs), and d are the measured disturbances. z denote the controlled variables (CVs). Regularized l2 output tracking problem with input and output constraints is formulated as (Maciejowski (2002)) 1 1 N zk − rk 2Qz + Δuk 2S ∑ 2 k=1 2
min φ
=
s.t.xk+1 zk umin Δumin zmin
= Axk + Buk + Edk , k = 0, 1, . . . , N − 1 = Cz xk , k = 0, 1, . . . , N ≤ uk ≤ umax , k = 0, 1, . . . , N − 1 ≤ Δuk ≤ Δumax , k = 0, 1, . . . , N − 1 ≤ zk ≤ zmax , k = 1, 2, . . . , N
(2)
2.2. Modelling of BioGrate boiler for MPC Modelling of grate combustion is of great importance, since unknown fuel flow and water evaporation result in uncertainty in the combustion power, and complicate the operation of the boiler. The model is based on two mass balances for water and dry fuel, in which both models are obtained from the literature and their parameters are defined with experimental data. Other models of the boiler include the drum, the primary air, and the secondary air models. 2.2.1. The model for amount of moisture in the furnace According to (Bauer et al. (2010)), the rate of water evaporation is mainly independent of the primary air flow and the dynamics of the moisture in the furnace mw is dmw = −αwev ∗ mw + mw,in (t − Td ) (3) dt where αwev is a dimensionless scaling factor, mw,in is the moisture in the fuel feed, and Td a constant delay. 2.2.2. The model for amount of dry fuel in the furnace Bauer et al. (2010) showed that the overall effect of the primary air flow rate on the thermal decomposition rate is multiplicative. Therefore, the thermal decomposition of dry fuel is as follows dmds = −αthd ∗ mds + mds,in (t − Td ) − α pa ∗ m pa (4) dt where αthd is the decomposition rate coefficient of fuel flow, and α pa the decomposition rate coefficient of primary air flow, mds,in is the stoker speed, and Td a constant delay.
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2.2.3. Drum model If the drum level is kept at a constant set point, the variations in the steam volume are small. Neglecting these variations, the drum model is (Åström and Bell (2000))
(! !%#&%*(&#)*(* - !()** )*
(5)
&!# ( '&, (
dp 1 = (Q − m f (hw − h f ) dt e −ms (hs − hw ))
(! !%#&%*(&#)*(* -) &% * )*
e ≈ ρwVwt
∂ hw ∂ Ts + mt C p ∂p ∂p
(6)
&!# ( '&, (
&%*(&#)*(* -,!* ,!* &+* + #$&!)*+( )& *) %)&(
2.2.4. Models for the primary air and secondary air flows The primary air and secondary air models represent the dynamics of the regulatory layer PID control loops of air transport. G pa (s) =
1 e−αs τ pa s + 1
(7)
Gsa (s) =
1 τsa s + 1
(8)
!$ ) &%
(!$(-!( #&," )
(-+ # #&," )
&!)*+( #&," )
where Q is combustion power (MJ/s), m is mass flow rate (kg/s), h is specific enthalpy (MJ/kg, ρ is specific density (kg/m3 ), V is volume (m3 ), mt is the total mass of the metal tubes and the drum (kg), C p is specific heat of the metal (MJ/kgK), and Ts temperature of steam (K). The subscripts f , w, s, refer to feed-water, water, and stem, respectively. Double subscript t denotes total system. The combustion power and fuel moisture soft-sensor are described detailed in (Kortela and Jämsä-Jounela (2010)) and (Kortela and JämsäJounela (2012)).
&!# ( '&, (
Figure 2. The test results when the MPC control estimates both states: fuel and moisture in the furnace (solid line), and the test results when the MPC control assumes that the moisture content is constant (dashed line). The top two pictures show the boiler power of the original control for the first and the second test.
Model predictive control for BioPower combined heat and power (CHP) plant
439
3. Test results of MPC control strategy of BioGrate boiler The performance of the MPC control strategy was evaluated using real industrial plant data. In the first test scenario, the power demand of the boiler was kept at 14 MW while the moisture content of the fuel flow was changed from 47% to 51% at a time 300 seconds, as shown in Fig. 2. When the MPC control estimates both states: fuel and fuel moisture in the furnace, the boiler power drops by 1.5 MW for a short time. Nevertheless, the power demand and the actual power match really well. To compare, there is an offset in the results when the MPC control assumes that the moisture content in the fuel is constant. With the original plant control, the change in the moisture content caused strong oscillations. In the second scenario, the power demand was changed from 14 MW to 15 MW at a time 700 seconds. The settling time is only 2 minutes with the MPC control strategy, whereas it was about 1.5 hours with the original control.
4. Conclusions This paper presented model predictive control (MPC) development for BioGrate boiler. The performance of the MPC was evaluated using real industrial plant data and comparison was made with the currently used control strategy. The test results show that the MPC followed power demand better compared with the currently used control strategy while maintaining stability.
References Bauer, R., Gölles, M., Brunner, T., Dourdoumas, N., Obernberger, I., 2010. Modelling of grate combustion in a medium scale biomass furnace for control purposes. Biomass and Bioenergy 34 (4), 417–427. Havlena, V., Findejs, J., 2005. Application of model predictive control to advanced combustion control. Control Engineering Practice 13 (6), 671–680. Kortela, J., Jämsä-Jounela, S.-L., 2010. Fuel quality soft-sensor for control strategy improvement of the Biopower 5 CHP plant. In: Control and Fault-Tolerant Systems (SysTol), 2010. Nice, France, 6-8 October 2010, pp. 221–226. Kortela, J., Jämsä-Jounela, S.-L., 2012. Fuel moisture soft-sensor and its validation for the industrial Biograte boiler. In: 17th Nordic Process Control Workshop. Kgs Lyngby, Denmark, 25-27 January 2012. Kortela, U., Lautala, P., 1981. A New Control Concept for a Coal Power Plant. In: Proceedings of the 8th IFAC World Congress. Kyoto, Japan, 1981. Maciejowski, J. M., 2002. Predictive Control with Constraints. Prentice Hal, Harlow, England. Prasad, G., Swidenbank, E., Hogg, B. W., 1998. A Local Model Networks Based Multivariable Long-Range Predictive Control Strategy for Thermal Power Plant. Automatica 34 (10), 1185–1204. Swarnakar, A.,Marquez, H.J., Chen, T. 2007 Robust stabilization of nonlinear interconnected systems with application to an industrial utility boiler. Control Engineering Practice 15(6), 639-654 Yin, C., Rosendahl, L. A., Kær, S. K., 2008. Grate-firing of biomass for heat and power production. Progress in Energy and Combustion Science 34 (6), 725–754. Wärtsilä Power Plants: Bioenergy solutions. Vaasa: Waasa Graphics; 2005. Åström, K. J. A., Bell, R. D., 2000. Drum-boiler dynamics. Automatica 36 (3), 363–378.
Model predictive control for BioPower combined heat and power (CHP) plant Jukka Kortela ∗ Sirkka-Liisa Jämsä-Jounela Aalto University School of Chemical Technology; PL 16100, FI-00076 Aalto, Finland
Abstract This paper presents a model predictive control (MPC) development for BioGrate boiler. Amount of fuel and moisture in the furnace are chosen as the state variables for the MPC model in order to take into account fuel quality. To this end, dynamic models for fuel decomposition and water evaporation in the furnace are used. As a result, the drum pressure can be predicted accurately and an efficient stabilization of the plant operations is possible by using the MPC. The performance of the MPC is evaluated using real industrial plant data and compared with the currently used control strategy. Finally, the results are presented, analyzed and discussed. Keywords: biopower, combustion, biomass, fuel quality, MPC, moisture, advanced control, power plant
1. Introduction The usage of biomass fuel for heat and power production is growing due to an increasing demand for replacing of fossil energy sources with renewable energy. The fuel is usually a blend of different batches, for example, spruce bark and dry woodchips with varying moisture content between 30% and 55% (Yin et al. (2008)). This varying moisture content of the fuel results in uncertainty in the energy content of the fuel and complicates the operation of combustors. One of the latest developed processes, which can burn biomass fuel with high moisture is BioGrate technology developed by MW Biopower. In the BioGrate system, this is achieved by feeding the fuel onto the center of a grate, thus improving water evaporation due to the heat of the surrounding burning fuel and thermal radiation from the brick walls (Wärtsilä Biopower (2005)). An important step in the control strategy development for BioGrate boiler has been to develop a method for estimating the furnace fuel flow and combustion power, as shown in theoretical studies and practical tests by Kortela and Lautala (1981). On-line measurements of oxygen consumption were used when a new cascade compensation loop was built to optimally control the fuel flow. It was reported that the amplitude and the settling time of the response of the generator power decreased to about one third of the original. In addition, advanced combustion control has been applied to control air and fuel. Havlena and Findejs (2005) used model-based predictive control to enable tight dynamical coordination between air and fuel to take into account the variations in power levels. The results showed that this approach enabled boiler to be permanently operated with optimum excess air, resulting in reduced O2 and a significant increase in the boiler efficiency. Similar results have also been reported for the application of a multivariable long-range predictive control (LRPC) strategy based on a local model network (LMN) in the simulation of a 200 ∗ jukka.kortela@aalto.fi
J. Kortela et al.
436
MW oil-fired drum-boiler thermal plant (Prasad et al. (1998)) and for a scheme presented by Swarnakar et al. (1998) for robust stabilization of a boiler, based on linear matrix inequalities (LMIs). However, there are still some challenges and unattained objectives in the development combustion power control. For example, variations in the moisture of fuel should be considered in order to correct any estimation errors of combustion power. This paper presents a model predictive control (MPC) development of BioGrate boiler. The paper is organized as follows: Section 2 presents the process description, the MPC control strategy, and the models for the MPC controller for BioPower CHP plant. The test results of MPC control strategy are presented in Section 4, followed by the conclusions in Section 5.
2. Description of the process and model predictive control strategy for Biopower 5 CHP plant In the BioPower 5 CHP plant, the heat used for steam generation is obtained by burning solid biomass fuel: bark, sawdust and pellets, which are fed to the steam boiler together with combustion air. Heat and flue gases generated in the result of combustion are used to produce steam which accumulates in the drum. The aim of the control is to keep the energy production at the target level that allows to stabilize the steam pressure in the drum. The suggested MPC control strategy presented in Fig. 1 utilizes fuel flow and fuel moisture soft-sensors and furnace state estimators to handle the inherent large time constants and long time delays of the boiler. In addition, models for fuel decomposition and water evaporation are used to predict the combustion power and to stabilize the drum pressure. This results in a radical reduction of settling time of the drum pressure and enables faster steam load changes, while maintaining the stability of the boiler. Soft-sensors
State estimators
O2
Prediction and regulator
Furnace model
Combustion power computation
Air
Amount of burned fuel Dry fuel flow
Air
Drum pressure model
Fuel
Fuel moisture soft-sensor
Boiler
Fuel in the furnace Eq. 11
Flue gas moisture
Drum pressure
Fuel moisture (Kortela and Jämsä-Jounela 2010, 2012)
Water in the furnace Eq. 10
Combustion power
Figure 1. Model predictive control (MPC) strategy of BioGrate boiler
Furnace state
Drum pressure set point
Steam demand
Model predictive control for BioPower combined heat and power (CHP) plant
437
2.1. MPC Controller for BioGrate boiler The MPC control is used as a master control, manipulating simultaneously set points for air and fuel flows. The configuration of the models is as follows: the fuel flow MV1 and the primary air flow MV2 are the manipulated variables; the fuel moisture DV1 is the estimated disturbance and the steam demand DV2 is the measured disturbance; and the combustion power CV1 and the drum pressure CV2 are controlled variables. The models are stacked into a linear state space system: xk+1 zk
= Axk + Buk + Edk = Cz xk
(1)
where x are the states, u are the manipulated variables (MVs), and d are the measured disturbances. z denote the controlled variables (CVs). Regularized l2 output tracking problem with input and output constraints is formulated as (Maciejowski (2002)) 1 1 N zk − rk 2Qz + Δuk 2S ∑ 2 k=1 2
min φ
=
s.t.xk+1 zk umin Δumin zmin
= Axk + Buk + Edk , k = 0, 1, . . . , N − 1 = Cz xk , k = 0, 1, . . . , N ≤ uk ≤ umax , k = 0, 1, . . . , N − 1 ≤ Δuk ≤ Δumax , k = 0, 1, . . . , N − 1 ≤ zk ≤ zmax , k = 1, 2, . . . , N
(2)
2.2. Modelling of BioGrate boiler for MPC Modelling of grate combustion is of great importance, since unknown fuel flow and water evaporation result in uncertainty in the combustion power, and complicate the operation of the boiler. The model is based on two mass balances for water and dry fuel, in which both models are obtained from the literature and their parameters are defined with experimental data. Other models of the boiler include the drum, the primary air, and the secondary air models. 2.2.1. The model for amount of moisture in the furnace According to (Bauer et al. (2010)), the rate of water evaporation is mainly independent of the primary air flow and the dynamics of the moisture in the furnace mw is dmw = −αwev ∗ mw + mw,in (t − Td ) (3) dt where αwev is a dimensionless scaling factor, mw,in is the moisture in the fuel feed, and Td a constant delay. 2.2.2. The model for amount of dry fuel in the furnace Bauer et al. (2010) showed that the overall effect of the primary air flow rate on the thermal decomposition rate is multiplicative. Therefore, the thermal decomposition of dry fuel is as follows dmds = −αthd ∗ mds + mds,in (t − Td ) − α pa ∗ m pa (4) dt where αthd is the decomposition rate coefficient of fuel flow, and α pa the decomposition rate coefficient of primary air flow, mds,in is the stoker speed, and Td a constant delay.
J. Kortela et al.
438
2.2.3. Drum model If the drum level is kept at a constant set point, the variations in the steam volume are small. Neglecting these variations, the drum model is (Åström and Bell (2000))
(! !%#&%*(&#)*(* - !()** )*
(5)
&!# ( '&, (
dp 1 = (Q − m f (hw − h f ) dt e −ms (hs − hw ))
(! !%#&%*(&#)*(* -) &% * )*
e ≈ ρwVwt
∂ hw ∂ Ts + mt C p ∂p ∂p
(6)
&!# ( '&, (
&%*(&#)*(* -,!* ,!* &+* + #$&!)*+( )& *) %)&(
2.2.4. Models for the primary air and secondary air flows The primary air and secondary air models represent the dynamics of the regulatory layer PID control loops of air transport. G pa (s) =
1 e−αs τ pa s + 1
(7)
Gsa (s) =
1 τsa s + 1
(8)
!$ ) &%
(!$(-!( #&," )
(-+ # #&," )
&!)*+( #&," )
where Q is combustion power (MJ/s), m is mass flow rate (kg/s), h is specific enthalpy (MJ/kg, ρ is specific density (kg/m3 ), V is volume (m3 ), mt is the total mass of the metal tubes and the drum (kg), C p is specific heat of the metal (MJ/kgK), and Ts temperature of steam (K). The subscripts f , w, s, refer to feed-water, water, and stem, respectively. Double subscript t denotes total system. The combustion power and fuel moisture soft-sensor are described detailed in (Kortela and Jämsä-Jounela (2010)) and (Kortela and JämsäJounela (2012)).
&!# ( '&, (
Figure 2. The test results when the MPC control estimates both states: fuel and moisture in the furnace (solid line), and the test results when the MPC control assumes that the moisture content is constant (dashed line). The top two pictures show the boiler power of the original control for the first and the second test.
Model predictive control for BioPower combined heat and power (CHP) plant
439
3. Test results of MPC control strategy of BioGrate boiler The performance of the MPC control strategy was evaluated using real industrial plant data. In the first test scenario, the power demand of the boiler was kept at 14 MW while the moisture content of the fuel flow was changed from 47% to 51% at a time 300 seconds, as shown in Fig. 2. When the MPC control estimates both states: fuel and fuel moisture in the furnace, the boiler power drops by 1.5 MW for a short time. Nevertheless, the power demand and the actual power match really well. To compare, there is an offset in the results when the MPC control assumes that the moisture content in the fuel is constant. With the original plant control, the change in the moisture content caused strong oscillations. In the second scenario, the power demand was changed from 14 MW to 15 MW at a time 700 seconds. The settling time is only 2 minutes with the MPC control strategy, whereas it was about 1.5 hours with the original control.
4. Conclusions This paper presented model predictive control (MPC) development for BioGrate boiler. The performance of the MPC was evaluated using real industrial plant data and comparison was made with the currently used control strategy. The test results show that the MPC followed power demand better compared with the currently used control strategy while maintaining stability.
References Bauer, R., Gölles, M., Brunner, T., Dourdoumas, N., Obernberger, I., 2010. Modelling of grate combustion in a medium scale biomass furnace for control purposes. Biomass and Bioenergy 34 (4), 417–427. Havlena, V., Findejs, J., 2005. Application of model predictive control to advanced combustion control. Control Engineering Practice 13 (6), 671–680. Kortela, J., Jämsä-Jounela, S.-L., 2010. Fuel quality soft-sensor for control strategy improvement of the Biopower 5 CHP plant. In: Control and Fault-Tolerant Systems (SysTol), 2010. Nice, France, 6-8 October 2010, pp. 221–226. Kortela, J., Jämsä-Jounela, S.-L., 2012. Fuel moisture soft-sensor and its validation for the industrial Biograte boiler. In: 17th Nordic Process Control Workshop. Kgs Lyngby, Denmark, 25-27 January 2012. Kortela, U., Lautala, P., 1981. A New Control Concept for a Coal Power Plant. In: Proceedings of the 8th IFAC World Congress. Kyoto, Japan, 1981. Maciejowski, J. M., 2002. Predictive Control with Constraints. Prentice Hal, Harlow, England. Prasad, G., Swidenbank, E., Hogg, B. W., 1998. A Local Model Networks Based Multivariable Long-Range Predictive Control Strategy for Thermal Power Plant. Automatica 34 (10), 1185–1204. Swarnakar, A.,Marquez, H.J., Chen, T. 2007 Robust stabilization of nonlinear interconnected systems with application to an industrial utility boiler. Control Engineering Practice 15(6), 639-654 Yin, C., Rosendahl, L. A., Kær, S. K., 2008. Grate-firing of biomass for heat and power production. Progress in Energy and Combustion Science 34 (6), 725–754. Wärtsilä Power Plants: Bioenergy solutions. Vaasa: Waasa Graphics; 2005. Åström, K. J. A., Bell, R. D., 2000. Drum-boiler dynamics. Automatica 36 (3), 363–378.